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title: Cryo-EM structure of human eIF5A-DHS complex reveals the molecular basis of
hypusination-associated neurodegenerative disorders
authors:
- Elżbieta Wątor
- Piotr Wilk
- Artur Biela
- Michał Rawski
- Krzysztof M. Zak
- Wieland Steinchen
- Gert Bange
- Sebastian Glatt
- Przemysław Grudnik
journal: Nature Communications
year: 2023
pmcid: PMC10042821
doi: 10.1038/s41467-023-37305-2
license: CC BY 4.0
---
# Cryo-EM structure of human eIF5A-DHS complex reveals the molecular basis of hypusination-associated neurodegenerative disorders
## Abstract
Hypusination is a unique post-translational modification of the eukaryotic translation factor 5A (eIF5A) that is essential for overcoming ribosome stalling at polyproline sequence stretches. The initial step of hypusination, the formation of deoxyhypusine, is catalyzed by deoxyhypusine synthase (DHS), however, the molecular details of the DHS-mediated reaction remained elusive. Recently, patient-derived variants of DHS and eIF5A have been linked to rare neurodevelopmental disorders. Here, we present the cryo-EM structure of the human eIF5A-DHS complex at 2.8 Å resolution and a crystal structure of DHS trapped in the key reaction transition state. Furthermore, we show that disease-associated DHS variants influence the complex formation and hypusination efficiency. Hence, our work dissects the molecular details of the deoxyhypusine synthesis reaction and reveals how clinically-relevant mutations affect this crucial cellular process.
eIF5A is the only protein known to contain hypusine. Here, the authors present the cryoEM structure of the eIF5A-DHS complex and provide mechanistic insights to understand the deoxyhypusination reaction and hypusination-related neurodegeneration.
## Introduction
The eukaryotic translation factor 5A (eIF5A) plays a pivotal role during translation1,2. It is the only cellular protein known to undergo hypusination, a unique post-translational modification of a conserved lysine residue (Lys50 in human eIF5A). Hypusination is essential to resolve ribosomal stalling during the translation of proline-rich polypeptides3,4. Recent findings show that the hypusination of eIF5A plays a role in key cellular processes, including autophagy5, senescence, polyamine homeostasis, and the determination of helper T cell lineages6. Significantly, misregulation of hypusination has been linked to pathological conditions, including cancer and diabetes. The hypusination pathway, therefore, represents an attractive molecular target for therapeutic interventions7–14.
Hypusination involves two distinct enzymatic steps14. During the first, rate-limiting, step, deoxyhypusine synthase (DHS) catalyzes the NAD-dependent transfer of the 4-aminobutyl moiety from spermidine (SPD) to the lysine side chain, forming deoxyhypusine15,16. Subsequently, deoxyhypusine is hydroxylated to hypusine (N6‐(4‐amino‐2‐hydroxybutyl)lysine) by deoxyhypusine hydroxylase (DOHH)17. Structural studies have provided some insights into the hypusination reaction. X-ray crystal structures of the human DHS apoenzyme and structures of the inhibitor (GC7)- and spermidine-bound DHS13,16,18,19 show the architecture of the protein’s composite active site and resolve SPD binding and the molecular basis of inhibition. Furthermore, the X-ray and cryo-EM structures of eIF5A bound to the translating ribosome have revealed how hypusine contributes to the rescue of stalled ribosomes. By stabilizing the productive position of P-tRNA, hypusinated eIF5A facilitates the transfer of the nascent chain from P-tRNA to A-tRNA20,21. However, no structural information on substrate-bound complexes during the hypusination reaction, i.e., eIF5A-DHS and eIF5A-DOHH, is yet available.
Ganapathi et al. recently identified disease-causing mutations in the human DHS encoding gene (dhps) and described the cellular consequences that result from these mutations22. The authors characterized a genetic syndrome, namely DHS deficiency, and report five affected individuals from four independent families, who carry biallelic variants in dhps. The clinical features common to most patients are developmental delay and intellectual disability, abnormalities in muscle tone, clinical seizures, decreased coordination, walking and locomotive and behavioral difficulties.
Herein, we uncover the mechanism of the first rate-limiting step of the hypusination reaction catalyzed by DHS using a hybrid structural biology approach. We sought to elucidate the impact of pathological mutations on the structure and function of proteins involved in the deoxyhypusination complex. We present the single-particle cryo-EM structure of the human eIF5A-DHS complex, which provides mechanistic insights into the deoxyhypusination reaction. Furthermore, we used ambient-temperature X-ray crystallography to solve the structure of DHS trapped in the reaction transition state, in which the postulated form of deoxyhypusinated DHS is directly visible. Moreover, we complement our structural work with thorough biochemical analyses of clinically-relevant DHS variants to reveal the molecular impacts of these mutations. Our data show that clinical mutations associated with neurodegeneration impair the first step of hypusination by affecting the structure and activity of DHS, spermidine binding, the eIF5A-DHS interaction, and the overall efficiency of hypusination.
## Cryo-EM structure of the eIF5A-DHSK329Acomplex
To understand how the binding of eIF5A and DHS facilitates the deoxyhypusination reaction, we used single-particle cryo-EM to determine the structure of the human eIF5A-1-DHS complex at an overall resolution of 2.8 Å (Fig. 1a, Supplementary Fig. 1 and Supplementary Table 1). We reconstituted and trapped the complex using individually purified components, namely eIF5A-1 and the catalytically dead variant DHSK329A (we refer to the complex as eIF5A-DHSK329A). The alanine substitution of the catalytic lysine rendered an enzymatically inactive DHS yet still capable of binding the cofactor NAD and both substrates, namely SPD and eIF5A.Fig. 1Overview of the eIF5A-DHS complex.a Cryo-EM map and b corresponding molecular model of eIF5A-DHSK329A complex in cartoon representation shown from two perpendicular perspectives (upper vs. lower panels). c Close-up view of the eIF5A binding site. The cryo-EM map (black mesh at 6σ) is countered around the hypusination loop (dark pink) placed between monomers A (blue) and B (yellow) of the DHS. Modifiable lysine (K50) and rotatable W327 are shown as sticks. Two views rotated by 30 degrees are shown for clarity. NAD is shown in the redox centre. d Surfaces of eIF5A-DHS complex components are colored by electrostatic potential. The DHS in an open conformation exposes a negatively charged (red) patch that is complementary to the positively charged (blue) N-terminal domain of eIF5A, which facilitates the binding of eIF5A to the active site (AS). Upon dissociation of eIF5A, the negative patch at the entrance to the DHS active site can be masked by a negatively charged DHS N-terminal ball-and-chain (BnC) motif.
The complex structure is composed of a single eIF5A monomer bound to a DHS tetramer (Fig. 1b). We observed well-resolved density in the core of DHS, but in some peripheral regions, the map quality appears weaker (e.g., surface regions and the C-terminal OB-fold domain of eIF5A) (Supplementary Fig. 1). The quality of the reconstruction allowed us to unambiguously place most amino acids of both complex components. Similarly, to previously determined DHS crystal structures16,18,23, individual protomers differ in the length of the defined density for their N-termini and loop regions (aa 79–83), which were often poorly ordered. Remarkably, however, the loop of chain A is significantly better structured in the cryo-EM structure of eIF5A-DHSK329A, compared to the crystal structure of DHS (Supplementary Fig. 2a). This difference may be a direct consequence of the adjacent binding of eIF5A and suggests that eIF5A binding may stabilize this chain. The entire N-terminal domain of eIF5A is well-defined in the Coulomb potential map with a well-resolved modifiable lysine residue (Lys50) (Fig. 1c). The C-terminal domain of eIF5A (aa 87-150) is not stabilized by any interaction with DHS and thus remains rather poorly defined (Supplementary Fig. 2b). The overall structure suggests that eIF5A-DHS complex formation is favored by charge complementarity (Fig. 1d).
## eIF5A binding to DHS unmasks the active site
The cryo-EM structure reveals that the DHS tetramer does not undergo pronounced structural rearrangements upon the binding of eIF5A. The overall architecture of DHS in the complex with eIF5A is very similar to that observed in crystal structures of human DHS16,18,24. Likewise, the DHS dimer (chains C and D) opposite the eIF5A binding site superposes almost perfectly on the unliganded DHS (RMSD 0.86 Å). Of note, we did observe a 5° rotation of the DHS half that interacts with eIF5A (chains A and B) (Supplementary Fig. 2c). Furthermore, we measured a 7° difference in the relative orientation of the DHS outermost helix (chain B) that flanks the binding site of eIF5A.
The structure also confirms that only one eIF5A monomer interacts with the DHS tetramer as proposed in previous biochemical studies2. The binding interface is located between two DHS protomers, which almost equally contribute to eIF5A binding (interaction surface with chain A 489.6 Å2 and chain B 564.2 Å2) and can be explained by DHS active site complementation25. Overall the eIF5A interface constitutes a large surface area of ∼1054 Å2, which covers approximately $12\%$ of the total surface of eIF5A25. This interaction is mediated mainly by a 3-stranded β-sheet formed by the N-terminal domain of eIF5A (Fig. 2a, b). The complex is stabilized by an extended network of specific hydrogen bonds with a significant contribution of less specific hydrophobic contacts (Fig. 2c). Hence, our structural data are consistent with previous findings showing that almost the entire N-terminal domain of eIF5A is needed for effective deoxyhypusination3. Our data thus suggest that this requirement reflects the contribution of this domain to the interaction with DHS.Fig. 2Details of the interaction architecture. Visualization of the eIF5A-DHS binding site (a, b). Side chains involved in the interaction are shown as stick representations (a and b for eIF5A and DHS, respectively). c Simplified visualization of the eIF5A hypusination loop depicted as an extended peptide (dark pink) with marked interacting residues of DHS monomer A (blue) and B (yellow). d The eIF5A-DHS complex in cartoon representation is colored according to the result of the HDX-MS experiment. Inlets present eIF5A (top) or DHS (bottom) in surface representation. Bluish coloring represents reduced deuterium incorporation of the eIF5A-DHS complex compared to the individual proteins reflecting alterations in protein conformation and shielding from bulk solvent.
Furthermore, the structure explains how eIF5A binding unmasks the DHS active site. In the absence of eIF5A, the so-called ball from the ball-and-chain motif in the N-terminal helix of DHS (residues 11–17) covers the entrance to the active site of the enzyme, maintaining a negative surface potential6,24 (Fig. 1d). The highly conserved hypusination loop of eIF5A (46SKTGKHGHA26) enters the DHS active site through a distinct tunnel and is stabilized mainly by three helices at the DHS dimer interface (Fig. 2). The hypusination loop thereby unlocks the entrance to the active site by displacing the ball motif. Of note, the ball motif is still clearly present in its previously described position in the remaining three unoccupied active sites of DHS (Supplementary Fig. 2d).
Finally, we observe that NAD binds to all four DHS active sites, while the SPD moiety binds to three—all but the one occupied by eIF5A. Indeed, SPD and Lys50 of eIF5A occupy a similar position within the active site, potentially explaining their exclusive binding. Of note, the density for the SPD ligand is significantly less defined than the density for NAD (Supplementary Fig. 2e).
## HDX-MS reveals alterations in the DHS tetramer upon eIF5A binding
As an alternate approach to understanding the interactions between eIF5A and DHS in solution, we used hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS). This method detects conformational changes that occur upon complex formation27. We proceeded to compare the HDX profiles of individual eIF5A and DHS proteins with that of the eIF5A-DHS complex (Fig. 2d).
For eIF5A, reduced HDX was apparent for residues 32–77 encompassing most of its N-terminal domain and a small portion (residues 97–101) in the C-terminal domain (Supplementary Fig. 3). The strong reduction in HDX of the former is in good agreement with the cryo-EM structure of the complex, as it contains the hypusination loop protruding into the DHS tetramer (Fig. 2d). Increased HDX mainly clustered in the C-terminal domain (residues 109–114, 120–122, 124–126, 144–151) and a short helical turn in the N-terminal domain (residues 24-30, Supplementary Figs. 3b, c). For some peptides located in these areas, we observed a bimodal distribution of the ion isotope clusters during HDX (Supplementary Figs. 5a–c & 6) indicating the presence of different stable conformations or/and transition between them28. For two peptides from the N-terminal domain of eIF5A (residues 32-42 and 41-60), the fast-exchanging species was dominant after 10,000 s of HDX for individual eIF5A, but not for eIF5A in complex with DHSK329A suggesting a restriction of conformational flexibility by DHS (Supplementary Fig. 6). In contrast, the difference in the portions of fast and slow-exchanging species between eIF5A and eIF5A-DHSK329A peptides from the C-terminal part of eIF5A (residues 90–101, 108–131, 111–134) was less severe indicating that the conformational flexibility in this part depends less on the presence of DHS (Supplementary Fig. 6). These results may also explain the weakly defined cryo-EM density of eIF5A, in particular in its C-terminal portion.
DHS exhibited perturbations in HDX in multiple parts of the protein. In particular, the eIF5A binding pocket of DHS (residues 104–118, 129–183, 266–278, and 282–295) and the active site loop (residues 306–344) showed HDX reductions (Fig. 3a, Supplementary Fig. 4). Additionally, increased HDX in residues 37–42 and 355–361, and a decrease in 49–62, regions that are remote from the eIF5A-binding and active sites, suggest alterations in the overall topology of the DHS homotetramer. Fig. 3Site-directed mutagenesis studies. The significance of selected interface or active site residues of DHS was demonstrated by their alanine substitution. The influence on various aspects is shown in consecutive panels: Thermal stability (a), oxidoreductase activity (b), hypusination capacity (c), affinity to eIF5A of the active site (d) and interface substitutions (e). For the thermal stability (a) and oxidoreductase activity (b) assays, data were normalized relative to wild-type. Bars represent the mean ± SD of at $$n = 4$$ (a) and n ≥ 7 (b) independent experiments. Dots represent individual measurements. In hypusination assay (c) for deoxyhypusination detection, monoclonal rabbit FabHpu98 antibody was used52. See the “Methods” section for details of assays. Representative blots of three independent experiments are shown. Panels f–h map the strength of the effect in color-coded visualization with the scale indicated below each panel. Source data for all panels are provided in Source Data file.
In almost all areas of DHS where the protein incorporated less deuterium in the context of the eIF5A-DHS complex, bimodal HDX behaviour was apparent that was characterized by a higher portion of the slow-exchanging population in eIF5A-DHS than in DHS alone, e.g. in peptides spanning residues 139-151, 152-169, 263-272, 279-293 and 312-327 (Supplementary Figs. 5d–f, Supplementary Fig. 7). One weakness of HDX-MS is that this method cannot discriminate between eIF5A-bound and empty DHS molecules. In this regard, the observed bimodality may simply reflect these different DHS species, which are a consequence of the asymmetry of the complex. However, bimodal HDX behaviour was also apparent for DHS in absence of eIF5A, and a peptide containing residues 47–59, which is remote from the eIF5A binding site. This suggests that DHS intrinsically exhibits a certain degree of conformational flexibility in the absence of eIF5A, and alterations in its complex topology upon eIF5A binding.
Collectively, the HDX-MS data corroborate the features of the DHS-eIF5A interaction inferred from the cryo-EM data in solution and furthermore identify subtle alterations in the tetrameric topology of DHS that occur upon eIF5A binding.
## Structure-guided mutagenesis separates DHS catalytic activity and eIF5A binding
To test the functional significance of the interactions defined by our structural work, we selected and mutated several residues from the eIF5A-DHS interface (Gln83, Asn173, Tyr176, Ser240, Phe247, Tyr250, Ile271, Phe272, Leu295) and the active site of DHS (Glu137, Asp243, His288, Trp327, Lys329). After the production and purification of the variants (see “Methods”), we analyzed their contribution to stability, activity, and complex formation (Fig. 3).
First, we assessed the thermostability of the individual proteins using differential scanning fluorimetry (DSF). Most of the mutations analyzed do not or only slightly affect the stability of DHS, but the mutation of hydrophobic interface residue (I271A) showed significantly decreased melting temperature (Fig. 3a).
We next examined catalytic activity. As mentioned above, DHS catalyzes the transfer of the 4-aminobutyl moiety from SPD to the modifiable lysine (Lys50) of eIF5A in a NAD-dependent manner. The reduction of NAD, when a transient DHS-NADH-SPD complex is formed, can be assessed by recording the intrinsic fluorescence of NADH29. We used this assay to analyze the oxidoreductase activity of the DHS variants. Based on the structure, we expected that mutations of the complex interface would rather not affect the DHS oxidoreductase activity. Not surprisingly, mutations in active site residues (D243A, H288A, W327A, and K329A) resulted in almost complete suppression of SPD dehydrogenation activity. A decrease in activity was also observed for some mutations of the complex interface (Q83A, I271A, and L295A). The opposite results were obtained for alanine mutants of residues mapping to the interface between eIF5A and DHS (N173A, S240A, M244A, F247A, Y250A and F272A) and active site mutant E137A. These mutations increased oxidoreductase activity (Fig. 3b). This result may reflect higher solvent accessibility, which may influence the NADH hydration state.
To further validate the importance of the above-mentioned residues in the catalytic reaction, we performed a qualitative hypusination assay to directly measure modification activity. As expected, mutations introduced in the active site of DHS (E137A, D243A, H288A, W327A, K329A), as well as three interface mutants directly involved in the stabilization of the eIF5A hypusination loop (N173A, Y176A, and L295A), completely inhibited hypusination activity. The remaining mutations did not affect the hypusination efficiency (Fig. 3c).
To elucidate the role of selected residues in complex formation, we measured the binding affinity between eIF5A and DHS using microscale thermophoresis (MST). For DHSwt the binding affinity was in the high nanomolar range: 33.8 ± 8.5 nM. The mutation in active site residue H288A did not affect the eIF5A binding (KD 49.7 ± 28.7 nM). On the other hand, the K329A substitution showed a decrease in affinity (KD 128.6 ± 37.9 nM; Fig. 3d, Supplementary Fig. 8). Moreover, mutation of the Trp327 residue caused a significant decrease in affinity to 602.5 ± 284.2 nM, highlighting the importance of this residue for complex formation. A similar effect was observed for D243A (corresponding to the SPD binding site) with an increased KD value of 511.3 ± 182.1 nM, and for E137A (NAD binding site) with a KD > 1 µM. All residues at the eIF5A-DHS interface contribute greatly to complex formation, and their alanine mutants exhibit an almost complete diminishment of the recruitment of eIF5A (Fig. 3e, Supplementary Fig. 8). These results underline the idea that hydrophobic interactions play a substantial role and that substitutions within the catalytic center of DHS (e.g. K329A) do not abolish the interaction with eIF5A. Interestingly, results from the activity and binding assays show that residues crucial for proper eIF5A binding are not essential for oxidoreductase activity (Fig. 3f–h) and vice versa.
## Visualization of a trapped reaction intermediate during DHS catalysis
The results from our structure-guided analyses suggested that the binding of eIF5A unblocks the active site. To examine whether eIF5A binding induces additional changes at the active site, we compared our eIF5A-DHS structure with the previously solved structure of the wild-type DHS protein (DHSwt)16. Interestingly, we observed that the indole ring of Trp327 is shifted perpendicularly in the complex, relative to its position in DHSwt (Fig. 4a, b). This rearrangement of the side chain caught our attention because the Trp327 residue is involved in the hydrophobic stabilization of the SPD methylene groups18,30. The cryo-EM structure clearly indicates that the wild-type position of Trp327 overlaps with that of the modifiable Lys50 residue of eIF5A in the complex. Simultaneously, within DHS, the rearranged position of Trp327 would collide with Lys329, which was mutated to alanine in the trapped complex used in our structural studies. It was therefore plausible that this conformational change was a direct or indirect result of the K329A mutation, rather than eIF5A binding per se. To address this issue, we determined the crystal structure of DHSK329A in complex with NAD and SPD and refined it at 1.6 Å (Supplementary Table 3). DHSK329A crystallized in the P3221 space group with one tightly associated DHS dimer per asymmetric unit. The DHS tetramer, which constitutes the active form of the enzyme, is completed by the neighboring crystallographic symmetry mate. In the structure of DHSK329A-NAD-SPD, we observed two alternative conformations of the Trp327 side chain with almost equivalent occupancies. In detail, we found the perpendicular position that is also found in the eIF5A-DHS complex and the position that is parallel to the bound SPD molecule, as in DHSwt (without protein ligand) (Fig. 4c). The observed conformational change indicates that the K329A mutation gives rise to greater conformational freedom of the bulky indole chain of Trp327.Fig. 4DHS active sites comparison. Comparison of the architecture of the active site of a the eIF5A-DHSK329A complex (8A0E), b DHS wt (6XXJ), c DHSK329A in complex with NAD and SPD (8A0F) and d DHS with the intermediate state on lysine 329 (8A0G). Ligands and key residues are shown as sticks.
Given the above results, we wondered how the conformational change involving the Trp327 indole chain impacts the dexyhypusination reaction. In the early stage of the reaction catalyzed by DHS, SPD is cleaved to the 1,3-diaminopropane (DAP) and the 4-aminobutyl moiety. 4-aminobutyl subsequently forms an imine linkage to Nε of Lys329 in DHS, resulting in a transient enzyme-imine product intermediate31. This transient double bond intermediate can be reduced to a stable single-bond in the presence of NaBH3CN32, leading to a covalent modification and the accumulation of the transition state analogue, namely deoxyhypusinated Lys329, and DAP (Supplementary Fig. 9a). To test this biochemical model, we sought to trap and visualize the reaction transition state. To do so, we collected a diffraction dataset of a DHS-NAD-SPD crystal pretreated with NaBH3CN at room temperature (RT). The intermediate state structure of DHS was solved with the same packing scheme and space group as the other DHS crystal structures and was refined at 1.8 Å resolution (Supplementary Table 3). As expected, the overall structure of the DHS reaction transition state is like those of previously reported DHS structures. However, our soaking approach at RT allowed us to observe a continuous electron density extending the Lys329 side chain, suggesting that this residue has been modified to deoxyhypusine upon NaBH3CN treatment (Fig. 4d, Supplementary Fig. 9b, c). The 4-aminobutyl moiety is covalently bound to the ε-amine group of Lys329 and stabilized by the Trp329 indole and NAD pyridine rings. Moreover, the electron density observed close to the exit of the active site tunnel likely represents DAP. These analyses therefore demonstrate the existence of the proposed intermediate and suggest that the rearrangement of the Trp327 would lead to a collision with the deoxyhypusinated Lys329 residue. Thus, the spatial restriction of the Trp327 indole ring in the wild-type DHS protein may facilitate the transfer of 4-aminobutyl moiety from Lys329 to Lys50 of eIF5A.
## Pathological mutations impact DHS stability and function
Recently, a set of clinically relevant mutations in the dhps gene were identified in five patients with neurodegenerative disease22 (Supplementary Fig. 10a, b). All patients carry an N173S mutation along with a secondary mutation. As the above data provided insight into DHS domains that may be critical for function, we sought to understand how disease-associated changes may impact DHS function. We chose to focus on DHSN173S and one of the secondary mutations, DHSdelY305_I306 as only those variants can be potentially expressed as active proteins in the cell22. Asn173 is part of the helix flanking the entrance tunnel to the active site. It interacts with the side chain of the neighboring Asn164 and with the Val17 and Lys19 main chains of the ball-and-chain motif (Supplementary Fig. 10b). The residues absent in DHSdelY305_I306 are located within the protein core, in one of the β-strands of the Rossman fold. These residues assist in the stabilization of the NAD binding pocket through an extensive interaction network of the main chain.
We expressed and purified both variants (see “Methods”). Initial trials showed a significant decrease in the solubility of DHSdelY305_I306, indicating possible misfolding or aggregation of the protein. However, a small portion of DHSdel305Y_306I remained soluble and we were able to obtain enough purified protein for further experiments. Of note, solubilization and refolding procedures were employed previously to assess the behavior of both DHS variants in vitro22, but we purified all DHS variants directly from the soluble fraction. As above, we first assessed protein stability using DSF. Surprisingly, the melting temperature (Tm) of the DHSN173S variant (64.1 ± 0.6 °C) is comparable with that of the wild-type protein (DHSwt) (62.9 ± 0.1 °C), whereas the Tm-values observed for DHSdel305Y_306I were significantly lower (46.7 ± 0.1 °C; Fig. 5a, Supplementary Fig. 10c). Next, we analyzed the oligomerization potential of the mutant proteins by mass photometry, which revealed differences in the oligomeric states of DHS variants. Whereas the profile of DHSN173S resembles the wild-type tetrameric form, the DHSdel305Y_306I variant is primarily dimeric in solution (Fig. 5b).Fig. 5Structural and biochemical analysis of loss-of-function DHS variants.a Thermal stability of DHS variants assessed by DSF. The color-coding indicated above panel a is applied to subsequent panels as well. b Mass photometry profiles obtained for DHS wt (yellow), DHSN173S (green), and DHSdel305Y,306I (blue) with the determined oligomeric state indicated as a schematic surface representation above the respective peaks. c Superposition of structures of wild type (PDB ID: 6XXJ) (yellow) and DHSN173S (PDB ID: 7A6T) (green). The mutation sites on the front of the molecules are indicated by a red star. The similarity for monomers or entire tetramers is expressed as the root-mean-square deviation (RMSD) for the corresponding Cα atoms and is indicated below the image. d, e Comparison of the bonding pattern of residue 173 in DHS wt (yellow) and DHSN173S (green). The main chain is shown as a cartoon representation. The mutant residues and the interacting residues are shown as sticks. Electrostatic interactions are shown as black dashed lines. f Single-turnover fluorescence activity assay performed for DHS wt and pathological variants. On the graph mean values ± SD of n ≥ 3 independent experiments are presented. g FRET-based binding assay with normalized fluorescence plotted as a function of SPD concentration and h the derived dissociation constant values tabularized. On the graph mean values ± SD of $$n = 3$$ independent experiments are presented. i Hypusination assessed by western blot. For deoxyhypusination detection, monoclonal rabbit FabHpu98 antibody was used52. The variant used is indicated above each line. Representative blots of three independent experiments are shown. j–l The affinity of DHSwt, DHSN173S and DHSdel305Y,306I, respectively, for eIF5A was assessed using MST, either in free buffer, with cofactor, or with cofactor and substrate supplemented as indicated by the legend. Data represent the mean ± SD of $$n = 3$$ individual experiments. The numerical values derived from the experiments are tabularized below. Source data for all panels are provided in Source Data file.
## Structural analysis of the DHSN173S variant
Given the weaker effects of the N173S mutation, we pursued structural studies to better understand its impact on DHS function. We solved a high-resolution structure of DHSN173S in a complex with NAD and SPD. Despite intensive efforts, we were unable to crystallize the DHSdel305Y_306I variant. DHSN173S crystallized in the P3221 space group with one tightly associated dimer per asymmetric unit, related to an identical dimer by a twofold axis. The structure was refined at 1.7 Å resolution (Supplementary Tab. 3). Structural comparison between wild-type and mutant DHS showed only minor differences of individual monomers (RMSD 0.15 Å) and the whole tetramer (RMSD 2.2 Å; Fig. 5c). The only significant difference is found near the mutated site. As mentioned above, Asn173 is located on the surface of the protein and stabilizes the neighboring ball-and-chain motif via polar interactions (with residues Val17, Lys19). The mutated serine residue in DHSN173S maintains the interaction with Asn164, but its side chain, having only one polar hydroxyl group, cannot form hydrogen bonds with the N-terminal part of the proximal protomer (Fig. 5d, e). These lost interactions may underlie the lack of observable electron density for the ball-and-chain motif, indicating poor ordering and partial destabilization of this motif in the variant structure. Mapping the N173S mutation onto the structure of the eIF5A-DHS complex suggested that the DHSN173S variant would also disturb the positioning of eIF5A in the DHS active site. In the structure of the eIF5A-DHS complex, the Asn173 residue of DHS interacts with the main chain of the eIF5A Thr48, constituting part of the interface of the complex (Supplementary Fig. 10d–f).
Structural analysis of DHSN173S, therefore, confirms that the mutation does not have a significant impact on overall protein structure but does affect the ball-and-chain motif, and likely also impacts the interaction with eIF5A.
## Pathological DHS mutations impair its activity and ligand binding
Next, we employed single-turnover fluorescence assays33, monitoring SPD dehydrogenation, to assess the influence of the mutations on enzymatic activity. An excess of SPD in the reaction triggers a rapid increase of fluorescence signal for both the wild-type and the N173S variant proteins. Interestingly, the final value of the fluorescence signal is ~$25\%$ higher for the N173S variant, but the progress of the reaction is identical for both wt and N173S variants (Fig. 5f). Concomitantly, the DHSdel305Y_306I variant was inactive and no readout was observed. The higher fluorescence values for the N173S variant may reflect increased oxidoreductase activity but may also reflect differences in the hydration state of NADH, indirectly triggered by the destabilization of the ball-and-chain motif4. Examining the hypusination reaction, the N173S variant led to a lower level of detected deoxyhypusine (Fig. 5i), while the deletion mutant yielded no deoxyhypusine product at all.
We also examined the impact of pathological mutations on SPD binding using a FRET assay (Fig. 5g)29. The calculated apparent KD values for SPD were 3.3 ± 0.3 (wt) and 27.0 ± 3.1 µM (N173S), respectively (Fig. 5h), indicating reduced binding. In the case of DHSdel305Y_306I, we did not observe any binding. The lack of binding is most likely caused by a general structural destabilization of this mutant.
Finally, we also investigated the impact of pathological mutations of DHS on the binding to eIF5A using MST assays. The KD values for the wild-type proteins are 33.8 ± 8.5 nM. The DHS variants show decreased binding parameters (115.1 ± 22.3 nM for N173S and 80.0 ± 23.4 nM for del305Y_306I), respectively. Thus, our results indicate a loss in affinity for eIF5A for both patient-derived DHS variants (Fig. 5j). The addition of NAD cofactor to the assay mixture did not change the strength of the interaction in the case of the wild-type and N173S variant proteins, whereas del305Y_306I showed a significant loss of binding affinity (Fig. 5k). In the presence of SPD we observed a further reduction in binding affinity for all three proteins (Fig. 5l). These results suggest insignificant changes within the core of the protein in the case of N173S and imply that the del305Y_306I deletion does not fully prevent NAD binding.
Together, the biochemical and mutational analyses suggest strong impacts of the del305_306I deletion on DHS stability, enzymatic activity, and substrate binding. In the case of the N173S mutation, the variant protein is stable, and the effects on activity and substrate binding are less pronounced.
## Discussion
Hypusination of eIF5A is an essential modification in eukaryotic cells. Impairment in this process leads to severe human disorders. Here, we employ high-resolution cryo-EM studies to examine the mechanism underlying the formation of the eIF5A-DHS complex. Furthermore, we visualize the previously hypothesized deoxyhypusination reaction transition state using ambient-temperature X-ray crystallography. Finally, we unveil the multifaceted influence of clinically relevant DHS mutants on the hypusination reaction.
Previous studies reported the crystal and cryo-EM structures of eIF5A bound to the ribosome20,34. The mechanism of eIF5A binding and export from the nucleus to the cytoplasm, using the Xpo4 and Pdr6 exportin systems, has also been elucidated35,36. The structures described herein allow us to complete the molecular landscape of eIF5A recognition by its binding partners. Our studies show that DHS binding utilizes only the part of the N-terminal domain of eIF5A containing the β-sheet structure. In contrast, the interaction of eIF5A with the ribosome is much more complex, involving almost the complete N-terminal and part of C-terminal domains (Fig. 6). In this context it is interesting to note that, of the recently described clinical variants of eIF5A37, only one (a T48N substitution) maps to the DHS binding site in eIF5A, whereas the rest affect ribosome binding sites. Furthermore, the binding sites of eIF5A to exportins also involve a greater number of residues than those that mediate the interaction with DHS. In the case of interaction with Xpo4, exportin recognizes both globular domains of eIF5A and the hypusine residue is buried in a deep acidic binding pocket. On the other hand, the binding of eIF5A to Pdr6 involves a smaller part of the N-terminal domain not containing the hypusination loop. Thus, eIF5A-DHS and eIF5-Pdr6 overlap only partially. Fig. 6Comparison of eIF5A interfaces. Several complexes bearing eIF5A are present in the PDB allowing us to compare the proteins binding interfaces responsible for various functionalities including posttranslational modification, nuclear import/export and resolving ribosomal stalling. eIF5A is shown as a dark pink cartoon in a complex with a binding partner shown as a coloured surface. Contact points, i.e. eIF5A in proximity less than 4.5 Å of the binding partner are marked with red. Depicted are complexes with a DHS (PDB ID: 8A0E blue), b ribosome (PDB ID: 5DAT, white), c Exportin-4 (PDB ID: 5DLQ, yellow) and d importin protein KAP122 (PDB ID: 6Q84, green). eIF5A is shown in the same relative position except for the ribosome where it is rotated by 180 degrees for the sake of clarity.
Our cryo-EM and crystal structures allow us to gain insights into the molecular mechanisms governing the synthesis of deoxyhypusine and to propose a highly plausible visualization of the reaction (Supplementary Movie 1). In the early stage of the DHS-catalyzed reaction, SPD is oxidized and cleaved into 1,3-diaminopropane (DAP) and the 4-aminobutyl moiety, which subsequently forms an imine linkage to Nε of K329DHS. This reaction is coupled with the reduction of NAD to NADH. As shown in the DHS transition-state mimic crystal structure, the SPD N6 atom is sequestered from the reduced nicotinamide of NADH. Hence, the NADH-driven reduction of the transition-state imine bond is significantly attenuated. Further analysis of the eIF5A-DHS structure highlights the importance of W327DHS, which serves as a gating residue for the incoming K50 of eIF5A. The conformational change of the W327DHS side chain upon binding to eIF5A is not only essential for the proper positioning of the to-be-hypusinated K50eIF5A, but it may also induce a significant shift of K329DHS, allowing the 4-aminobutyl transfer to form an imine intermediate with K50eIF5A. In turn, the imine bound becomes accessible to the hydride ion transferred from the NADH and deoxyhypusine (Dhp50eIF5A) is formed.
Our findings also provide a framework for a better understanding of the molecular mechanism of DHS deficiency. The reported exome sequencing of individuals affected with this recently recognized rare hereditary disease identified a total number of four dhps variants. However, each of the affected individuals has a single copy of the DHSN173S variant. Thus, we and others hypothesized that the N173S variant retained partial activity allowing for the survival and further organismal development. Indeed, we find that the N173S retains overall structural integrity and some enzymatic activity. However, our data also suggest that the N173S substitution influences the stability and/or mobility of the ball-and-chain motif of DHS. This, in turn, may affect the accessibility of the active site to the substrates, namely spermidine and eIF5A. Furthermore, N173DHS not only forms a hydrogen bond with another active site residue (N164DHS) but also interacts with T48eIF5A. As mentioned above, the substitution of this residue has been associated with neurological disease37. Indeed, mutations affecting both N173DHS and T48eIF5A, lead to similar clinical manifestations, consistent with our structural studies mapping both of these residues to the interface between eIF5A and DHS.
Taken together, we describe the molecular basis for eIF5A recognition by DHS and broaden our understanding of the hypusination reaction. Furthermore, we reveal the molecular mechanisms underlying DHS deficiency, the recently described rare hereditary disease. The importance of the hypusination axis for neural development has been demonstrated in a recent study showing that neuron-specific ablation of eIF5A or DHS leads to impairments in neurodevelopment and cognitive functions in mice38. Individuals with certain variants of either eIF5A37 or DOHH39 display similar developmental delay and intellectual disability to those affected by DHS deficiency22. Hence, future research needs to investigate the effects of those mutations on the mechanisms of hypusination.
## Protein cloning, expression and purification
The dhps gene encoding for full-length Homo sapiens (Uniprot: P49366, residues 1–369) and two pathological variants: N173S and del305Y_306I were synthesized (Genescript) and cloned with an N-terminal 6xHis-tag followed by a TEV cleavage site into pET24d plasmid using NcoI/BamHI restriction sites. For eIF5A expression pET28-MHL (Addgene) encoding 15-151 sequence of eIF5A1 fused to an N-terminal HisTag followed by a TEV recognition site or pETM-40 encoding eIF5A-2 with N-terminal MBP tag were used. Additionally, the pET24d plasmid encoding full-length DHS wt was used as a template for site-directed mutagenesis. Mutation specific primers were designed and purchased from Merck. Point mutations were introduced with site-specific primers (Merck) by PCR reaction and the DpnI-treated PCR products were used to transform XL1-Blue super-competent cells, which were grown on agar plates in the presence of kanamycin. The presence of appropriate mutations was confirmed by DNA sequencing (Eurofins Genomics GmbH). Human eIF5A1 and all DHS variants were expressed in *Escherichia coli* and purified according to the previously described procedures16. In short, cells were collected by centrifugation (17,700 × g, 12 min, 4 °C) and re-suspended in lysis buffer (50 mM Tris-HCl pH 7.8, 300 mM NaCl, 20 mM imidazole, $10\%$ glycerol, 10 mM β-ME). Cells were disrupted in the presence of lysozyme (Sigma-Aldrich) by sonication (15 min, 5 s pulse/3 s pause cycles). Homogenous cell solution, with the addition of benzonase (Sigma-Aldrich), was subjected to centrifugation (53,000 × g, 45 min, 4 °C). After centrifugation, the cleared lysate was applied onto an equilibrated affinity column (5 mL HisTrap HP; GE Healthcare Europe GmbH, Freiburg, Germany) and washed with wash buffer (50 mM Tris-HCl pH 7.8, 200 mM NaCl, 40 mM imidazole, $5\%$ glycerol, 5 mM β-ME) to elute non-specifically bounded proteins. The protein of interest was eluted with the elution buffer (50 mM Tris-HCl pH 7.8, 200 mM NaCl, 400 mM imidazole, $5\%$ glycerol, 5 mM β-ME). Eluted protein was dialyzed against the storage buffer (50 mM Tris- HCl pH 7.8, 200 mM NaCl, 5 mM β-ME) and subjected to overnight TEV protease cleavage during the second step of dialysis to remove the affinity tag. After HisTag cleavage, the tag-free protein was separated from the undigested protein and HisTagged-TEV protease during the reverse HisTrap column chromatography. Fractions containing HisTag-free protein were concentrated using Amicon Ultra (Millipore) concentrator (cut-off: 10,000 kDa) and subjected to size-exclusion chromatography (SEC) on a HiLoad $\frac{16}{60}$ Superdex 75 column in a storage buffer. Peaks of the highest purity were pooled, concentrated, aliquoted and flash-frozen in liquid nitrogen for further analysis. The full-length eIF5A-2 (Uniprot: Q9GZV4, residues 1–153) was synthesized (Genescript), and cloned into pETM40 vector using NcoI/XhoI restriction sites and expressed as an MBP-fusion protein in E. coli BL21(DE3) cells. Protein was purified using affinity (5 mL MBP-Trap HP; GE Healthcare Europe GmbH, Freiburg, Germany) and size-exclusion chromatography in storage buffer as an MBP-eIF5A-2 fusion protein.
## Complex reconstitution
We reconstituted the human eIF5A-DHSK329A complex using previously purified individual components namely eIF5A-1 and catalytically inactive DHSK329A. DHSK329A was mixed with a molar excess of eIF5A-1 (2 mole eIF5A per 1 mole DHS). Proteins were incubated in 200 mM Glycine/NaOH pH = 9.3 buffer with 200 mM NaCl in the presence of 1 mM NAD and 1 mM SPD. The complex was then purified by size-exclusion chromatogram using a Superdex 200 Increase $\frac{10}{300}$ column (GE Healthcare) pre-equilibrated with 200 mM Glycine/NaOH pH=9.3 buffer with 200 mM NaCl. Due to the very small difference in mass of the complex relative to DHS alone the fraction from the very front of the elution peak was used for further investigation.
## Protein crystallization
DHSwt, DHSN173S and DHSK329A crystals were obtained using previously determined conditions16. Briefly, 0.5 µl of protein (~20 mg/ml) was mixed with an equal volume of mother liquor solution consisting of a 0.025–0.125 mM carboxylic acid mix, 30–$60\%$ precipitant mix (MPD, PEG 1000, PEG 3350) and 100 mM Tris-Bicine with a pH of 8.5 and equilibrated by a sitting drop vapour diffusion technique. To obtain crystals in a complex with NAD and SPD, an equal volume of 50 mM mixture of SPD and NAD was added to the crystallization drop. Crystals appeared after 2 days and were soaked in cryo-solution containing $25\%$ of ethylene glycol in mother liquor and flash frozen in liquid nitrogen. For trapping the intermediate state a crystal was incubated for approximately half a minute in the well-solution supplemented with 10 mM NaBH3CN directly before mounting on the goniometer.
## Diffraction data collection and structure determination
Diffraction data for DHSN173S and DHSK329A crystals were collected at the MX-beamline 14.1 in cryogenic conditions. Diffraction data for the intermediate state were collected at MX-beamline 14.3 at room temperature under controlled humidity. Both beamlines are operated at the BESSY II electron storage ring (HZB, Berlin, Germany)40. Diffraction data were processed using XDS as implemented in the XDSAPP3 v.1.8 package41. All DHS crystal structures were solved by molecular replacement with Phaser42 using 6XXJ as a search model and rebuilt using Coot43. ~$1.5\%$ of the reflections were used for cross-validation analysis to monitor the refinement strategy in Phenix5. Water molecules were automatically placed during structure refinement, or further added using Coot and subsequently manually inspected. The quality of the model was validated using MolProbity44 and the final resolution cut-off was applied according to the PAIREF software45. The crystal structures were finalized with satisfactory geometrical parameters and rather low Rwork/Rfree values indicating the good quality of the structure. All significant data collection, structure refinements, and validation statistics are summarized in Supplementary Table 3. The analysis and comparison of structures were performed in PyMOL (Molecular Graphics System, Version 2.0 Schrödinger, LLC) or UCSF Chimera46.
## Sample preparation and cryo-EM data collection
Approximately 3 μL of the sample solution was applied on freshly glow-discharged TEM grids (Quantifoil R$\frac{2}{1}$, Cu, mesh 200) and plunge-frozen in liquid ethane with the use of Vitrobot Mark IV (Thermo Fisher Scientific). The following parameters were set—humidity: $100\%$, temperature: 4 °C, blot time: 2 s. Frozen grids were kept in liquid nitrogen until clipping and loading into the microscope. Cryo-EM data were collected at National Cryo-EM Centre SOLARIS (Kraków, Poland). Datasets containing 12419 movies (40 frames each) were collected with Titan Krios G3i microscope (Thermo Fisher Scientific) at the accelerating voltage of 300 kV, magnification of 105k and corresponding pixel size of 0.86 Å/px. K3 direct electron detector was used for data collection in BioQuantum Imaging Filter (Gatan) setup with 20 eV slit enabled. K3 detector was operated in counting mode with physical pixel resolution. Imaged areas were exposed to 39.90 e-/Å2 total dose each (corresponding to ~16.05 e-/px/s dose rate measured on vacuum). The images were acquired at under-focus optical conditions with a defocus range of −3.3 to −0.9 µm with 0.3 µm steps.
## cryoEM reconstruction
All micrographs (Supplementary Fig. 1a) were inspected and motion-corrected using WARP47. After CTF estimation and correction, particles were picked using an automated protocol embedded in WARP. Picked particles were imported to cryoSPARC v3.3.148 and the whole reconstruction was performed with this software from this point. Imported particles were submitted to reference-free 2D classification and assigned to 100 classes (Supplementary Fig. 1b) in order to remove false-positive picks. The remaining particles were submitted to subsequent reference-free 2D classifications to select the best possible particles for 3D reconstruction. Ab-initio reconstruction was performed for 5 classes with 100k of randomly selected particles, after which heterogeneous refinement protocol was employed using the complete data set. The most defined 3D class, containing most of the particles, was refined in C1 symmetry using a homogenous refinement protocol. To further clean up the particle set, iterative rounds of 3D classification were used. Particles were subclassified into 5 classes (Supplementary Fig. 1e) without reference volume. The best class, containing ~$91\%$ of the remaining particles, were forwarded to the next steps to “homogenous refinement” (Supplementary Fig. 1f) and “local refinement” protocols (Supplementary Fig. 1g) to get the final 3D map. All steps of 3D refinement were done applying C1 symmetry.
## Model fitting, refinement and validation
Structures of monomeric human eIF5A (PDB ID: 3CPF) and tetrameric DHS (PDB ID: 6XXL) were manually docked using UCSF ChimeraX46 followed by “Dock in Map” tool in Phenix49. Further manual model rebuilding was performed in COOT, followed by iterative cycles of real-space refinement in Phenix50. Final models were validated using MolProbity. Figures were created in UCSF ChimeraX46 and PyMOL.
## Thermal stability analysis
To investigate the protein stability and determine its melting temperature we used two complementary methods: Differential Scanning Fluorymetry (DSF) and analysis based on intrinsic fluorescence using a Tycho NT device (Nanotemper, Germany)51. For the DSF assay the protein solution (2 mg/ml) was incubated with 1:500 diluted Sypro Orange dye and storage buffer. During measurement fluorescence signal (λex = 492 nm, λem = 610 nm) from Sypro Orange was measured as a function of temperature between 5 and 95 °C in increments of 1.2 °C/min. The melting temperature was calculated from the inflexion point of the fluorescence curve. For comparison, protein stability was also assessed using label-free analysis with Tycho NT.6 (Nanotemper). The capillary was loaded with 10 µL of protein solution (2 mg/ml) and then heated from 35 to 95 °C in 3 min. The melting temperature was calculated from the inflection point of the ratio of 350 nm/330 nm curve. At least three independent repeats were done for each experiment.
## Single turnover fluorescence assay
The single turnover fluorescence assay was performed as described previously16,33. Briefly, 15 µM DHS variants were incubated in the presence of 1 mM NAD in 100 mM Tris-Bicine pH 8.5 buffer and the fluorescence excited at 350 nm and fluorescence emission at 441 nm was recorded. After ~2 min, SPD was added to a final concentration of 1 mM and measurement was immediately continued for ~2 min. An observed rapid burst of fluorescence, derived from a rising NADH concentration, was taken as the measure of DHS activity within the first step of its reaction. Each experiment was carried out at least three times.
## FRET measurements
To investigate the DHS affinity to polyamine, a FRET experiment relying on the energy transfer from DHS W327 residue to the di-hydro nicotinamide ring of NADH was performed, as described previously29. Briefly, 5 µM of DHS wt was incubated in the presence of 10 µM NAD in 0.2 M Glycine/NaOH pH = 9.3 buffer, and sequentially, $\frac{5}{10}$/20 µL of 100 µM SPD were added to the reaction mixture. After the addition of each substrate portion, the fluorescence spectrum (λex = 295 nm, λem = 441 nm) was recorded using Shimadzu Fluorescence Spectrometer RF-6000 until saturation was achieved. Experiments were carried out in triplicates. Fluorescence data were normalized for increasing reaction volume and apparent KD values were calculated using the Hill model implemented in Graphpad Prism 8 (GraphPad Software, Inc., CA, USA). The maximal binding (Bmax value) of DHS wt was used as a reference for final calculations.
## Analysis of the protein oligomeric state by mass photometry
Mass photometry data were collected on a Refeyn OneMP instrument. The instrument was calibrated with BSA standard protein. Ten microliters of selected protein was applied to 10 µL buffer on a coverslip resulting in a final concentration ~1-5 nM. Movies were acquired by using AcquireMP 2.3.0 software followed by data processing in DiscoverMP 2.3.0 software. Masses of DHS variants were estimated by fitting a Gaussian distribution into the mass histograms and taking the value at the apex of the distribution. Probability density functions were extracted and overlaid in GraphPad Prism 8.
## Microscale thermophoresis (MST)
A Monolith NT.115 instrument (NanoTemper Technologies) was used to analyse the complex formation between DHS and eIF5A. To investigate how DHS variants bind to eIF5A, His-tagged DHS proteins were labelled with Monolith His-tag Labeling Kit RED-tris-NTA 2nd Generation (NanoTemper Technologies). Experiments were performed in assay buffer (50 mM Tris, 200 mM NaCl. 5 mM β-ME pH 8). For all the measurements, sixteen eIF5A dilutions in the range of 0.00175–57.5 μM were prepared and then mixed with 8.32 nM of DHS-labelled proteins followed by loading into Monolith NT.115 capillaries. For eIF5A pathological variants, 50 nM labelled His-tagged eIF5A variants were incubated with 0.000122–8 µM DHS wt solutions. Initial fluorescence measurements followed by thermophoresis measurements were carried out using $40\%$ excitation power and $60\%$ MST power, respectively. Data for at least three independent measurements were analysed (MO.Affinity Analysis software, NanoTemper Technologies), allowing for a determination of dissociation constants (KD). The data were presented using GraphPad Prism 8 software. The interactions between DHS variants and eIF5A in the presence of NAD with or without SPD were carried out in assay buffer supplemented with appropriate ligands to a final concentration of 1 mM.
## Hypusination assay and western blot analysis
The enzymatic activity of DHS variants was assessed by western blot analysis. Five micrograms of His-tagged eIF5A1 was incubated in the presence of 15 µg of appropriate DHS variant in the reaction mixture containing 1 mM NAD, 1 mM SPD in 0.2 M Glycine/NaOH pH = 9.3 with 0.2 M NaCl buffer. Reactions were also performed in the absence of DHS wt and eIF5A as controls. All reaction mixtures were incubated for 1 h at 37 °C and then 100 mM SPD was added to terminate the reaction. Samples were loaded onto an SDS-PAGE gel and blotted to a nitrocellulose membrane (25 mM Tris,192 mM glycine and $20\%$ methanol) and blocked with $5\%$ skim milk in PBS (pH 7.0) for 1 h at RT. Membranes were incubated for 1 h at RT with a primary rabbit FabHpu98 antibody (Creative Biolabs) diluted 1:4000 in $5\%$ skim milk in PBS (pH 7.0)52. Membranes were washed with a T-PBS buffer (PBS supplemented with $0.1\%$ Tween 20). As a next step secondary anti-rabbit-IgG horse-radish, peroxidase-conjugated antibody (Cell signalling) was used (1:2000) for 1 h at RT. The membranes were again washed with T-PBS buffer, followed by development with an enhanced chemiluminescence detection kit (SuperSignal West Pico Plus, Thermo Scientific) according to the manufacturer’s instructions.
## Hydrogen/deuterium exchange mass spectrometry (HDX-MS)
Individual eIF5A and DHSK329A proteins and the eIF5A-DHSK329A complex, which was prior HDX-MS constituted and purified by size-exclusion chromatography (see above), were employed at concentrations of 40 µM. Preparation of samples for HDX-MS experiments was aided by a two-arm robotic autosampler (LEAP Technologies) and conducted essentially as described previously with minor modifications53.
In brief, 7.5 μl of protein solution (eIF5A, DHSK329A or the eIF5A/DHSK329A complex) was mixed with 67.5 μl D2O-containing buffer (25 mM HEPES-Na pH 7.5, 150 mM NaCl, 5 mM β-mercaptoethanol) to initiate the hydrogen/deuterium exchange reaction. After incubation at 25 °C for 10, 30, 95, 1,000 or 10,000 s, 55 μl were withdrawn from the reaction and mixed with 55 µl quench buffer (400 mM KH2PO4/H3PO4, 2 M guanidine-HCl, pH 2.2), which was predispensed and cooled at 1 °C. 95 µl of the resulting mixture was injected into an ACQUITY UPLC M-Class System with HDX Technology54. Undeuterated samples were prepared similarly by tenfold dilution in H2O-containing buffer followed by approximately 10 s incubation at 25 °C. The injected quenched HDX reaction was flushed out of the sample loop (50 µl) with constant flow (100 μl/min) of water + $0.1\%$ (v/v) formic acid and guided to a cartridge (2 mm × 2 cm) that was filled immobilized porcine pepsin and digested at 12 °C. The resulting peptic peptides were trapped on a trap column cartridge (2 mm × 2 cm) filled with POROS 20 R2 material (Thermo Scientific) kept at 0.5 °C. After 3 min of trapping, the trap column was placed in line with an ACQUITY UPLC BEH C18 1.7 μm 1.0 × 100 mm column (Waters), and the peptides eluted at 0.5 °C with a gradient of water + $0.1\%$ (v/v) formic acid (A) and acetonitrile + $0.1\%$ (v/v) formic acid (B) at 60 µl/min flow rate as follows: 0–7 min/95–$65\%$ A, 7–8 min/65-$15\%$ A, 8–10 min/$15\%$ A. Eluting peptides were guided to a G2-Si HDMS mass spectrometer with ion mobility separation (Waters), and ionized by electrospray ionization (capillary temperature 250 °C, spray voltage 3.0 kV). Mass spectra were acquired over a range of 50 to 2000 m/z in enhanced high definition MS (HDMSE) or high definition MS (HDMS) mode for undeuterated and deuterated samples, respectively55. Lock mass correction was conducted with [Glu1]-Fibrinopeptide B standard (Waters). During chromatographic separation of the peptides, the pepsin column was washed three times with 80 µl of $4\%$ (v/v) acetonitrile and 0.5 M guanidine hydrochloride, and blanks were performed between each sample. Three technical replicates (independent H/D exchange reactions) were measured per incubation time. No correction for HDX back exchange was conducted.
Further data analysis was conducted as described53. Peptides were identified with ProteinLynx Global SERVER (PLGS, Waters) from the non-deuterated samples acquired with HDMSE by employing low energy, elevated energy, and intensity thresholds of 300, 100 and 1000 counts, respectively. Hereby, the identified ions were matched to peptides with a database containing the amino acid sequences of eIF5A, DHSK329A, porcine pepsin, and their reversed sequences with the following search parameters: peptide tolerance = automatic; fragment tolerance = automatic; min fragment ion matches per peptide = 1; min fragment ion matches per protein = 7; min peptide matches per protein = 3; maximum hits to return = 20; maximum protein mass = 250,000; primary digest reagent = non-specific; missed cleavages = 0; false discovery rate = 100. Deuterium incorporation into peptides was quantified with DynamX 3.0 software (Waters). Only peptides that were identified in all undeuterated samples and with a minimum intensity of 30,000 counts, a maximum length of 30 amino acids, a minimum number of three products with at least 0.1 product per amino acid, a maximum mass error of 25 ppm and retention time tolerance of 0.5 min were considered for analysis. All spectra were manually inspected and, if necessary, peptides omitted (e.g., in case of low signal-to-noise ratio or presence of overlapping peptides).
The observable maximal deuterium uptake of a peptide was calculated by the number of residues minus one (for the N-terminal residue that after proteolytic cleavage quantitatively loses its deuterium label) minus the number of proline residues contained in the peptide (lacking an exchangeable peptide bond amide proton). For the calculation of HDX in per cent the absolute HDX was divided by the theoretical maximal deuterium uptake multiplied by 100. To render the residue-specific HDX differences from overlapping peptides for any given residue of eIF5A or DHSK329A, the shortest peptide covering this residue is employed. Where multiple peptides are of the shortest length, the peptide with the residue closest to the peptide’s C-terminus is utilized.
An overview of the parameters and characteristics of the HDX-MS experiments is given in Supplementary Table 2.
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|
---
title: 'Striatal dopamine D2-like receptors availability in obesity and its modulation
by bariatric surgery: a systematic review and meta-analysis'
authors:
- Gabriela Ribeiro
- Ana Maia
- Gonçalo Cotovio
- Francisco P. M. Oliveira
- Durval C. Costa
- Albino J. Oliveira-Maia
journal: Scientific Reports
year: 2023
pmcid: PMC10042861
doi: 10.1038/s41598-023-31250-2
license: CC BY 4.0
---
# Striatal dopamine D2-like receptors availability in obesity and its modulation by bariatric surgery: a systematic review and meta-analysis
## Abstract
There is significant evidence linking a ‘reward deficiency syndrome’ (RDS), comprising decreased availability of striatal dopamine D2-like receptors (DD2lR) and addiction-like behaviors underlying substance use disorders and obesity. Regarding obesity, a systematic review of the literature with a meta-analysis of such data is lacking. Following a systematic review of the literature, we performed random-effects meta-analyses to determine group differences in case–control studies comparing DD2lR between individuals with obesity and non-obese controls and prospective studies of pre- to post-bariatric surgery DD2lR changes. Cohen's d was used to measure effect size. Additionally, we explored factors potentially associated with group differences in DD2lR availability, such as obesity severity, using univariate meta-regression. In a meta-analysis including positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies, striatal DD2lR availability did not significantly differ between obesity and controls. However, in studies comprising patients with class III obesity or higher, group differences were significant, favoring lower DD2lR availability in the obesity group. This effect of obesity severity was corroborated by meta-regressions showing inverse associations between the body mass index (BMI) of the obesity group and DD2lR availability. Post-bariatric changes in DD2lR availability were not found, although a limited number of studies were included in this meta-analysis. These results support lower DD2lR in higher classes of obesity which is a more targeted population to explore unanswered questions regarding the RDS.
## Introduction
Obesity is a global challenge for public health, with well-known associations with morbidity and mortality1,2. One major concern regarding obesity treatment is poor sustainability of lifestyle modifications over time2,3. On the other side, bariatric surgery, namely gastric bypass and sleeve gastrectomy, while indicated for severe obesity only, is a successful intervention for sustained weight loss4–6. Furthermore, some anti-obesity drugs, namely phentermine/topiramate, naltrexone/bupropion, liraglutide and orlistat, are used as co-adjuvants of behavioral modification7. The first two options mentioned above include drugs acting, at least in part, via the dopaminergic system8, suggesting potential clinical relevance of this neurotransmitter system in obesity.
Indeed, the importance of the brain in feeding behavior regulation is well established9–12. Several brain structures take part in this function12. For example, the hypothalamus integrates appetite-regulating signals, while the caudal brainstem coordinates ingestion, digestion, and food absorption12. Reward and learning processes relevant to feeding behavior occur in corticolimbic regions12, with the striatum, including the caudate, putamen and nucleus accumbens (NAc), as a crucial structure for the processing of food reward13,14. Neurons in the striatum can express the five types of dopamine receptors, with D1 and D2 receptors being predominant13.
The striatum's decreased availability of dopamine D2-like receptors (DD2lR) has been reported in patients with drug misuse syndromes9,10,15,16. Comparable findings were found in severe obesity, with reports of decreased DD2lR availability and a negative correlation between DD2lR availability and body mass index (BMI)17, contributing to converging theories for excessive eating and substance misuse9–11. According to these theories, a blunted reward system, resulting, for example, from reduced DD2lR availability, is suggested to enhance the drive to overeat as a compensation operating through increased dopamine release9–11. On the other hand, DD2lR availability may decrease due to chronic dopaminergic overstimulation resulting from overeating9–11. These interpretations align with the concept of a 'reward deficiency syndrome', integrating reduced DD2lR availability with addiction-like behaviors15,16,18–20.
In humans, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are the primary techniques to assess DD2lR availability, using several radiopharmaceuticals with an affinity for DD2lR12,21,22. While some PET and SPECT studies compared individuals with obesity and controls for DD2lR availability12, only a few studies performed such assessments before and after bariatric surgery12. However, results are not consensual, with reports of decreased, increased or unaltered DD2lR availability in obesity, and only one meta-analysis, restricted to [11C]raclopride PET studies published until 201523. Although previous non-systematic reviews raised interesting points to conciliate discrepancies12,22,24, for guidance of future studies a systematic review and meta-analysis is crucial. Furthermore, literature on prospective assessment of striatal DD2lR in bariatric surgery has not been systematically reviewed or meta-analyzed. Therefore, with this work, we aimed to systematically review the literature for quantitative evidence that DD2lR availability is lower in obesity than in non-obese controls and increases after bariatric surgery.
## Search strategy
Before starting the study, the proposed methodology was published in the Prospero Platform (registration number PROSPERO 2020: CRD42021229282). We systematically reviewed the literature to identify case–control studies comparing DD2lR between adults with obesity [body mass index (BMI) ≥ 30 kg/m2] and non-obese controls (BMI < 30 kg/m2). We also systematically reviewed prospective studies that assessed changes in DD2lR availability following bariatric surgery, either gastric bypass or sleeve gastrectomy. The literature search was performed using a predetermined syntax based on Medical Subject Headings (MeSHs) terms. Search terms were defined according to the PRISMA statement, comprising the following fields: [1] nuclear medicine imaging techniques; [2] striatal DD2lR availability; [3] the disorder of interest—obesity; [4] the intervention of interest—bariatric surgery. In addition, we applied a filter to restrict search results to papers with human subjects, with no restrictions regarding publication year or country of origin (Supplementary Table 1). The search syntax was the following: (‘Positron Emission Tomography’ OR ‘PET’ OR ‘Single Photon Emission Computed Tomography’ OR ‘SPECT’) AND (‘dopamine’ OR ‘striatum’ OR ‘basal ganglia’ OR ‘putamen’ OR ‘caudate’ OR ‘accumbens’ OR ‘D2’ OR ‘DD2R’) AND (‘obesity’ OR ‘overweight’ OR ‘body mass index’ OR ‘BMI’ OR ‘bariatric’ OR ‘gastric bypass’ OR ‘sleeve’ OR ‘gastrectomy’). The standardized search was performed on three electronic bibliographic databases (PubMed, Web of Science, and Embase) on January 13, 2021.
## Study selection and risk of bias assessment
Duplicate articles were eliminated using Zotero 5.0.96.2, after which two researchers (GR and AM) performed article selection independently in three subsequent steps: title, abstract, and full-text review. Upon completing each step, the two researchers reached a consensus on articles selected for the following stage, with a third researcher participating if necessary. At the end of the full-text review, we manually searched the reference lists of the selected papers to identify additional relevant articles for the final complete paper consensus. Studies were selected according to the presence of both of the following inclusion criteria: [1] case–control studies, with cases defined as having obesity (BMI ≥ 30 kg/m2) and a control group without obesity (BMI < 30 kg/m2); or prospective cohort studies including patients with obesity undergoing bariatric surgery, either gastric bypass and sleeve gastrectomy, assessed before and after surgery; [2] assessment of DD2lR-specific binding expressed as the binding potential (BP) obtained from the region of interest (ROI)-based analyses. While we did not impose publication date, country of origin, or radiopharmaceuticals restrictions, we only considered English, French, Portuguese, Spanish or German-written articles and excluded animal studies and literature reviews.
Two researchers (GR and AM) independently assessed the methodological quality of each article included in the review by using the Newcastle–Ottawa scale (NOS)25, either for case–control or cohort studies. The final appraisal was reached by consensus. For this review, studies awarded a NOS score equal to or higher than eight were considered high-quality studies26.
## Data extraction
Data was extracted manually and independently by two researchers (GR and AM), including [1] striatal DD2lR BP (primary outcome) and putamen, caudate, and NAc DD2lR BP (secondary outcomes); [2] first author name, publication title, country of origin, year, journal, study type; [3] sample size, percentage of men, mean age at inclusion, years of education, BMI at inclusion, BMI after surgery and percent weight loss (in the case of prospective cohort studies); [4] nuclear medicine methods namely scanner type, radiopharmaceuticals, co-registration with magnetic resonance imaging (MRI), brain reference region used in ROI analyses and acquisition time; [5] percentage of smokers, current or past major psychiatric disorders (including eating disorders, alcohol or substance abuse), severe medical conditions (including neurological conditions and diabetes mellitus type 1 or 2), history of head trauma, current or past exposure to dopaminergic or anti-diabetic medication, pregnancy or breastfeeding. We also contacted the authors of 16 papers to obtain additional information about study eligibility or DD2lR availability that was not reported in the papers. The requested information was provided for ten articles, including four databases with individual socio-demographic, BMI, and DD2lR data27–30. Although two studies had overlapping cohorts28,29, the corresponding author provided non-overlapping individual data. Some papers did not report DD2lR availability for the whole striatum but instead estimated specific ROIs (putamen, caudate, and Nac/ventral striatum). Thus, to maximize the number of studies included in the meta-analysis, data were either directly extracted from articles31–33 or analyzed using databases provided by the authors27–30. The research team determined the weighting factors for each region based on volumes obtained from the international consortium for brain mapping (ICBM) Montreal neurological institute (MNI) template34, resulting in the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Striatal DD2lRBP = (0.54 \times Putamen BP) + (0.42 \times Caudate BP) + (0.04 \times NAc/VS BP).$$\end{document}StriatalDD2lRBP=(0.54×PutamenBP)+(0.42×CaudateBP)+(0.04×NAc/VSBP).
In cases where we calculated the weighted mean with values extracted from the papers, rather than databases31–33 the formula to calculate the BP standard deviation (SD) was the following: SD = weighted mean × coefficient of variation (CoV) of the remaining studies (where CoV = σ∕μ). For Guo J et al.31, however, that reported mean and $95\%$ confidence intervals (CI), SD was calculated using the following formula35: SD = √N × (upper limit − lower limit) ∕ (2 × value from t-distribution). In Steele KE et al.36, DD2lR BP was obtained from a graph using a millimetric grid. Also, in this study36, we calculated the SD of demographic data using individual mean values reported in the paper.
## Statistical analysis
Considering the anticipated high heterogeneity between studies, we performed a random-effects meta-analysis to compare striatal DD2lR availability between individuals with obesity and non-obese controls. The model computed the between-groups effect size (Cohen's d) weighted by sample size and I-squared (I2) index to assess the heterogeneity of effect sizes. In addition, when at least ten studies were available35, we tested publication bias using Egger's test37,38, visual inspection of funnel plot38,39, and leave-one-out meta-analyses. Finally, to address the hypothesis that bariatric surgery induces changes in striatal DD2lR availability, we used random-effects meta-analysis, computing the post-surgical change in striatal DD2lR BP. The effect size of post-surgery change (Cohen's d) was calculated according to Borenstein formulas for correlated data40, assuming an r of 0.5, according to prior literature40–42.
Subgroup meta-analyses were performed by comparing groups of papers according to variables of interest: [1] obesity severity—class I-II (BMI 30 to < 40 kg/m2) and class III or higher (BMI ≥ 40 kg/m2); [2] ROIs—putamen, caudate and NAc/ventral striatum; [3] scanner type—PET and SPECT; [4] radiopharmaceuticals—[11C]raclopride, [123I]IBZM, [18F]fallypride and [11C]NMB (we did not analyze [11C]PHNO since there was only one study); [5] MRI co-registration—PET with co-registration and PET without co-registration; [6] Study quality according to NOS score—high quality (≥ 8) and others (< 8)26. We also performed a univariate meta-regression to test the association between obesity level and DD2lR availability, using the Cohen's d of striatal DD2lR BP (obesity vs. controls) as the dependent variable. The independent variables were the mean BMI of each group or the BMI difference between cases and controls (ΔBMI). This analysis included 11 studies, following best practices for meta-regression35. In addition, we performed similar metaregression with age or gender as independent variables.
Descriptive statistics and weighted means were computed using SPSS version 25 (SPSS Inc., Chicago, IL, USA) and all meta-analyses and meta-regressions using Stata Statistical Software: Release 15 (StataCorp LLC, College Station, TX). Finally, figures and graphs were prepared using Adobe Illustrator version CC 2019 (Adobe Inc., San Jose, CA, USA).
## Search results
From an initial pool of 559 articles, 17 articles17,23,27–33,36,43–49 were eligible for systematic review (see Fig. 1 for the article selection flowchart). The 17 studies provided 13 case–control comparisons between individuals with obesity and controls and 4 were prospective cohort studies conducted before and after bariatric surgery. Three of the studies had overlapping cohorts that could not be adequately resolved43,46,48, and thus the overlapping longitudinal article with shorter follow-up was excluded from the meta-analyses48. Regarding case–control studies, we retained those with the primary aim of comparing striatal DD2lR BP between obesity and control groups over studies that addressed other questions43,46. Furthermore, we could not extract striatal DD2lR BP values from one article23, and thus it was also excluded from meta-analyses. Figure 1Article selection flowchart. We performed a systematic review according to PRISMA guidelines, and from an initial pool of 840 titles, 17 articles were eligible for qualitative analysis, of which 13 were included in the meta-analysis. These included ten case–control studies comprising obesity and control groups and three prospective cohort studies of patients with obesity assessed before and after bariatric surgery. Of the prospective studies, one study also comprised a control group at baseline, and thus the data was included in case–control studies. Abbreviations: DD2lR—striatal dopamine D2-like receptor; BP—Binding Potential; ROI—Region of Interest.
## Systematic review
Regarding case–control studies, sample sizes were mostly small to moderate, with a median sample size of 15 participants in both groups, ranging from 6 to 27 in the obesity group and 10 to 112 in the controls. Prospective cohort studies also had small samples, with a median of 8 participants. In case–control studies groups had, on average, similar age and gender distribution, but with some studies not including male participants (Table 1 summarizes the characteristics of the included studies). However, the BMI of the obesity group was heterogeneous across studies, with seven comprising individuals with class III obesity or higher17,23,28,43–46, and the remaining six27,29–32,47 including class I or class II obesity. Regarding prospective cohort studies, all samples had class III obesity and the follow-up time was mainly short (6–9 weeks)33,36,48, with only one study49 reporting long-term assessment (2.1 to 3.6 years). Three of four studies36,48,49 comprised gastric bypass patients only, with the remaining prospective study33 including patients after gastric bypass or sleeve gastrectomy. Most studies (12 [$70.6\%$]) were conducted in the United States, with only a minority (5 [$29.4\%$]) comprising European participants from The Netherlands (4 [$23.5\%$]) and Finland (1 [$5.9\%$]). The majority of the studies (13 [$76.5\%$]) used PET17,23,27–33,36,43,46,47 with different radiopharmaceuticals, namely [11C]raclopride17,23,27,33,36,43, [11C]NMB28,29,46,47, [18F]fallypride30,31, and [11C]PHNO32, while four studies ($23.5\%$) used [123I]IBZM—SPECT44,45,48,49 (see Supplementary Table 2 for a summary of methodological characteristics). As assessed by the NOS, the average quality of the 17 studies was 6.9 (see Supplementary Table 3 for details).Table 1Description of eligible studies. PublicationNumber of subjectsAge at inclusion Years; Mean (SD)Gender (% male)Body mass index (kg/m2)Striatal DD2lR BPNOSObesityControlsObesityControlsObesityControlsObesityControlsObesity vs. ControlsPost-op ChangesCase–control studiesWang et al.17101038.9 (7.3)37.5 (5.9)507051.2 (4.8)24.7 (2.6)↓5Volkow et al.a43101235.9 [10]33.2 [8]505051 [5]25 [3]↓5de Weijer et al.44151537.8 [7]28 (10.4)0046.8 (6.5)21.7 (2.1)↓6van De Giessen et al.45151536.3 [4]38.5 (5.6)0042.9 (4.9)21.8 (1.8)↓7Karlsson et al.23131439.1 (10.7)44.9 (12.9)0041.9 (3.9)22.7 (2.9) ↔ 7Pepino et al.b46221931.2 (6.3)28.3 (5.4)13.621.140.3 [5]22.5 (2.4) ↔ 7Eisenstein et al.28151532.5 (5.9)29.7 (5.6)2026.740.3 (4.9)22.6 (2.2) ↔ d7Eisenstein et al.29272031.5 (6.6)28.6 (5.2)14.82539.9 (4.8)22.4 (2.4) ↔ d7Eisenstein et al.47221731.4 (6.3)28.5 (5.5)13.623.539.6 (5.2)22.1 [2] ↔ 7Guo et al.31202335.0 (7.4)28.0 (6.1)505236.1 (4.6)22.4 (2.5)↑6Gaiser et al.32141437 (10.1)34.9 (10.2)71.471.435.3 (4.5)22.3 (1.8)↑8Dang et al.301811246.8 (18.1)33.9 (17.6)44.444.634.5 [4]24.1 [3] ↔ d7Wang et al.2761353.3 (3.4)51.5 (3.2)5061.533 (2.4)24.6 (2.8) ↔ d4Prospective cohort studiesde Weijer et al.c481840.4 [8]045.7 (6.3) ↔ 9van der Zwaal et al.49111144.3 [6]40.5 [4]0045.2 (6.7)21.9 [2]↓↑9Steele et al.36532.2 (7.3)045.2 (5.9)↑9Dunn JP et al.33545.8 (3.8)043.2 (5.6)↓8Note: Studies are organized by the body mass index (BMI) level of the obesity group. Abbreviations: Striatal DD2lR BP, Striatal dopamine D2-like receptor binding potential (BP). NOS, Newcastle–Ottawa scale.aSample overlapped with Wang G-J et al., 2001.bSample overlapped with Eisenstein SA et al., 2013, Eisenstein SA et al., 2015a or Eisenstein SA et al., 2015b.cSample overlapped with van der Zwaal EM et al., 2016.dDD2lR BP group values were calculated for metanalysis based on data provided by the authors.
In a qualitative analysis of the eligible case–control studies ($$n = 14$$), 5 ($35.7\%$)17,43–45,49 reported decreased striatal dopamine D2 receptor availability in obesity relative to controls. All 5 of these studies included obesity groups with class III obesity and were conducted with [11C]raclopride—PET17,43 or [123I]IBZM—SPECT44,45,49. Three of these 5 studies had exclusively female participants44,45,49. Seven studies ($50\%$)23,27–30,46,47 showed unaltered DD2lR availability between groups, one of which was not considered for analysis46 since it had an overlapping cohort with 3 of the remaining studies28,29,47. Half of these studies used [11C]NMB PET28,29,47, a DD2R-specific tracer, and the remaining used [11C]raclopride23,27 or [18F]fallypride30, a tracer preferring the high-affinity state of D$\frac{2}{3}$R22. With one exception23, these studies included both female and male participants, and they had a wider BMI range, with the obesity groups comprising participants with obesity from classes I to III. Only two studies ($14.3\%$)31,32 reported increased DD2lR availability in the obesity group. Both included males and females and comprised, on average, participants with obesity class II. These studies used PET performed either with [18F]fallypride31 or [11C]PHNO PET32, a D3-preferring tracer22. All the studies attempted to minimize confounding variables, namely by excluding participants with current or past major psychiatric disorders (including eating disorders, alcohol or substance abuse), severe medical conditions (including neurological conditions and diabetes mellitus type 1 or 2), and current or past exposure to dopaminergic or anti-diabetic medication. Not all studies reported if head trauma was an exclusion factor23,31,45,47,49.
Regarding the four prospective cohort studies eligible for qualitative analysis33,36,48,49, two had partially overlapping cohorts48,49, leading to the decision to retain only the study with longer follow-up49. Of the three resulting studies, two reported increased striatal DD2lR after surgery36,49, one of which conducted in Europe (The Netherlands) with [123I]IBZM—SPECT49 and the other in the US using [11C]raclopride—PET36. Patients in these two studies had similar BMIs before undergoing bariatric surgery, but the mean age at inclusion differed between the two, as did follow-up, that was 6 weeks after surgery for one study36 and up to 3.6 years for the other49. The third prospective study showed decreased DD2lR after surgery33. This study was conducted in the US with [11C]raclopride—PET, had a short follow-up of approximately 7 weeks) and also included patients undergoing sleeve gastrectomy33, while the two studies reporting increased striatal DD2lR after surgery36,49 only included gastric bypass patients.
## Striatal dopamine D2 receptor availability in obesity
In the meta-analysis, including PET and SPECT studies, striatal DD2lR availability did not differ significantly between individuals with obesity and non-obese controls (Cohen's d = − 0.4, $$P \leq 0.1$$; $$n = 11$$), as shown in Fig. 2. Yet, there was significant heterogeneity across studies (I2 = $78.4\%$, P ≤ 0.0001). In addition, a visual assessment of the funnel plot revealed an apparent asymmetry, even though Egger's test for publication bias was not statistically significant ($$P \leq 0.1$$; Supplementary Fig. 1. Notably, in this primary analysis, differences between obesity and control groups in striatal DD2lR availability were sensitive to excluding single studies (see Supplementary Fig. 1).Figure 2Striatal dopamine D2-like receptor availability random-effects meta-analysis in obesity and healthy controls. A pooled meta-analysis of PET and SPECT studies showed that the availability of striatal dopamine D2-like receptor (DD2lR) did not differ between the obesity and control groups. Notes: Studies are organized according to scanner type—positron emission tomography (PET—lighter blue) or single-photon emission computed tomography (SPECT—darker blue). Within scanner type, studies are ranked from highest to lowest mean body mass index (BMI) of the obesity group. Studies in black writing comprise obesity groups with class III obesity or higher, while those in grey/blue comprise class I—II obesity.
Some studies included patients with very high BMIs, while others comprised lower BMI ranges, so we compared striatal DD2lR availability according to the obesity level. We found lower striatal DD2lR in the obesity groups when the meta-analysis was restricted to studies comprising class III obesity or higher (Cohen's d = − 1.2, $$P \leq 0.001$$; $$n = 5$$, Fig. 3a). However, we did not find significant differences for studies including only class I-II obesity (Fig. 3b). We further explored the effect of obesity levels using univariate meta-regression. As shown in Fig. 4a, higher mean BMI in the obesity group was significantly associated with larger effect sizes of case–control difference in striatal DD2lR availability (B = − 0.1, $$P \leq 0.01$$, adjusted R2 = $69.1\%$; $$n = 11$$), an effect that was not found for BMI in the control group ($B = 0.04$, $$P \leq 0.9$$, adjusted R2 = − $15.0\%$; $$n = 11$$). Accordingly, higher ΔBMI (obesity-controls) was also significantly associated with larger effect sizes of case–control difference in DD2R availability (B = − 0.1, $$P \leq 0.01$$, adjusted R2 = $60.4\%$; $$n = 11$$; Fig. 4b).Figure 3Striatal dopamine D2-like receptor availability random-effects meta-analysis in obesity and healthy controls according to classes of obesity. Meta-analysis of striatal dopamine D2-like receptor (DD2lR) availability in individuals with obesity and controls showed significant group differences favoring lower striatal DD2lR in the obesity group when only studies comprising class III obesity or higher were analyzed (Fig. 3a). No differences were found in a meta-analysis of studies that included class I-II obesity (Fig. 3b). Notes: Studies are organized according to scanner type—positron emission tomography (PET—lighter blue) or single-photon emission computed tomography (SPECT—darker blue). Within scanner type, studies range from the highest to lowest mean body mass index (BMI) of the obesity group. Class III obesity or higher: BMI ≥ 40 kg/m2; Class I-II obesity: BMI from 30 to < 40 kg/m2.Figure 4Univariate meta-regression of the association between striatal dopamine D2-like receptor availability and obesity level. An increasing body mass index (BMI) of the obesity group was significantly associated with striatal dopamine D2-like (DD2lR) availability favoring lower values in the obesity group (Fig. 4a). Accordingly, a higher BMI difference between the obesity and control groups (ΔBMI) was significantly associated with DD2lR availability, favoring lower values in the obesity group (Fig. 4b).
Since age and gender could also influence striatal DD2lR availability, we tested these variables in other univariate meta-regressions. We did not find significant associations with effect sizes of case–control difference in DD2R availability for mean participant age, in either the obesity groups (B = − 0.04, $$P \leq 0.4$$, adjusted R2 = − $5.2\%$; $$n = 11$$) or the control groups (B = − 0.04, $$P \leq 0.3$$, adjusted R2 = $4.2\%$; $$n = 11$$), nor for gender distribution, in either the obesity groups (B = − 0.02, $$P \leq 0.1$$, adjusted R2 = $38.9\%$; $$n = 11$$) or the control groups ($B = 0.02$, $$P \leq 0.1$$, adjusted R2 = $31.2\%$; $$n = 11$$).
In further subgroup analyses, we did not find significant differences between cases and controls in meta-analyses for specific striatal regions, namely putamen, caudate, and NAc (Supplementary Fig. 3a–c). Although there were no group differences in a meta-analysis of PET studies, for SPECT studies, DD2lR availability was significantly lower in the obesity group (Cohen's d = − 1.5, P ≤ 0.0001; $$n = 3$$; Supplementary Fig. 4a, b). Consistently, there were no group differences for the majority of the radiopharmaceuticals, namely, [11C]raclopride (Cohen’s d = − 0.8, $$P \leq 0.2$$; $$n = 2$$), [18F]fallypride (Cohen’s $d = 0.3$, $$P \leq 0.5$$; $$n = 2$$) or [11C]NMB (Cohen’s d = − 0.1, $$P \leq 0.8$$; $$n = 2$$), but a significant difference was found for [123I]IBZM (Cohen’s d = − 1.5, P ≤ 0.0001; $$n = 3$$), that is the radiopharmaceuticals used for SPECT. Of note, all [123I]IBZM SPECT studies included class III or higher participants, while in PET studies, there was a wider BMI range. Finally, we did not find group differences in separate meta-analyses according to MRI co-registration within PET studies (Supplementary Fig. 5a,b) or study quality scores (Supplementary Fig. 6a,b).
## Striatal Dopamine D2 Receptor availability following bariatric surgery
One of the primary aims of this study was to determine if striatal DD2lR availability changed after bariatric surgery. However, a small number of studies were available ($$n = 4$$)33,36,48,49, one of which was not included in the meta-analysis48 due to an overlapping sample49. Overall, we did not find significant changes in striatal DD2lR availability from pre- to post-surgery (Cohen's $d = 0.2$, $$P \leq 0.6$$; $$n = 3$$; Fig. 5). Figure 5Striatal dopamine D2-like receptor availability random-effects meta-analysis after bariatric surgery. This analysis did not show changes in striatal DD2lR availability from pre- to post-bariatric surgery.
## Discussion
Initial theories about reward processing in obesity suggested that lower striatal DD2lR availability was associated with a reward deficiency, contributing to overeating10. However, literature in this field has been challenging to reconcile given the inconsistent findings that have been reported since Wang et al.17, that may be explained by a nonlinear relationship between obesity and central dopaminergic physiology. Furthermore, in line with the original theory, there is some evidence that bariatric surgery may result in a normalization (i.e., increase) of DD2lR availability. Thus, this study systematically reviewed and meta-analyzed published PET and SPECT studies, comparing DD2lR availability in obesity and controls and performing prospective assessments following bariatric surgery.
In what regards to DD2lR availability in obesity relative to non-obese individuals, we did not find differences between the obesity and control groups in a pooled analysis of PET and SPECT studies. However, group differences were significant when the analysis was restricted to studies comprising class III obesity or higher. Meta-regression corroborated this finding showing inverse associations between the BMI of the obesity group or ΔBMI (obesity-controls) and the effect of obesity on DD2lR availability. Although we did not find significant changes in DD2lR availability following bariatric surgery, this finding should be cautioned given the small number of studies included. Overall, our results corroborate decreased DD2lR availability in severe obesity, although further studies are needed to determine the effect of bariatric surgery on dopaminergic physiology.
Previous literature supports our results of lower DD2lR availability in more severe forms of obesity. For instance, Van Galen et al.22 explained inconsistencies in these results through an inverted U-shaped association between DD2lR availability and BMI. Specifically, there would be an initial increase in DD2lR availability at moderately high BMI, particularly of high-affinity state D$\frac{2}{3}$ receptors, followed by a decrease of DD2lR availability in severe obesity22. Also, Horstmann A et al.24, suggested a non-linear (quadratic) association between changes in DD2lR availability and degree of obesity, to reconcile conflicting results. Similar non-linear associations have also been described for other reward-related measures and BMI, such as hedonic hunger and sensitivity to reward50,51.
Pre-clinical evidence corroborates decreased DD2lR availability in obesity. For example, mice with diet-induced obesity (DIO) had lower DD2R, but not DD1R binding relative to lean mice, associated with impaired physical activity52. Another study with rodent models of DIO53 showed decreased striatal DD2R availability and greater extracellular dopamine in obesity-prone (OP) compared with obesity-resistant (OR) rats53. While this study also showed lower expression of the presynaptic dopamine transporter (DAT) in OPs53, there seems to be no reliable association between DAT availability and obesity in clinical studies54–56.
The lack of group differences in the pooled analysis of PET and SPECT studies aligns with previous non-systematic reviews in this field12,22,24. Therefore, we decided to assess group differences, including both PET and SPECT studies, since they have comparable outcomes. However, since SPECT has a lower spatial resolution than PET, it was crucial to analyze the techniques separately and consider the effects of different radiopharmaceuticals. We found group differences in DD2lR availability in SPECT, but not PET studies, in which high BMI may have a greater influence on the results in the obesity groups than the techniques per se. It should be noted that, although we did not find significant associations between effect sizes of case–control differences and gender distribution in the obesity and the control groups, all [123I]IBZM SPECT studies excluded male participants. Despite differences in sensitivity and resolution in favor of PET57, SPECT is more widely available and the same radiopharmaceutical for DD2lR assessment has been usually used. On the other hand, there are several radiopharmaceuticals in PET, introducing variability. In the PET studies included in the meta-analysis, several used [11C]NMB PET28,29,47, all of which revealed unaltered DD2lR between groups. Contrary to radiopharmaceuticals used in the remaining studies, that also have affinity for D3 receptors, [11C]NMB selectively targets D2 receptors, and is not sensitive to competition from endogenous dopamine. A lack of group differences was also found in another PET study30 conducted with [18F]fallypride, suggested to have high affinity for D$\frac{2}{3}$R22. The same radiopharmaceutical showed increased DD2lR in the obesity group in a case–control study31 with a much smaller control group. The only study that used [11C]PHNO PET32, which is a D3-preferring DD2lR agonist tracer, also showed increased DD2lR in obesity relative to controls. In fact, it can be hypothesized that there could be fewer low-affinity state D2R and relatively more high-affinity states in obesity. Alternatively, there might be more striatal D3R12. However, considering the lower expression of D3R compared to D2Rs, it is unlikely that altered D3R availability is a determinant of group differences58. Indeed, some authors have suggested that the sensitivity of each radiopharmaceutical to receptor-binding competition by endogenous dopamine may contribute to group differences22,24. However, our results do not reveal particularly insightful differences in outcomes according to the use of both competition-sensitive27,30 and non-sensitive28,29,47 radiopharmaceuticals. Thus, lower DD2lR availability in obesity may reflect decreased availability of the receptor and/or increased endogenous synaptic dopamine levels resulting from more dopamine release12,22.
Research on altered endogenous synaptic dopamine levels in obesity remains inconclusive. Some have suggested that this question can be addressed with dopamine depletion studies using alpha-methyl-para-tyrosine to decrease dopamine synthesis59. Others have used a stimulus (e.g., food stimuli) or a pharmacological agent (e.g., dexamphetamine) to induce dopamine release and used the level of radiopharmaceutical displacement by endogenous dopamine as a proxy of dopamine release12. One study compared normal-weight controls with overweight or up to class II obesity patients using [11C]raclopride PET after a stimulus (intravenous—i.v. glucose injection) or placebo (i.v. saline NaCl $0.9\%$)60. Despite no differences between overweight and controls, there was a gender effect, with men showing decreased DD2lR binding following i.v. glucose injection (i.e., increased dopamine release), while women had the opposite result60. In addition, an [123I]IBZM SPECT study with a dexamphetamine challenge showed significant dopamine release in normal-weight controls but not in individuals with obesity, suggesting blunted dopamine release in the latter45. This study included, on average, class III obesity45, further supporting that altered striatal dopamine homeostasis may be more relevant in more severe forms of obesity.
With this study we also aimed to determine if DD2lR availability would change following bariatric surgery. However, the number of studies available was extremely small33,36,49, and patients were followed for a short time33,36. Two studies had a follow-up of six to seven weeks33,36, and only one study had a follow-up of at least two years49. In patients treated with gastric bypass surgery, the latter study found a significant increase in DD2lR availability using SPECT, which was not found in a shorter follow-up (six weeks) of the same cohort48. Unfortunately, such positive results have not yet been replicated, and further research is warranted to confirm or otherwise refute the effects of bariatric surgery on DD2lR availability. Also, despite one study33 including both gastric bypass ($$n = 4$$) and sleeve gastrectomy ($$n = 1$$), the number of patients in each group was insufficient to compare surgery types. Thus, the long-term assessment of DD2lR availability should address comparisons between gastric bypass and sleeve gastrectomy.
Results of meta-regression corroborate that altered dopaminergic physiology may be of particular relevance for more severe forms of obesity, emphasizing that within obesity there is biological and phenotypical heterogeneity. Beyond differences at the BMI level, obesity is inherently multifactorial, and can be differently expressed across individuals in terms of metabolism61 and psycho-biological variables such as reward-related taste sensitivity62,63, addiction-like feeding behaviors63,64, hedonic hunger50,64, eating style63, and reward sensitivity65. In the present study, the categorization of obesity solely based on BMI provided an insight on a subgroup that may be more vulnerable to changes in dopaminergic physiology, i.e., severe obesity. Among theories that have explained individuals differences in food reward sensitivity, the dynamic vulnerability model of obesity66 proposes that an initial heightened responsivity to palatable foods promotes overeating of these foods, but that upon repeated consumption of palatable foods dopamine signaling may decrease, reducing responsivity to palatable food cues over time66. This model thus assumes a baseline level of reward sensitivity, including to palatable foods, that is modulated by the level of dietary exposure to palatable foods over time66. In light of this model, we can hypothesize that individuals with severe obesity are more likely to present early biological changes that heighten reward sensitivity, which is then aggravated by a more frequent or longer exposure to consumption of palatable foods.
Although our findings suggest that central dopaminergic changes are more relevant for higher obesity classes, it is unlikely that obesity, and even severe obesity, can be translated into a single dopamine-related determinant. Within the dopamine system, nuclear medicine studies have typically targeted a single substrate, cross-sectionally, in small samples67. Nevertheless, this approach is not in line with the intricacy of obesity nor of dopaminergic physiology67. Hence, for personalized treatment of obesity, it will be fundamental to characterize dopamine profiles within obesity subtypes and determine their functional effects. Indeed, we propose that future PET/SPECT studies should aim to characterize and select participants in further detail, including metabolically and behaviorally. Additionally, multi-radiopharmaceutical studies to assess, for example, striatal receptor availability and dopamine synthesis capacity in the same individuals, ideally in larger samples with a wider BMI range, can provide useful information67. Despite the obvious gaps that exist in the field, the results reported here corroborate brain changes in obesity, that may contribute to behavioral factors such as poor compliance to diet and exercise. Indeed, if this is accurate, it is possible that pharmacologic options targeting the dopamine system may be useful for treatment of obesity as co-adjuvants of behavioral changes. Lastly, given that GLP-1 analogues may also have effects on human reward-related feeding behavior68, studies addressing the effects of GLP-1 analogues on central dopamine function may be an interesting avenue for future studies.
The interpretation of this study requires an understanding of its limitations. First, the weighted mean computed as a proxy for striatal DD2lR availability is not as accurate as a direct measure of striatal BP. Second, our meta-analyses included a relatively low number of studies and sample sizes. For example, the primary meta-analysis assessing DD2lR availability in obesity and control groups had 11 studies, close to the minimal number of recommended studies. While one study included more than 100 participants30, it did not have a pure case–control design but was instead a cohort with a wide BMI range, including an obesity group of $13.4\%$ of the total sample. Importantly, in the primary analysis, despite publication bias not being significant according to Egger's test, the funnel plot had some asymmetry. Specifically, there is a lack of large studies showing high negative effect estimates (i.e., lower DD2lR availability in obesity vs. controls). Indeed, several factors, including inclusion criteria or challenging methods for large-scale applications, can lead smaller studies to find more significant effects, resulting in asymmetry69. These challenges range from the cost and half-life of radiopharmaceuticals to scan time and exclusion criteria in nuclear medicine imaging. The primary meta-analysis also had substantial methodological heterogeneity, expected when pooling different scanner types and radiopharmaceuticals. The comparison between obesity and control groups in striatal DD2lR availability was sensitive to the exclusion of single studies, such as Guo J et al.31, the only study showing lower striatal DD2R availability in controls. While heterogeneity was an important challenge for our analyses, we note that, regarding our search protocol, we minimized database bias by using three reference databases and screening each chosen article reference list for articles missed by our syntax.
## Conclusions
This study was the first systematic review and meta-analysis to assess DD2lR availability in obesity, including PET and SPECT studies and its prospective assessment following bariatric surgery. DD2lR availability may not significantly differ between individuals with obesity and non-obesity controls across all obesity ranges. However, DD2lR availability seems to be lower in severe obesity. On the other hand, there does not appear to be normalization of DD2lR availability in the short term after bariatric surgery, which will require additional studies with longer follow-ups and comparisons between gastric bypass and sleeve gastrectomy. Overall, the analysis herein reported reinforces the importance of studying determinants of altered dopaminergic physiology in obesity and potential contributions to obesity phenotypes and variability in treatment response. This advance in knowledge will contribute to precision medicine in treating patients with obesity.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-31250-2.
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|
---
title: Arabic gum ameliorates systemic modulation in Alloxan monohydrate-induced diabetic
rats
authors:
- Rasha Mohammed Ibrahim
- Hemmat Mansour Abdelhafez
- Sawsan Abd EL-Maksoud EL-Shamy
- Fatma Ahmed Eid
- Alya Mashaal
journal: Scientific Reports
year: 2023
pmcid: PMC10042862
doi: 10.1038/s41598-023-31897-x
license: CC BY 4.0
---
# Arabic gum ameliorates systemic modulation in Alloxan monohydrate-induced diabetic rats
## Abstract
Medicinal plants are considered an alternative therapy for diabetes mellitus as they regulate glucose levels. Moreover, a variety of plants offer a rich source of bioactive compounds that have potent pharmacological effects without any negative side effects. The present study aimed to clarify the effects of Arabic gum/Gum Acacia (GA) on the biochemical, histopathological, and immunohistochemical changes observed in diabetic rats. Further, the anti-inflammatory activity of GA in response to diabetes, through inflammatory mediators analysis. Male rats were divided into four groups: untreated control, diabetic, Arabic gum-treated, and Arabic gum-treated diabetic rats. Diabetes was induced using alloxan. Animals were sacrificed after 7 and 21 days of treatment with Arabic gum. Body weight, blood and pancreas tissue samples were collected for analysis. Alloxan injection significantly decreased body weight, increased glucose levels, decreased insulin levels, and caused depletion of islets of Langerhans and β-cell damage in the pancreas. Arabic gum treatment of diabetic rats significantly increased body weight, decreased serum glucose levels, increased insulin levels, exerts anti-inflammatory effect, and improved the pancreas tissue structure. Arabic gum has beneficial pharmacological effects in diabetic rats; therefore, it might be employed as diabetic therapy to reduce the hyperglycemic damage and may be applicable for many autoimmune and inflammatory diseases treatment. Further, the new bioactive substances, such as medications made from plants, have larger safety margins, and can be used for a longer period of time.
## Introduction
Diabetes mellitus (DM) is a chronic disorder affecting carbohydrate metabolism and involving hyperglycemia, as a result of deficiencies in insulin release, action, or even both. These range from defects that lead to resistance to insulin action to autoimmune destruction of the pancreatic β-cells with subsequent insulin shortage. It can lead to a series of complications and consequently, to injuries in various tissues and poor quality of life1. DM is among the 10 most frequent causes of death worldwide. Therefore, DM poses a unique and significant threat to millions of people over the globe2. Epidemiological studies have shown that diabetes is the chronic disease with the highest incidence worldwide3. The global prevalence of diabetes is $2.8\%$ among all age groups and is predicted to increase annually to reach a value as high as $4.4\%$ in 20304. Type-1 DM (T1DM) is a chronic autoimmune illness categorized by the insidious and unremitting breakdown of β-cells5. This illness is caused by immune system dysfunction, which is mostly driven by T helper 1 (Th1) cells. Additionally, immune cells are activated and infiltrate the islets, resulting in the destruction of pancreatic β-cells and overt hyperglycemia6. Hyperglycemia produces reactive oxygen species (ROS) that induce cell damage through different pathways7, which leads to secondary complications of DM8. T1DM is characterized by the production of cytokines such interferon (IFN), tumor necrosis factor (TNF), and IL-1 by infiltrating immune cells. The transcription factors NF-B and STAT-1 are in charge of activating the gene networks in β-cells, which causes IL-1 and/or TNF plus IFN to directly damage β-cells and mediate apoptosis. Nitric oxide (NO) and chemokines are produced when NF-κB is activated, and endoplasmic reticulum calcium is depleted9.
The alloxan-induced diabetes model has been utilized by a number of authors as a "research tool" to clarify the pathophysiology of the disease, in research concerning diabetes, alloxan is a traditional and one of the most prevalent diabetogenic agents frequently used to evaluate the antidiabetic activity of both pure chemicals and plant extracts10. Alloxan is one of the drugs used to induce diabetes experimentally. Through its unique actions suppressing glucokinase activity and ROS generation, has a significant necrotizing effect on β-cells. Therefore, the reduction in the number of β-cells results in insulin deficiency followed by carbohydrate, protein, and fat metabolism dysfunctions11.
Despite recent advancements in diabetes care, various complications, including hypoglycemia, cardiovascular risks, increased risk of morbidity12, and gastrointestinal disorders13, are still associated with medications currently available. Natural products isolated from medicinal plants have been used to prevent and cure various diseases, such as cancers, DM, heart diseases, and high blood pressure14. Medicinal plants are a tremendous resource to produce medicines. Indeed, medicinal plants have low toxicity and excellent biological activities and economic viability15. Antidiabetic plants are used to treat diabetes by reducing the amount of glucose in the blood without inducing pain or other complications16. Therefore, despite the worrying statistics on diabetes from organizations such as the World Health Organization, International Diabetes Federation, and American Diabetes Association, with early detection and the right care, DM can be managed, and its complications avoided2.
Arabic gum/Gum Acacia (GA) is an edible, dried sticky exudate derived from Acacia seyal or Acacia senegal stems and branches and is rich in non-viscous soluble fibers17. GA consists primarily of two entities, i.e., oligosaccharides and arabinogalactan, which contribute to approximately $97\%$ of GA's total composition. Proteins represent less than $3\%$ of GA18. GA can act as a natural fiber to raise the levels of short-chain fatty acids, which have immunomodulatory effects to minimize inflammation and provide patients with a high quality of life19. Inflammation is a complex biological response mediated by many factors, such as inflammatory cytokines such as TNF-α and IL-10, the others exerting different effects20. GA has historically been used for chronic renal disease, stomach pain, and other disorders. Recently, several pharmacological and medical effects of GA have been evidenced including weight loss, antihyperlipidemic, antihypertensive, antibacterial, anticoagulant, antidiabetic, anti-inflammatory, and nephron-protective actions21.
## Experimental animal feeding and maintenance
All experiments were performed after approval of the protocol by the Research Ethics Committee of the Faculty of Medicine for Girls, Al-Azhar University (NO. 202009312), and the procedures were carried out in compliance with the National Institutes of Health's Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised 1996). The design of the experiment was compatible with the ARRIVE guidelines 2.0. A total of 48 adult male Sprague Dawley rats (120–160 g) aged 7 ± 1 weeks were used for the present study. The animals were obtained from the animal house of VACSERA (Cairo-Helwan, Egypt). The rats were housed and kept under regulated temperature, light and adequate ventilation for 2 weeks before the experimental work. The rats had access to food and water ad libitum and were fed a normal diet (El-Gomhouria Company, Cairo, Egypt).
## Diabetic model (induction of diabetes)
Alloxan was obtained from El-Gomhouria Company, Cairo, Egypt. After a fasting period of at least 8 h, DM was induced in male rats by the intraperitoneal injection of a single dose of alloxan monohydrate (150 mg/kg body weight), which was dissolved in normal saline22. Blood glucose levels were measured using a glucometer 48 h after alloxan injection, and rats with fasting blood glucose levels above 250 mg/dl were classified as diabetic23.
## Preparation of GA solution
GA was obtained from El-Gomhouria Company, Cairo, Egypt as a fine powder. GA aqueous solution was prepared freshly each day as described by Gamal El-din et al.24 and given orally through a gastric tube at a dosage of 25 gm/kg/day. The dose used in rats was estimated based on the human dose using Paget's formula25.
## Experimental design
The rats were randomly and equally distributed into four groups (12 rats in each group) as follows: group 1 contained healthy untreated control rats (C), group 2 diabetic untreated rats (diabetes induced using alloxan and maintained for 21 days) (D), group 3 consisted of rats treated with GA orally (25 mg/kg body weight/day) for 21 days (G), and group 4 contained diabetic rats treated with 25 mg/kg body weight GA per day for 21 days (D + G). The experimental rats (6 rats/group) were sacrificed after 7 and 21 days.
## Blood collection and tissue sampling
After the animals of all groups were anesthetized using isoflurane, blood was collected directly from the retro-orbital plexus26. Blood samples were left to coagulate at room temperature and centrifuged at 3000 rpm for 10 min to obtain a clear, non-hemolyzed serum for biochemical analysis. The pancreas was quickly dissected, and pancreas samples were prepared for various histological and immunohistochemical analyses.
## Body weight measurement
The body weight of control and treated rats was measured at initiation and different time intervals of the experiment on days 7 and 21 of treatment.
## Biochemical analyses
Fasting serum glucose was estimated using the enzymatic colorimetric method described by Tietz27, whereas serum insulin levels were assayed according to the method of Reeves28 using kits of Bio Source Europe S.A. Company.
## Cytokine assays
Sandwich enzyme-linked immunosorbent assay (ELISA) was used to detect tumor necrosis factor-α (Abcam, ab46070) and interleukin-10 (Abcam, ab214566) in serum. The optical density was read on a microtiter plate reader with 450 nm (ELX-808, BioTek Instruments, Winooski, VT, USA).
## Histopathological and immunohistochemical analyses
On days 7 and 21, animals from the control and treated groups were sacrificed. The pancreas tissues were quickly removed, fixed for 24 h in $10\%$ neutral formalin, dehydrated, cleared in xylene, and embedded in paraffin wax. 5 μm tissues thickness are stained with hematoxylin and eosin for general histological structure. Mallory’s trichrome staining was used to detect collagen fibers29 and standard immunohistochemical methods described by Eissa and Shoman30 were applied to detect insulin markers.
## Statistical analysis
The data were processed and analyzed using the SPSS program (Statistical Analyses for Social Science, Version 8). Analysis of variance was employed according to Snedecor and Cochran’s method31. Student's t-test was used to assess significant differences between treatment means. Data are presented as means ± standard deviations, and P ≤ 0.05 was considered statistically significant.
## Body weight
The diabetic group exhibited a significant decrease in body weight in the first week and a very highly significant decrease after 21 days, which valued 124.6 ± 8.17 and 116.2 ± 7.66 after 7 and 21 days respectively compared to the findings in the control group (134 ± 7.55–144.6 ± 7.09).
Oral administration of GA alone showed non-significant decrease in the mean values of body weight (133.4 ± 9.20 and 143.2 ± 8.76) on 7 and 21 days post-treatment in comparison with the control group.
Moreover, the diabetic rats which were treated with GA were normalized (131.6 ± 4.45–141 ± 6.08) when compared to diabetic rats (Fig. 1).Figure 1The average of body weight associated with *Arabic gum* and diabetes-induced rats. Each value is presented as the mean ± standard deviation (SD), $$n = 6$$ rats. * Significant difference vs the control group at $P \leq 0.05$, **highly significant difference vs the control group at $P \leq 0.01.$ C control group, D diabetic group, G Arabic Gum group, D + G diabetic-Arabic Gum treated group.
## Glucose and insulin levels
The present results showed very highly significant increases ($P \leq 0.001$) in the mean values of serum glucose levels of the diabetic group which reached 254.71 ± 7.43 and 265.33 ± 6.92 mg/dl on 7 and 21 days post-treatment respectively as compared to the untreated control group (88.60 ± 2.99 and 88.61 ± 2.98). Accompanied by very highly significant decreases ($P \leq 0.001$) in the mean values of serum insulin levels such decreases were 1.63 ± 0.13 and 1.40 ± 0.27 U/ml after 7 and 21 day as compared to the control group (2.64 ± 0.35 and 2.63 ± 0.34). Oral administration of GA alone showed non-significant changes ($P \leq 0.05$) in the mean values of serum glucose (88.17 ± 1.79 and 87.87 ± 1.05) and insulin (2.60 ± 0.22 and 2.71 ± 0.22) levels on 7 and 21 days post-treatment in comparison with the control group. Additionally, treatment with GA post-alloxan injection significantly decreased serum glucose levels (92.18 ± 2.58 and 89.70 ± 4.52) on the 7 and 21 day post-treatment respectively) and it significantly increased insulin levels (2.49 ± 0.21 and 2.59 ± 0.22 U/ml at 7 and 21 days post-treatment respectively) as compared with the diabetic groups (Fig. 2A,B).Figure 2Circulating glucose, insulin, tumor necrosis factor-alpha, and interleukin-10 concentrations associated with *Arabic gum* and diabetes-induced rats. Each value is presented as the mean ± standard deviation (SD), $$n = 6$$ rats. ( A) Glucose mean values. ( B) Insulin mean values. ( C) *Tumor necrosis* factor-alpha (TNF-α) mean values. ( D) Interleukin-10 (IL10) mean values. C control group, D diabetic group, GA Arabic Gum group, D + G Diabetic-Arabic Gum treated group, TNF-α tumor necrosis factor-alpha, IL-10 interleukin-10. ** Highly significant difference vs the control group at $P \leq 0.01.$ *** very significant difference from the mean of the control group at $P \leq 0.001.$ Superscripts indicate significance ($P \leq 0.05$) where: adenotes significance vs. control group; bdenotes significance vs. diabetic group.
## Systemic inflammation
The effect of GA treatment on diabetic/non-diabetic rats with circulating inflammatory and anti- inflammatory markers expressed by TNF-α and IL-10 cytokines was shown in Fig. 2. TNF-α concentration of Arabic gum-diabetic group (32 ± 0.84 and 29 ± 0.82) was significantly decreased compared to diabetic group (40 ± 0.63 and 39 ± 0.89) at time intervals 7 and 21 days respectively, in contrast to its expression compared to control (27 ± 0.63 and 27 ± 0.62) which significantly increased. IL-10 was significantly increased (900 ± 1.41 and 870 ± 1.09) compared to both control (850 ± 1.26 and 850 ± 1.16) and diabetic groups (700 ± 1.27 and 680 ± 2.09) at experiment durations respectively (Fig. 2C,D).
## Histological analysis
The pancreas tissues were stained with H&E and Mallory’s trichrome stains to assess the effect of GA on pancreas damage induced by alloxan injection. The control group displays a normal appearance of islets of Langerhans and the pancreatic acini with their triangular cells which contained basal rounded nuclei (Fig. 3A) with normal distribution of collagen fibres around islets of Langerhans and the pancreatic acini (Fig. 4A).Figure 3Pancreatic tissue sections from control and treated rats stained with hematoxylin and eosin. ( A) Normal appearance of islets of Langerhans (arrow) and pancreatic acini (pa) in the control group. ( B,C) Representative pancreas sections from diabetic rats showing hypertrophied nuclei of tunica intima (ti), faintly stained nuclei of tunica media (tm), distorted connective tissue of tunica adventitia (ta), hemolyzed blood cells (arrow), edematous area (star), hemorrhagic area (h), and atrophied islet of Langerhans (arrow) and degenerative changes in the nuclei of the acini like absence of some of them (black curved arrow) 7 days after alloxan injection. ( D,E) Representative pancreas sections from diabetic rats showing distorted islets of Langerhans (arrows), interlobular ducts (stars) containing hemolyzed blood cells 21 days after alloxan injection. ( F,G) Representative pancreas sections from rats treated with *Arabic gum* (GA) showing normal islets of Langerhans (arrows) and pancreatic acini (Pa) after 7 (F) and 21 days (G) of treatment. ( H,I) Representative pancreas sections from rats treated with alloxan and GA showing an improved architecture of islets of Langerhans (arrows). Magnification: ×400 in (A,B,F,G,I) and ×250 in (C,D,E,H).Figure 4Pancreatic tissue sections from control and treated rats after Mallory's trichrome staining. ( A) Representative control section showing normal distribution of collagen fibers (arrow). ( B,C) Pancreatic tissue sections from diabetic rats showing highly increased amounts of collagen fibers in the artery (a), vein (v) and interlobular duct (star) and atrophied islets of Langerhans (arrow) 7 days after alloxan treatment. ( D,E) Pancreatic tissue sections from diabetic rats showing higher numbers of collagen fibers in the interlobular duct (star), islets of Langerhans (arrow), the artery (a), and the vein (v) 21 days after alloxan treatment. ( F,G) Pancreatic tissue sections from rats treated with *Arabic gum* (GA) showing thin collagen fibers (arrows) after 7 and 21 days of treatment. ( H,I) Pancreatic tissue sections from diabetic rats treated with GA (D + G group) showing a slightly increased number of collagen fibers (arrows) after 7 and 21 days of treatment, respectively. Magnification ×250 in (B–D), ×200 in (E), and ×400 in (A,F,G,H).
The histological examination of the pancreas tissues showed that alloxan injection after 7 days caused a highly thickened arterial wall with hypertrophied nuclei of endothelial lining of tunica intima, faintly stained nuclei of muscle cells of tunica media and highly distorted connective tissue of tunica adventitia, hemolyzed blood cells inside the artery in addition to the presence of edematous areas around the pancreatic acini, also degenerative changes in the nuclei of the acini were detected such as absence some of them (Fig. 3B), further, highly elongated and ruptured wall of the venule which contained hemolyzed blood cells inside it with large hemorrhagic areas were also detected at 7 days post alloxan treatment (Fig. 3C). Concerning the distribution of collagen fibres in pancreatic tissue of diabetic group after 7 days, highly increased collagen fibres were detected in and around walls of arteries, veins, interlobular duct also in between the acini, around the highly atrophied islets of Langerhans and around the pancreatic tissue (Fig. 4B,C).
By the end of 21 days following alloxan injection, highly atrophied and distorted islets of Langerhans were detected, most of the different cells inside it disappeared, but a few pyknotic nuclei were detected, interlobular ducts contained hemolyzed blood cells inside them with a highly stratified wall of their cuboidal cells (Fig. 3D,E). Also, highly increased collagen fibres were observed in and around the interlobular duct around the islets of Langerhans, in between the pancreatic acini, in walls of the artery, vein and around them (Fig. 4D,E).
Rats administrated GA alone showed normal histological appearance of the pancreatic tissue after seven (Fig. 3F) and 21 days (Fig. 3G) with thin collagen fibres supported islets of Langerhans, interlobular duct and in between the pancreatic acini after seven (Fig. 4F) and 21 days (Fig. 4G) post-treatment.
Pancreas sections of rats administrated GA for 21 days post-alloxan injection and examined after 7 and 21 days showed improvement architecture of islets of Langerhans and the pancreatic acini after 7 days (Fig. 3H) as compared to the diabetic group with slightly increased collagen fibres deposition in between the exocrine acini and around the islets of Langerhans (Fig. 4H) as compared to the control group. As shown in Fig. 3I islets of Langerhans and exocrine acini resumed their normal structure after 21 days with thin scattered collagen fibres supported walls of the artery and vein, around the islets of Langerhans and in between the pancreatic acini (Fig. 4I).
## The immunohistochemical results
Immunohistochemical examination of non-diabetic non-treated rats for detection of insulin marker revealed active positive cytoplasmic stain ability of almost beta cells of islets of Langerhans. Alpha and delta cells were negatively stained (Fig. 5A,B).Figure 5Detection of insulin in the pancreatic tissues of the control and diabetic groups. ( A,B) Insulin-positive cytoplasmic staining in β-cells of the islets of Langerhans (yellow and blue stars). In contrast, α-(red star) and δ-cells (white arrow) were not stained. Acini of the exocrine pancreas were visible (black arrow). ( C,D) Strong insulin-positive staining of islet β-cells (red and blue arrows) 7 days after alloxan injection. The remaining cells were not stained (yellow and black arrowheads). ( E,F) Very intense insulin-positive staining of islet β-cells (blue arrows) 21 days after alloxan treatment. The remaining cells were not stained (red arrows). Scale bars 40 µm in (A) and 20 µm in (B–F).
The diabetic rat’s pancreas on 7 days post-alloxan-injection showed 10–$35\%$ high power field (HPF) positively reacted and stained cells with insulin diabetic marker (some sections showed weak staining reactivity), the remaining cells 65–$90\%$ HPF were negatively stained. Other islet cells were also unstainable (Fig. 5C,D). Meanwhile, 21 days post-alloxan injection diabetic rat’s pancreas revealed 20–$45\%$ HPF positively reacted and stained cells with insulin diabetic marker, the remaining cells 55–$80\%$ HPF were negatively stained (Fig. 5E,F).
Most of the beta cells of the islets of Langerhans (95–$100\%$ HPF) of *Arabic gum* treated groups were positively stained for the insulin marker in most of the examined sections. Very few cells, mostly of Alpha type were unstained after 7 (Fig. 6A) and 21 days (Fig. 6B).Figure 6Detection of insulin in the pancreatic tissues of Arabic gum-treated and Arabic gum/diabetic rats. ( A,B) Positive insulin staining of most β-cells (yellow stars) after 7 and 21 days of treatment with Arabic gum. The α-cells (red stars) and acini of the exocrine pancreas (black arrow) were not stained. ( C,D) Moderate immunostaining of β-cells with brown intracytoplasmic granules (yellow stars), whereas α-cells were not stained (red stars) in diabetic rats treated with *Arabic gum* (D + G group) for 7 days. ( E,F) Most of the islet β-cells were positively stained in diabetic rats treated with *Arabic gum* for 21 days. Scale bars 40 µm in (A,C,E) and 20 µm in (B,D,F).
Sections from the pancreas of alloxan-induced diabetic rats treated with *Arabic gum* 7 days post-alloxan-injection showed partial enhancement of beta cells for insulin marker staining reactivity as the positively stained cells were ranged from 60 to $70\%$ HPF. Most of the cells were intensely stained with the presence of characteristic brown intra cytoplasmic granules (Fig. 6C,D). Meanwhile, sections from the pancreas of alloxan-induced diabetic rats treated with *Arabic gum* 21-day post—alloxan injection showed complete enhancement of beta cells for insulin marker staining reactivity as the positively stained cells were ranged from 95 to $100\%$ HPF, most of the cells were intensely stained with the presence of characteristic brown intracytoplasmic granules (Fig. 6E,F).
## Discussion
Insufficient insulin frequently caused fast, considerable weight loss in diabetes mellitus patients. As a result, glucose provides the body with a very insufficient amount of energy, causing it to burn through its fat and muscle reserves and cause weight loss32. Administration of GA in this study improved the weight gain against alloxan diabetic rats. High dietary fibre intake, including GA, is linked to favorable effects on fat metabolism. Dietary fibre enhances feelings of fullness and satiety, modifies the glycemic index, influences stomach emptying and gut hormone release, and hence aids in weight management33.
The present results showed an elevation in glucose serum concentration and a reduction in insulin levels of diabetic rats compared to untreated group. The severe hyperglycemia in diabetic rats reported in the present study may be a direct consequence of the hypoinsulinemia induced by the specific cytotoxicity of alloxan toward β-cells resulting in β-cell vacuolation. Alloxan directly affects the permeability of the cell membrane by triggering ionic pump failure and increasing cell size, thus, inhibiting intracellular energy generation. This may cause a reduction in ATP levels, an inhibition of insulin production, and cell necrosis34.
Lenzen reported that alloxan inhibits glucose release induced by insulin by specifically inhibiting glucokinase activity and triggers an insulin-dependent diabetes condition by stimulating the generation of ROS through a cyclic reaction with its reduction product, dialuric acid13. Islet cells exposed to alloxan produce substantially higher levels of peroxynitrite, nitric oxide, and ROS and exhibit significantly increased lipid peroxidation, decreased cell viability, and increased mitochondrial membrane potentials. Alloxan induces extreme oxidative and cytotoxic stress in islets, likely resulting in an impaired cell ability to release insulin35.
In the current study, GA exerts anti-inflammatory effect similarly reported by Ali et al.36. Inflammation and oxidative stress mediate various pathophysiological features in many diseases37, factors that can control and reduce these features may be effective as prophylactic or treated agent of these diseases. The balance between TNF-α and the IL-10 incidence play an important role in keeping the inflammation homeostasis38. Triggering of these cytokines leads to inflammation, resulting in the production of even more reactive oxygen species, which can contribute to even more oxidative stress, generating a feedback loop39. The ability of GA to reduce inflammatory cytokines and oxidative stress mediators, which are markedly diminished by its administration, may be one of the ways by which it controls diabetes, these finding are in parallel to those of Ali et al.40.
In the present study, the mean serum levels of glucose and insulin of GA alone or GA-diabetic group were not significantly different from the control group. The improvement of serum glucose levels in diabetic rats treated with GA is supported by the results of Babio et al. who reported important physiological benefits of dietary fibers, which decrease postprandial glucose and lower cholesterol plasma concentrations41. In addition, dietary fibers were shown to have potential hypoglycemic effects by decreasing blood glucose levels in a diabetic rabbit model42.
The present data also agree with the results from Hegazy et al. who reported that oral administration of two distinct doses (100 and 200 mg/kg) of *Acacia arabica* extract to diabetic rats for 21 days decreased the elevation in serum glucose and insulin, diminished insulin resistance, and enhanced lipid metabolism43. The same authors added that all these results were secondary to the elevated levels of insulin. Another work also showed that food supplementation with GA (10 g/day for 16 weeks) in prediabetic and diabetic subjects significantly reduces fasting blood sugar and glycated hemoglobin levels44. Finally, the present results are also supported by the study by Al Zaabi et al. who reported that GA in drinking water ($15\%$ w/w) prevents, at least partly, streptozotocin's effect by stimulating insulin release and increasing insulin resistance45.
The pancreas of the diabetic rats contained highly atrophied islets of Langerhans and exhibited a hypocellularity affecting the different cell types. The present findings are supported by studies conducted by El-Esawy et al. who reported a degeneration, atrophy, and vacuolization in the islets of Langerhans and β-cells, a reduced number of β-cells, and loss of normal islet structure in diabetic animals46. Experimentally, alloxan causes major injuries to pancreatic β-cells by producing ROS, leading to the induction of diabetes in animals47. Various mechanisms have been suggested to explain islet cell injuries, especially β-cell damage, in T2DM. These include increased oxidative stress, enhanced metabolic stress, increased endoplasmic reticulum stress, activation of inflammatory pathways, and islet amyloid polypeptide toxic accumulation. In all cases, the result is a β-cell dedifferentiation and apoptosis48.
Tangvarasittichai observed that hyperglycemia is the cause of biochemical processes such as oxidative stress, low-grade inflammation, and apoptosis49, and that these biochemical changes precipitated the development of insulin resistance and the degradation of β-cells. Additionally, the average area occupied by insulin immunoreactivity in immunohistochemical experiments is greatly diminished in diabetic animal models50. Moreover, inflammatory and apoptotic pathways lead to β-cell damage in a rat model of T2DM induced by high-fat diet and STZ as confirmed by inducible nitric oxides and caspase-3 immunohistochemistry.
The pancreatic tissues of the rats in the current investigation that received GA were normal; GA is a recognized safe direct food additive and has no known genotoxic effects51. Furthermore, developmental toxicity has not been associated with intraperitoneal or oral administration of GA52, and no carcinogenic effects of GA were reported53. Although GA is indigestible to both humans and animals, it is fermented in the colon to produce short-chain fatty acids, which have a wide range of potential health advantages54.
We observed signs of improvements in histopathological modifications and the state of the islets of Langerhans and pancreatic acini observed in diabetic animals post GA treatment. These data agree with those described previously by Eliza et al. who proposed that A. arabica extracts have an insulin-like action by increasing glucose absorption into the muscle and adipose tissues and by inhibiting hepatic gluconeogenesis55. It was also suggested that A. arabica exerts its hypoglycemic effect by activating insulin receptors56. Yasir et al. showed that, although there are other mechanisms, A. arabica main mechanism of action occurs through the revitalization and maybe the regeneration of the damaged β-cells as suggested by the increased serum levels of insulin in diabetic rats treated with A. arabica extract57. This mechanism can be explained by the antioxidant properties of A. arabica extracts, which are evidenced by a decreased malondialdehyde concentration and increased coenzyme-Q10 levels58. GA can act as a hypoglycemic agent by causing the production of insulin from pancreatic β-cells in healthy rabbits59. Plants have hypoglycemic, antihyperlipidemic, and antioxidant actions because they are important sources of flavonoids, gallotannins, amino acids, and other related polyphenols60. Many components, including polyphenols, tannins, and flavonoids (such as quercetin), are found in A. arabica. Tannins were shown to improve the function of pancreatic β-cells and promote insulin secretion61. Quercetin is an antioxidant that functions through several pathways, including the scavenging of oxygen radicals; therefore, it protects against lipid peroxidation and chelation of metal ions58. These antioxidant substances may underlie the antidiabetic effects of A. arabica62.
## Conclusion
Arabic gum has effective pharmacological role in diabetic rats’ recovery; in addition, GA administration modulate pro- and anti-inflammatory pathways and exert protective effect. Tackling all findings together, GA might be employed as diabetic therapy to reduce inflammation and hyperglycemic damage. Our manuscript creates a paradigm for future studies to investigate gene expression/inflammatory cells markers that modulate and regulate the mechanism action and relationship between GA and different cells reaction, to obtain optimized therapeutical effect of GA.
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|
---
title: A comprehensive molecular profiling approach reveals metabolic alterations
that steer bone tissue regeneration
authors:
- Julia Löffler
- Anne Noom
- Agnes Ellinghaus
- Anke Dienelt
- Stefan Kempa
- Georg N. Duda
journal: Communications Biology
year: 2023
pmcid: PMC10042875
doi: 10.1038/s42003-023-04652-1
license: CC BY 4.0
---
# A comprehensive molecular profiling approach reveals metabolic alterations that steer bone tissue regeneration
## Abstract
Bone regeneration after fracture is a complex process with high and dynamic energy demands. The impact of metabolism on bone healing progression and outcome, however, is so far understudied. Our comprehensive molecular profiling reveals that central metabolic pathways, such as glycolysis and the citric acid cycle, are differentially activated between rats with successful or compromised bone regeneration (young versus aged female Sprague-Dawley rats) early in the inflammatory phase of bone healing. We also found that the citric acid cycle intermediate succinate mediates individual cellular responses and plays a central role in successful bone healing. Succinate induces IL-1β in macrophages, enhances vessel formation, increases mesenchymal stromal cell migration, and potentiates osteogenic differentiation and matrix formation in vitro. Taken together, metabolites—here particularly succinate—are shown to play central roles as signaling molecules during the onset of healing and in steering bone tissue regeneration.
Metabolites are demonstrated to be critical regulators of fracture healing. Succinate is found to promote vascularization and ossification, highlighting that metabolites have the potential to be used therapeutically to promote fracture healing.
## Introduction
In contrast to most tissues, bone has the unique capacity of scar-free healing after injury or fracture. Bone regeneration consists of well-balanced cellular and molecular pathways and cascades essential to re-establish tissue integrity and function. However, fracture non-unions, which occur in 10–$15\%$ of patients, put a significant burden on the individual patient and the health care systems1,2. Understanding of the underlying pathological mechanisms leading to delayed healing progression or non-union is key to improve treatment or even preventive care options.
Successful bone regeneration, results from a fine-balanced interplay of anabolic and catabolic processes, including inflammatory and angiogenic signaling, matrix formation and tissue remodeling3. These processes steering callus formation and fracture repair are driven by immune cells, endothelial cells, fibroblasts and stromal cells, all of which arrive early after injury in the fracture hematoma3–5. Beside immune cell functionality such as specific cytokine production also cell activation and proliferation are driven by metabolic pathways, which provide e.g., lipids as membrane building blocks and nucleotides for DNA replication6–8. This concept also applies to fibroblast or mesenchymal stromal cell proliferation and differentiation, which are critical processes during fracture repair9,10. It is without question that sufficient nutritional supply is central to effective healing, although the impact of this highly dynamic and time sensitive energetic demand has been only scarcely considered in regenerative research so far11.
Considering the dynamic conditions during bone repair, comorbidities, such as diabetes, hormonal disorders and advanced age put additional stress on the already challenging metabolic setting and can impair bone healing12,13. Locally altered or limited nutrient availability may cause metabolic shortcomings in the callus tissue and result in different cell functionality and phenotypes—a concept long known for tumor cell transformation14,15.
Since metabolic intermediates are not only relevant as biosynthetic precursors but can further act as ligands of specific receptors (e.g., G-Protein-coupled receptors)16,17, cells engaging altered metabolic profiles can subsequently lead to changes in their communication. Fine-tuned communication and collaboration are the basis of successful re-organization and regeneration of an organ. Variation in this fine-tuned communicative interplay may consequence in disturbances or failure of bone regeneration and result in scar tissue formation, as frequently found in impaired revascularization or under prolonged or excessive inflammation18.
So far, little attention has been paid to the local metabolic environment following a bone fracture incident. We hypothesize that the local metabolic environment is an essential driver of efficient bone tissue repair and metabolic alterations, especially during early bone healing. To prove this hypothesis, we used a novel angle by analyzing metabolic profiles and potential communication signals of local bone regeneration. To allow identification of differentiating signals between successful and compromised bone regeneration, we chose to pursue a comparative approach in early bone healing using two distinct experimental groups. This was done by utilizing a well-characterized animal model of successful versus biologically compromised fracture healing in female Sprague Dawley rats19–21.
## Biologically compromised bone healing shows altered metabolic and immune responses at protein level compared to successful healing
We employed a model of biologically compromised bone healing that manifests retarded bone formation in aged female Sprague-Dawley rats compared to young animals after receiving a 2 mm femoral osteotomy, as published19–25 (Fig. 1 a, b, Supplementary Data 1). This model allows for the comparison of successful endogenous bone healing (young rats) to biologically compromised healing (aged ex-breeder rats, with a minimum litter of three), without any intervention to manipulate healing outcome. We started by validating the applicability of our model for early fracture healing time points (day 3, day 7, day 14) by histological, and gene expression analysis (Fig. 1c). Analysis confirmed progressed healing in young animals by faster callus progression (Fig. 2a, b) and higher levels of tissue mineralization (Fig. 2c, d, Supplementary Data 2). Lastly, fracture callus tissue from young rats showed increased expression levels of the osteogenic genes collagen type I alpha 2 chain (Col1a2) at day 3 and 7 and secreted phosphoprotein 1(Spp1) (Fig. 2e, Supplementary Data 2) at day 14.Fig. 1Schematic overview of applied in vivo fracture healing model and experimental workflow.a Applied surgical procedure introducing a 2 mm standardized osteotomy gap in the left femur of young rats, aged 3 months, showing normal bone healing are compared to aged rats (12 months) that had a minimum litter of three, showing biologically compromised bone healing. b Newly formed bone has different properties in successful healing compared to compromised healing. Successful healing showed higher bone volume ratio and trabecular thickness, while bone volume did not change in comparison to compromised healing after 6 weeks. $$n = 9$$–10 individual biological replicates, Mann-Whitney U test19,21,22 Central image: external fixator placed on the left femur, right image: dissected femur, where the region of interest (ROI: 2 mm hematoma + 1 mm proximal and distal to fracture gap) has been removed. All bones were placed in the same orientation for histological analysis, with the femur head (proximal) on the left side and the knee condyle (distal) on the right side of the image. BV - bone volume, TV- total volume, Tb – trabecular. Rat icon obtained from: Flaticon.com, artist: Nhor Phai. c Samples were harvested at day 3, 7 and 14 after osteotomy and subjected to the different downstream analysis, ROI – region of interest. Fig. 2Validation of the in vivo model suitability for the early healing time-points: day 3, day 7, and day 14 by histological assessment and gene expression analysis of osteogenic markers.a Tissue distribution (mineralized tissue, blot clot tissue) evaluated by Movat-Pentachrome staining. Day 3, 7, and 14 after osteotomy shown from left to right showing successful healing. Femurs are placed in the same orientation, with the proximal part on the left side and the distal part on the right side of the image. Black boxes indicate the magnified area displayed in Fig. 2c. b The panel shows an example for compromised healing, days 3, 7, and 14 after osteotomy arranged from left to right. Femurs are placed in the same orientation, with the proximal part on the left side and the distal part on the right side of the image. Black boxes indicate the magnified area displayed in Fig. 2c. c Magnification of light microscopic images from Figs. 2a and b. Arrows indicate newly mineralized tissue. Movat-Pentachrome staining: yellow color = mineralized tissue, red/brown = blood clot. Proximal part on the left side and the distal part on the right side of the image. d Tissue quantification of Movat-Pentachrome-stainings showed higher tissue mineralization in successful healing conditions at day d14. Compromised healing showed longer presence from initial blood clot tissue. $$n = 6$$ individual biological replicates per group and time point analyzed, One-way ANOVA. e Osteogenic factor, Col1a2 (collagen type I alpha 2 chain) showed significantly higher expression at day 3 and day 7 and Spp1 (osteopontin) at day 14 after osteotomy in successful healing, relative expression to Tbp (TATA-binding protein, housekeeping gene) and d3 young, $$n = 3$$–6 individual biological replicates per group and time point, One-way ANOVA. Mean±Standard deviation shown for all graphs.
To validate our hypothesis of the local metabolic environment being a driver of effective bone tissue repair, we performed untargeted proteomic screening (LC-MS/MS) of fracture hematoma/callus tissue sampled at days 3, 7, and 14 after femoral osteotomy from successful healing and biologically compromised healing, respectively.
To quantify protein levels using untargeted LC-MS proteomics, homogenized whole hematoma /callus tissue was used, as described in the method section under paragraph 4.3 “Animal sacrifice and sample harvest”. The schematic workflow on sampling and down-stream analysis is depicted in Fig. 1c.
To identify up- and down- regulated proteins between groups of successful and compromised bone healing groups, fold changes of normalized label-free quantities (LFQ) intensities were calculated (compromised healing: successful healing). Subsequently, gene ontology (GO) term and KEGG pathway enrichment analyses were performed to identify differentially regulated proteins and protein cluster between successful and compromised healing. The strongest resulting regulation was seen in KEGG pathways associated with extracellular matrix and migration, metabolism, and immune responses (Fig. 3a; Supplementary Data 3, 16, 17). In support of our hypothesis, regulation of metabolic proteins between successful and compromised bone healing was among the most differentially regulated protein cluster. In particular, clusters related to sugar metabolism, amino acid metabolism and oxidative metabolism were decreased in the group of compromised healing (Fig. 3a; Supplementary Data 3; 16). By contrast, proteins related to immune response and secondary metabolic pathways were increased in compromised bone healing when compared to successful healing (Fig. 3b; Supplementary Data 3; 17). The results on protein level were indicative of a prolonged inflammatory phase in animals showing compromised healing, complementing the histological findings of an extended initial blood clot presence in the osteotomy gap (Fig. 2c).Fig. 3GO and KEGG enrichment analyses of the fracture proteome reveal distinct differences in metabolic-, immune- and matrix-related processes between successful and compromised bone healing in rats.a Down-regulated pathways and processes in compromised bone healing tissue are related to amino acid and carbohydrate metabolism and extracellular matrix at day 3 and day 7, while oxidative metabolism processes are downregulated at day 14. Cell contraction and calcium signaling proteins are downregulated at day 3 and day 14 in particular. Total protein number (n) as identified by GO and KEGG enrichment analyses are indicated for each timepoint. b Protein cluster associated with immune responses and extracellular matrix—receptor interactions have been found up-regulated in compromised healing for all time points, while cell cycle proteins were upregulated at day 3 and 7 and lysosomal proteins increased at day 3 and day 14. Total protein number (n) as identified by GO and KEGG enrichment analyses are indicated for each time-point.
By proceeding with a detailed analysis of the identified protein cluster, we found inflammatory and innate immunity-related proteins, like cathepsin G (CTSG), complement component 6 (C6), or fibrinogen gamma chain (FGG) particularly up-regulated in compromised bone healing tissue shortly after osteotomy (from day 3 onwards), lasting until day 14 (e.g., cathepsin A (CTSA), lysosome-associated membrane protein 2 (LAMP2), Fig. 3c). Levels of glucose and TCA cycle metabolism-associated proteins (e.g., phosphofructokinase (PFKM), isocitrate dehydrogenase 1 (IDH1)) started to increase in samples showing successful healing at day 7 and were highly increased at day 14 (e.g., isocitrate dehydrogenase 2 (IDH2), citrate synthase (CS), Fig. 4a, Supplementary Data 4). The most pronounced differences between successful and compromised bone healing were detected in proteins related to oxidative metabolism/phosphorylation (OXPHOS) and highly upregulated in successful healing at day 14 after osteotomy e.g., NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 9 (NDUF9b), ubiquinol-cytochrome c reductase, Rieske iron-sulfur polypeptide 1 (UQCRSF1, Supplementary Fig 1; Supplementary Data 11).Fig. 4Expression of selected metabolic and inflammatory genes from fracture tissue validates proteomic findings of altered metabolism and inflammation between successful and compromised bone healing.a Heatmap of proteins related to the immune response and metabolism highlighting specific profiles for successful and compromised healing. Day 3, in particular, showed increased levels of immune and inflammatory proteins in tissue of compromised healing fractures. Proteins from metabolic pathways showed an increasing trend in successful healing fracture tissue at day 7, and a strong upregulation compared to compromised healing at day 14. Shown are sum over mean normalized LFQ intensities, with following color coding, red= upregulated, white= unchanged between conditions, blue= downregulated, grey= no values. $$n = 3$$–5 biological replicates (BR) per group and time-point. b Gene expression analysis of selected inflammatory and metabolic markers validate findings from protein levels. Pro-inflammatory genes like Ctgs, Tnf-alpha or Nos2, showed significant higher expression in compromised healing samples. The anti-inflammatory gene Il-10 and enzymes from central carbon metabolism (Hk2, Sdhb) showed higher expression in samples from successful healing. Relative expression to Tbp (TATA-binding protein, housekeeping gene) and d3 young, $$n = 3$$–6 individual biological replicates per group and time point, One-way ANOVA, mean±standard deviation.
Gene expression analysis of inflammatory markers and metabolic enzymes were performed to validate findings on protein level. Expression of pro-inflammatory genes like the tumor necrosis factor-alpha (TNF-alpha) or nitric oxide synthase 2 (Nos2) was high in compromised healing samples at day 3. In contrast, expression of the anti-inflammatory cytokine interleukin-10 (Il-10) was higher in fracture samples from successful healing on days 3 and 7. Similarly, central carbon metabolic enzymes (hexokinase 2, Hk2, and succinate dehydrogenase beta, Sdhb) showed higher expression levels in successful healing samples during early healing time points, namely day 3 and day 7 (Fig. 4b, Supplementary Data 4).
## Checkpoints of central carbon metabolism show alterations between successful and compromised healing after 7 days
To validate whether the observed regulations of metabolic pathways proteins are related to an altered metabolism, the second part of the homogenized hematoma/callus tissue specimen was subjected to GC-MS-based metabolic profiling. Metabolic intermediates of the central carbon metabolism (CCM) were identified using an analytical workflow as published by Kuich et al.26. While no significant differences in metabolic intermediates between the healing groups were detected at day 3 (Fig. 4a), differences in CCM metabolites at metabolic pathway checkpoints (e.g., 3-phosphoglyceric acid (3PG), lactate, and succinate) were detected (Fig. 5a, b, Supplementary Data 5) in day 7 samples. It is important to note, that glucose levels were comparable between successful and compromised bone healing across all time points measured (Fig. 6a, Supplementary Data 6), suggesting no substrate limitations at the beginning of the glycolytic pathway. Lactate showed a 1.4-fold upregulation in samples from successful healing compared to compromised healing, while down-stream metabolites of the CCM, like pyruvate - which connects glycolysis and the tricarboxylic acid cycle (TCA cycle)- and citrate, an intermediate of TCA cycle, showed no differences in measurements. Fig. 5Untargeted metabolomics of rat fracture tissue from successful and compromised bone healing reveal differences in intermediates from the central carbon metabolism.a Heatmap of selected central metabolites from the central carbon metabolism (glycolysis, TCA cycle). Shown are sum over mean normalized LFQ intensities, with following color coding, red= upregulated, white= unchanged between conditions, blue= downregulated, grey= no values. $$n = 3$$-5 biological replicates (BR) per group and time-point. b Schematic central metabolic pathway, metabolites that are shown in heatmap are marked by grey and enlarged circles. Fig. 6Analysis of central carbon metabolites shows increased levels of succinate and expression of the succinate receptor gene at day 7 for successful healing fractures.a Relative label-free qualification of selected metabolites from untargeted metabolite screening. No differences in relative glucose levels between successful and compromised healing identified at day 7 but an increased trend for lactate in successful healing. At day 14 a general increase of the central carbon metabolism (especially the TCA cycle) was detectable in successful bone healing, as seen in significantly increased levels of pyruvate and increased trends in citrate, glutamate, and α-ketoglutarate levels. Succinate showed significantly increased values at day 7 in successful bone healing compared to compromised healing, $$n = 3$$–5 individual biological replicates per group and timepoint, t-test, the dotted line represents control values of unfractured, contralateral bone. b Expression of the succinate receptor 1 (Sucnr1) gene is increased at day 7 in successful healing samples. Relative expression to Tbp (TATA-binding protein, housekeeping gene) and d3 young, $$n = 3$$–5 individual biological replicates per group and time point, One-way ANOVA. Mean±standard deviation is shown for all graphs.
Most interestingly, succinate levels were increased significantly at day 7 in samples from successful healing compared to compromised healing by 3-fold (Fig. 6a, Supplementary Data 6). Levels of the TCA cycle intermediates following succinate–fumarate and malate–were not altered between the bone healing groups (Fig. 6a).
At day 14 after osteotomy, no differences in lactate or succinate levels were detectable between the groups. However, pyruvate and citrate levels were reduced in samples from animals showing compromised healing (2.2-fold and 1.6-fold, respectively) compared to the group of successful bone healing (Fig. 6a). Similar tendencies were detected for the metabolites glutamate, α-ketoglutarate and protein levels of alpha-ketoglutarate dehydrogenase (OGDH), suggesting increased glycolytic and TCA cycle activity and TCA cycle replenishment from glutaminolysis in animals with a successful healing progression (Fig. 6a & Supplementary fig 2, Supplementary Data 6, 12).
Early regulation of the two CCM intermediates, succinate, and lactate, marked them as interesting candidates in regard to their roles during the bone healing process and progress. Both metabolic intermediates have already been discussed as mediators of immune cell responses in literature. Particularly, a study by Tannahill and colleagues, demonstrated significant effects of intracellular accumulation of succinate on interleukin 1-beta (IL-1β) expression by hypoxia-inducible factor 1-alpha (HIF-1α) stabilization in macrophages27. Further studies showed that succinate has additional properties as an extracellular signaling molecule, mediated over its G-protein-coupled receptor, succinate receptor 1(SUCNR1)16,28,29. Interestingly, SUCNR1 is highly expressed in bone marrow and blood cells, particularly MSCs, monocytes, and macrophages (Human protein atlas, Version 19.3)30,31.
Gene expression analysis of the succinate receptor in fracture samples showed an increased expression in samples of successful healing at day 7 (Fig. 6b, Supplementary Data 6). We were interested whether altered cytokine expression of macrophages, e.g., by intracellular succinate accumulation, may influence cellular crosstalk and lead to healing cascade alterations. Metabolic analysis of serum samples collected at the time of animal sacrifice confirmed that there was no systemic regulation of succinate between healing time points in successful or compromised healing (Supplementary Fig 3, Supplementary Data 13). Unfortunately, localization and cellular origin of the accumulated succinate was not possible in the applied setting, due to the usage of homogenized hematoma/callus tissue samples.
We, therefore, decided to focus our attention on the potential function of extracellular succinate as a signaling molecule during the process of bone healing. We could demonstrate a differential expression of macrophage markers between young and old animals in a previous study, showing not only higher monocyte-macrophage marker expression, like Cd14 and Cd68 in fracture tissue of successful healing but also increased anti-inflammatory M2 macrophage gene expression23. Additionally, angiogenic marker genes, like Hif-1alpha and others associated with overall vessel formation were increased in fractures from young animals at day 7. Further studies performed at our institute further confirmed that M2 macrophages appear in the fracture gap at around day 7 in the selected animal model32. We assumed that the observed difference in succinate levels between the successful and compromised healing animals could be related to macrophages and aimed to explore this potential crosstalk further.
We did so, by simulating a local exposure of succinate to cells relevant in bone healing, like macrophages, mesenchymal stromal cells (MSCs) or endothelial cells in vitro, aiming to mimic the microenvironment during successful fracture healing.
## Extracellular succinate induces transcriptional and functional changes in activated human macrophages and CD14+ cells in vitro
First, effects of physiological concentrations of extracellular succinate (50 and 500 µM33,34) on monocytes/macrophages were analyzed. The shift from M1 to M2 macrophages is a strong indicator for the transformation from the pro- to the anti-inflammatory phase in the early hematoma and a crucial step in successful endogenous bone healing18,23. Human monocytic cells (THP-1 cell line) were chosen to study the effect of extracellular succinate exposure (50 and 500 µM) on the differentiation of M1 and M2-like macrophages in vitro. Changes in transcription of M1- and M2-like macrophage markers and cytokines, upon extracellular stimulation with succinate, were analyzed (for differentiation experiments of monocytic cells into macrophage subsets please refer to Supplementary Fig 4, Supplementary Data 14). Addition of succinate to the cultures led to an increase of IL-1β expression in M1 versus M2 macrophages (Fig. 7a, Supplementary Data 7). IL-1β secretion was likewise increased in M1 macrophages upon addition of succinate to the culture (Fig. 7b, Supplementary Data 7). When freshly isolated human cluster of differentiation 14 positive (CD14+) cells were activated with lipopolaysaccharide (LPS) and stimulated with 50 µM succinate, IL-1β secretion was also increased (Fig. 7c, Supplementary Data 7). These results complement previous studies from Tannahill and colleagues, which showed that intracellular accumulation of succinate leads to IL-1β expression27,35.Fig. 7Modulation effects of different cell populations by extracellular succinate application.a Physiological concentrations of extracellular succinate (50 and 500 µM) upregulate the transcription of the IL-1β gene in activated THP-1 M1 macrophages, $$n = 3$$ experimental replicates, Relative expression to RPL13A and d0. One-way ANOVA, Suc – succinate. b IL-1 β secretion was significantly increased in M1 macrophages when stimulated with 50 µM succinate, $$n = 3$$ individual experimental replicates, One-way ANOVA, Suc - succinate. c CD14+/LPS activated cells stimulated with 50 µM succinate secrete higher levels of IL-1β compared to CD14+/LPS activated cells w/o succinate, $$n = 4$$ individual biological replicates, t-test, stim stimulated, Suc succinate. d Tube formation in hHUVECS (human umbilical vein endothelial cells) is increased upon succinate stimulation (50 µM), when added to conditions containing angiogenic growth factors and in growth factor free conditions, $$n = 3$$ biological replicates, Two-way ANOVA, Suc succinate. e Light microscopic pictures of tube formation after 18 h (+GF + FCS) showing representative pictures of control (w/o succinate), 50 µM, and 500 µM succinate, including images of identified tubular structures by ImageJ used to calculate tube length. GF growth factors, FCS fetal calf serum. f Repopulated area/scratch wound healing (%) by hMSCs is enhanced when succinate is added to the culture, in three independent donors after 24 h. $$n = 3$$-4 individual experimental replicates, One-way ANOVA. g Light microscopic images at 0, 12, and 24 h after scratch of treatment conditions (control, 50 µM, and 500 µM succinate). The scratch/cell layer boarder is indicated by the black line. h Analysis of hMSC migration rate every 3 h during the course of the 24 h-experiments. Addition of succinate enhances the migratory rate from early timepoints on. Pool of all three donors, *significant 50 µM to control, #significant 500 µM to control, Two-way ANOVA, CTRL control, Suc succinate, hMSCs human mesenchymal stromal cells. All graphs show mean ± standard deviation.
## Extracellular succinate enhances angiogenic and migratory processes of primary human cells in vitro
As angiogenesis and wound closure are essential steps in bone healing and closely linked to the switch from pro- to anti-inflammation, further experiments targeted the modulation of these two steps by extracellular succinate. Tannahill and colleagues showed that intracellular succinate induces mRNA expression and HIF-1α activity27. When we investigated in vitro tube formation using human HUVECs (hHUVECs), addition of succinate to cultures significantly enhanced tube length and network formation (Fig. 7d;7, Supplementary Data 7) under angiogenic culture conditions (+GF, +FCS, EGM bullet kit, Lonza). This was particularly seen when adding 50 µM of succinate. Remarkably, extracellular stimulation of succinate (50 µM) also led to increased tube formation in growth factor-free conditions (−GF, +FCS) (Fig. 7d, Supplementary Data 7), indicating that extracellular succinate can function as an angiogenic factor. This result complements and extends findings by Mu et al., which demonstrated that higher concentrations of succinate (200–800 µM) promoted the formation of tubular structures in vitro and in zebrafish36.
Wound healing and migration of cellular progenitors into the fracture area is a vital part to bone regeneration37. Therefore we analyzed, how succinate affects the regenerative capability of primary human mesenchymal stromal cells (hMSCs), obtained from patients undergoing total joint replacement surgery.
Addition of succinate to a scratch/wound healing assay considerably enhanced the migration of primary human MSCs. Cells from three independent donors were investigated, all showing higher percentages of repopulated scratch area, when succinate was added to the culture media (15–$20\%$, Fig. 7f, g, Supplementary Data 7). Monitoring hMSC migration over a time of 14 hours showed, that upon treatment with succinate an increased migratory rate was detected compared to control conditions without succinate addition. This effect was significant for treatments with 50 µM succinate at 15 hours post-scratch and onwards, also for treatments with 500 µM succinate from 21 hours post-scratch and onwards (Fig. 7h, Supplementary Data 7). As Ko and colleagues showed, this effect is most probably mediated by the succinate receptor SUCNR138.
## Osteogenic differentiation potential of primary human MSCs increased by extracellular stimulation with succinate in vitro
Osteogenic differentiation using primary human hMSCs obtained from different donors undergoing total joint replacement surgery was performed to investigate the effect of extracellular succinate stimulation on bone formation. Differentiation was performed using a well-established protocol for a period of 18 days (details see Methods). Enzymatic activity of alkaline phosphatase (ALP), marker for early osteogenic differentiation, was significantly increased at day 7 in osteogenic cultures (OM), upon addition of succinate to the culture medium in all tested donors (Fig. 8a, Supplementary Data 8). Similarly, after 18 days of osteogenic differentiation cultures, matrix formation and mineralization were increased when succinate was added to the culture (Fig. 8b, d, Supplementary Data 8). To account for cell division, the cell number was determined by Hoechst staining and did not show any differences between the treatments (Fig. 8c, Supplementary Data 8).Fig. 8In vitro osteogenic differentiation of human MSCs is enhanced upon extracellular stimulation with succinate.a Enzymatic activity of alkaline phosphatase, early phase marker of osteogenic differentiation, is significantly increased at day 7 in conditions with succinate treatment (50 µM, 500 µM), $$n = 3$$-5 individual experimental replicates per donor, One-way ANOVA, mean ± standard deviation. b Alizarin red staining used to identify mineralized matrix formation and osteogenic differentiation shows higher values in succinate treated conditions, compared to control osteogenic differentiation at day 18. $$n = 3$$ individual experimental replicates per donor, One-way ANOVA, mean ± standard deviation c Cell number determined by Hoechst staining is not altered between the treatments (control versus succinate) at day 18 of the differentiation. $$n = 3$$ experimental replicates per donor, dot plot shows the mean, One-way ANOVA. d Exemplary light microscopic picture of alizarin red staining at day 18 shown for osteogenic versus control (expansion medium) conditions and succinate treated wells. EM expansion medium, OM osteogenic medium, Suc succinate, pNPP 4-nitrophenylphosphate, OD optical density, RFI relative fluorescence intensity.
Addition of succinate (500 µM) in vitro particularly induced strong mineralization, when the overall mineralization capacity appeared delayed or less pronounced, as suggested by observations from donor three (Fig. 8c, Supplementary Data 8).
## Combining effects of extracellular succinate and transient IL-1β enhance matrix mineralization during osteogenic differentiation of primary MCSs in vitro
Considering that extracellular succinate induced IL-1β expression and secretion from LPS stimulated macrophages, addition of both, succinate and IL-1β, was investigated on osteogenic differentiation of human MSCs. While constant IL-1β (0.1 ng/ml, 10 ng/ml)39 lead to a decreased osteogenic differentiation and premature cell contraction (Supplementary Fig 5, Supplementary Data 15), a transient addition of 0.1 ng/ml IL-1β (24 h) to hMSCs under osteogenic culture conditions, led to a significant increase of mineralized matrix formation after 18 days (Fig. 9 a, c, Supplementary Data 9). Additional supplementation with succinate significantly enhanced the formation of matrix even further in 2 out of 3 donors and by trend in 1 additional donor (Fig. 9a, c, Supplementary Data 9). Cell number showed no significant alterations between the treatment groups (Fig. 9b, Supplementary Data 9).Fig. 9Combination of transient IL-1β and succinate stimulation during osteogenic differentiation of human MSCs strongly increases osteogenic differentiation potential in vitro.a Addition of IL-1β (0.1 ng/ml) for 24 h after the induction of osteogenic differentiation resulted in increased levels of mineralized matrix compared to control osteogenic conditions (OM without succinate) after 18 days. Simultaneous addition of succinate (50 µM, 500 µM) to IL-1 β conditions, let to a further increase in mineralized matrix levels. $$n = 5$$ individual experimental replicates per donor, *significant to OM control, # significant to OM + IL-1β treatment, by One-way ANOVA. Shown are mean ± standard deviation. b Cell numbers as determined by Hoechst staining is not altered between the treatments (control versus IL-1β versus succinate) at day 18 of differentiation. $$n = 5$$ individual experimental replicates per donor, dot plot shows the mean, One-way ANOVA. c Exemplary light microscopic pictures of alizarin red staining at day 18 shown for osteogenic conditions versus non-osteogenic control conditions and IL-1β ± succinate treated wells. EM expansion medium, OM osteogenic medium, IL-1β interleukin 1 beta, Suc succinate, OD optical density, RFI relative fluorescence intensity.
## Paracrine signals from macrophages stimulated by extracellular succinate generate osteogenic induction in the absence of further osteogenic stimuli in vitro
To further investigate the paracrine crosstalk between succinate-stimulated macrophages and MSCs during bone healing progression, osteogenic differentiation assays were performed using conditioned media from M1- and M2-like macrophages that have been cultured and polarized with and without succinate (50 and 500 μM) addition to the culture medium. This culture media was harvested and given to osteogenic differentiation assays using primary human MSCs. The addition of media from M2-like macrophages in conditions with osteogenic additives led to a significant increase of matrix mineralization as determined by alizarin red staining. This effect was even more pronounced when succinate was added to the cultures during macrophage polarization and stimulation. Addition of conditioned media from M1-like macrophages however, showed decreased mineralization, again the effect was stronger, when succinate was added to the cultures during macrophage polarization (Fig. 10a, b, Supplementary Data 10).Fig. 10In vitro osteogenic differentiation of human MSCs is highly influenced by paracrine signals from conditioned medium from macrophage polarization with additive effects from succinate.a Identified mineralized matrix formation by alizarin red staining shows higher levels in osteogenic conditions when media from M2-like cultures was added to the wells. This effect was higher when M2-like macrophages were stimulated with extracellular succinate during polarization. Media from M1-like macrophage cultures showed a decreasing effect on mineralization when compared to osteogenic control conditions. Media from macrophages stimulated mineralization in control conditions (EM, expansion medium) in the absence of osteogenic differentiation factors. EM expansion medium, OM osteogenic medium, OD optical density, Suc- succinate. M1- M1-like macrophages, M2-M2-like macrophages. Pool of 3 biological replicates/donors, $$n = 3$$ individual experimental replicates for each condition for each donor, One-way ANOVA. Shown are mean ± SD. b Exemplary light microscopic picture of alizarin red staining at day 14 shown for osteogenic versus control (expansion medium) conditions and media from macrophages ± succinate treated wells. EM expansion medium, OM osteogenic medium, Suc succinate. OD optical density, M1- M1-like macrophages, M2-M2-like macrophages.
Most astonishing was the effect observed in cultures without any osteogenic supplements, that usually served as control conditions and where expansion medium was used. Here addition of conditioned media from macrophage polarizations induced mineralization, independent of macrophage polarization (M1 or M2) as seen in Figs. 10a and b. In control conditions, when media from M2-like macrophages stimulated with 50 μM of succinate during polarization and differentiation was added, levels of mineralization showed the highest results.
## Discussion
Bone is one of the few organs that can regenerate without scar formation. This capacity, though in principle well conserved until high ages, appears to be hindered or retarded in many aged individuals and is often linked to prolonged or excessive inflammation, instable fracture fixation or disturbed angiogenesis18,23,32,40,41. The fine-tuned and complex regenerative process is highly dependent on molecular and cellular communication and their interplay. How local nutrient supply affects cellular metabolism and functionality is important in understanding scar-free and successful regeneration. However, the role of metabolic cascades in early bone healing has not been intensively researched so far. Here we present, to the best of our knowledge, for the first time a study that analyzes and compares the metabolic profile of during early bone healing phases in a preclinical in vivo model of functional endogenous versus biologically compromised healing. We further highlight, that specific metabolites – here succinate – can act as a metabolic communicator and thereby steer successful bone tissue regeneration.
The presented metabolomic and proteomic analyses of regenerating bone hematoma and callus tissue revealed a time-dependent engagement of specific metabolic pathways that differ between the experimental groups of successful and compromised bone healing. Successful healing showed enhanced cellular metabolism within the complex hematoma and early callus tissue phases when compared to compromised bone healing. A higher anabolic demand in the group of successful endogenous healing may result in the accumulation of metabolites at metabolic check points of the CCM or upregulation of metabolic proteins. Upregulation of specific metabolites and metabolic proteins at different time points, in particular succinate and lactate at day 7, and at day 14, in parallel to proteins associated with oxidative phosphorylation, suggests a close connection between successful healing progression and local metabolic pathway engagement, which in contrast could not be seen in the group of compromised bone healing. The combination of metabolic and protein data (increased levels of glycolytic, TCA cycle and mitochondrial/oxidative phosphorylation proteins) complemented by gene expression analysis (higher levels of hexokinase 2 indicating increased shuttling of glucose into the CCM), gave us reason to assume that an enhanced cellular metabolism occurs across the complex hematoma and early callus tissue in successful healing (high anabolic demand) compared to compromised bone healing. The TCA-cycle intermediate succinate showed significant accumulation in fracture tissue of successful healing at day 7. However, the subsequently following TCA-cycle metabolites fumarate and malate, did not show such alterations, leading to the assumption that TCA cycle activity towards specific succinate formation or enrichment is reduced locally in compromised healing fracture tissue.
As mentioned before, significant work by Tannahill and colleagues, showed how intracellular accumulation of succinate affects IL-1β expression by HIF-1α stabilization in macrophages27. Within the fracture hematoma/callus environment macrophages and endothelial cells interact to promote angiogenesis, where HIF-1α plays a crucial factor42. Moreover, bone healing progression depends on the cell-cell interaction of macrophages and mesenchymal stromal cells43,44. We believe that altered cytokine expression of macrophages, e.g., by intracellular succinate accumulation, can influence this crosstalk and lead to healing cascade alterations. However, our experimental design with a usage of whole hematoma/callus tissue samples for measurements and analyses, did not permit an assessment of the cellular localization of the accumulated succinate.
Most records reporting on the function of succinate are either investigating the effect of intracellular or extracellular succinate. Several studies have highlighted how accumulation of intracellular succinate stabilizes HIF-1α leading to the expression of angiogenic and pro-inflammatory genes in inflammatory innate immune cells, such as macrophages and dendritic cells27,35,45 Extracellular succinate is also relevant for inflammatory processes and many studies have explored the pro-inflammatory potential of high levels of succinate on inflammatory macrophages, which contributes to disease aggravation e.g., in rheumatoid arthritis or cancer27,29,35,46,47. Succinate, however, can also mediate anti-inflammation, as recent studies show that macrophage derived-extracellular succinate suppresses inflammation in neurons and boost anti-inflammatory responses in adipose tissue30,48,49. These effects are mediated by SUCNR1 ligation16,28, which is highly expressed in bone marrow, MSCs, and blood cells, particular monocytes and macrophages (Human protein atlas, Version 19.3)30,31,50.
What is reported in the literature underlines the here presented finding of succinate acting as an extracellular modulator of cell function, here of human macrophages, endothelial cells, and MSCs. Taken together, these findings support the possibility of succinate as a metabolic communicator in bone healing and possibly other healing scenarios28,48,49. Two recent studies are in line with this hypothesis. While Mu and colleagues focused on succinate/succinate receptor 1 effects in promoting angiogenesis in cancer and used higher concentrations of succinate36, it still demonstrates – together with our work – the potential of succinate driving angiogenesis in tissue regeneration. Ko et. al. showed a direct link of succinate, the succinate receptor 1, and tissue healing. They treated umbilical cord blood-derived stromal cells with 50 µM succinate before transplantation into a skin incision in mice. Wounds showed a faster closure when treated with succinate stimulated cells compared to wounds treated with cells where SUCNR1 signaling was blocked38. This is concurrent with our results of primary hMSCs obtained from the iliac crest of hip replacement patients, where addition of succinate increased hMSCs migration. Importantly, we additionally demonstrated that exposure to succinate increased osteogenic differentiation of hMSC, highlighting the great potential of metabolic communication and modulation for bone tissue regenerative approaches by acting presumably not only as a migratory stimulant for hMSCs within the fracture gap but also supporting osteogenesis and bone formation.
Interestingly, we found that already physiological concentrations of extracellular succinate let to an increased expression and secretion of a pro-inflammatory cytokine—IL-1β. As macrophages and MSCs work in close collaboration during bone healing4,37,43 and succinate peaked at day 7 in the group of successful healing, we explored possible functions of IL-1β on bone healing progression. While Mumme et al. showed that constant IL-1β induced osteogenic and chondrogenic differentiation in vitro39, our data – however – may point in a different direction. Continuous exposure of IL-1β to osteogenic cultures of hMSCs let to a diminished osteogenic differentiation. In contrast, when IL-1β was given only for a specific time at the beginning of differentiation, it highly enhanced osteogenic differentiation of hMSCs. Around day 7 of successful bone healing in the here used animal model the fracture hematoma transitions towards callus formation, with chondrogenic areas forming that later develop into newly formed mineralized tissue. Our findings and supporting reports from literature51,52 indicate that the presence of IL-1β, possibly derived from macrophages stimulated by succinate, could be a factor steering mineralization at this phase of regeneration. Succinate, not only functions as an inducer of IL-1β but afterwards as a stimulator of osteogenic differentiation, which further potentiates mineralization. Conversely, prolonged IL-1β could lead to inflammatory signaling and result in compromising healing, in line with growing knowledge of successful endogenous bone healing being a fine-tuned process, where any alteration may lead to delays. First-line experiments in support of this hypothesis, have been performed by us by adding conditioned media from M1- and M2-like macrophages to osteogenic differentiation assay of primary human MSCs, thus mimicking paracrine signals possibly received by hMSCs and secreted by macrophages during fracture healing. M2-like macrophages-related signals not only increased in vitro mineralization in osteogenic cultures compared to osteogenic control conditions but had M2-like macrophages been stimulated with extracellular succinate during polarization tissue mineralization was even more enhanced. Contrary, M1-like macrophage-related signals reduced in vitro mineralization in osteogenic conditions, more so when succinate was present during macrophage polarization.
Most interesting, paracrine signals from macrophages—whether M1-like or M2-like – stimulated in vitro mineralization of hMSCs, in the absence of osteogenic additives. Signals from M2-like macrophages, stimulated with succinate rendered the highest levels of mineralization, suggesting a close link and fine-tuned interplay of different types of macrophages with MSCs and respective paracrine signals.
This study gives a first glimpse into the complex but essential role of the local metabolic microenvironment in enabling successful endogenous bone healing. However, several limitations need to be mentioned: As discussed earlier, we were not able to unravel the cellular origin of the increased succinate due to technical limitations. Therefore, all further investigations on cellular crosstalk and the involved cell populations within the local healing environment are still speculative. Whether the observed effects are indeed exclusively mediated over the succinate/SUCNR1-axis, a potential target for intervention and advanced therapies, is another important issue that needs to be investigated in further pre-clinical studies. Moreover, we could analyze healing characteristics earliest at day 3, since earlier specimen were still too fragile and instable for analysis, mainly consisting of the initial blood clot. This technical and practical limitation in turn means that we are unable to detect any significant differences in healing that might occur in the very early healing cascades. Besides metabolic targets, different patterns of extracellular matrix and adhesive molecules were identified by the proteomic analysis, indicating possible links between metabolism and extracellular matrix remodeling as additional future perspective. We believe nonetheless that tissue regenerative approaches can strongly benefit from the here presented and discussed findings concerning a metabolic regulation of bone healing and the postulated role of succinate as a potent metabolic cell-to-cell communicator.
We acknowledge once more, that our study marks only a starting point and more research is needed before this knowledge can be transferred towards therapeutic strategies. Especially single cell technologies, may enable to investigate the crosstalk of the distinct cell types in the fracture hematoma in the future.
## Animal model
In vivo animal studies were performed using 3- and 12-months old female ex-breeder Sprague Dawley rats purchased from Charles River WIGA Deutschland GmbH. As published, aged rats that had a minimum of three litters develop a fracture non-union after receiving a 2 mm osteotomy gap in the left femur if no further treatment is applied, therefore served as a model for biologically compromised bone healing in our study19–21,25. Four to six animals were randomly allocated to each group. All experiments were following ARRIVE guidelines, the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and the National Animal Welfare Guidelines. Animal experiments were approved by the local legal representative (Institutional Animal Care and Use Committees, LaGeSo, G$\frac{0120}{14}$, G$\frac{0172}{15}$).
## Surgical procedure
In order to assess bone healing a 2 mm femoral osteotomy was used as described and published22–24. In brief, prior to surgery animals were anesthetized by i.p. injection, antibiotics were given by s.c. injection and eye balm applied (Supplementary Table 1). A longitudinal skin incision and blunt fascia dissection were made to expose the left femur. To stabilize the bone an in-house developed external fixator was mounted. For detailed information on the development of the fixator system please refer to previous publications from Preiniger et al. and Strube et al.21–24, as well as the supplement. The 2 mm standardized double osteotomy was introduced into the femoral bone by sawing, gap reproducibility was ensured by using a sawing template. At the end of the procedure, opened muscle fascia and skin were closed; an anesthetic antagonist and post-surgical analgesia were given (Supplementary Table 1). For a more detailed description of the procedure, please refer to the supplementary information.
## 4.3 Animal sacrifice and sample harvest
Animals were sacrificed at days 3, 7 and 14 post-surgery, after anaesthesia (i.p. injection), by intracardiac injection of 7 ml potassium chloride solution (KCl, 1 M) inducing a cardiac arrest. The left femur was dissected and dislodged from the joints and surrounding muscle tissue carefully removed. Dependent on the down-stream analysis, specimens were handled differently as follows: [1] For RNA and mass spectrometry analyses—2 mm of fracture tissue and adjacent tissue 1 mm proximal and distal (region of interest, ROI) were snap frozen in liquid nitrogen and homogenized subsequently. It was ensured that the tissue remained frozen during the whole homogenization procedure. The frozen and ground tissue samples were split in three different vials for (a) proteomics, (b) metabolomics and (c) RNA extraction. [ 2] For histological and radiographic analyses, femurs were fixed in $4\%$ paraformaldehyde/PBS solution for 24 h at 4 °C. [ 3] Intracardiac blood was collected from all animals before cardiac arrest. Blood serum was obtained by centrifugation, aliquoted and stored at -80 °C until further use.
## µCT & radiographic analysis
Following harvesting and fixation, bones were rinsed with water for 45 min at RT. Femurs were transferred to PBS solution and directly loaded on a sample holder. The scans were performed with a VivaCT 40 microCT (Scanco Medical AG, Brüttisellen, Switzerland) at an isotropic voxel size of 12.5 μm. A 0.5 mm aluminum filter was employed and an x-ray tube voltage of 70kVp and a current of 114 μA. Reconstruction was carried out with a modified Feldkamp algorithm using the Scanco reconstruction software. Ring artifact reduction and beam hardening correction were automatically applied.
## Histology & histomorphometrical analysis
Histological assessment of bone healing was performed on 5 µm thick cyro-sections according to the Kawamoto’s film method53. After fixation, femurs were transferred into ascending sucrose solutions for cryo-protecting purposes, before they were embedded in the same orientation (proximal right, distal left) in SCEM-Medium and frozen by immersion into cold n-Hexan (Sigma-Aldrich, MI, USA). Movat-Pentachrome staining was used to differentiate between mineralized and soft tissues in the fracture zone (ROI)23. Tissues are stained in the following color-code: mineralized bone—yellow/orange, collagen—yellow, cartilage—green/blue, osteoid—dark red, elastic fibers—orange/red and nuclei—blue/black. Pictures were taken with Zeiss Axioscope 40 Microscope, 10x objective and condenser, and Imaging AxioVision LE Software (Carl Zeiss, Germany). Quantification of tissue within the ROI was done using a semi-automated method on blinded sections in ImageJ (Version 1.44p).
## Protein and peptide extraction
Frozen and ground rat fracture tissue samples that have been put aside for proteomic analysis (as described under 4.3 “Animal sacrifice and sample harvest”) were resuspended in urea buffer (8 M urea, 100 mM Tris-HCL, pH 8.25, Sigma-Aldrich, Germany) and sonicated using a Bioruptor®. The homogenate was centrifuged for 10 min, 4 °C, and the supernatant collected. Total protein concentration was determined by BCA colorimetric assay, 100 μg per sample taken for protein digestion. Samples were digested in urea buffer and DTT (2 mM Sigma-Aldrich, MI, USA), reducing the disulfide bonds. Idoacetamide (11 mM, Sigma-Aldrich, MI, USA) was subsequently added, preventing a new disulfide bond formation. A double digest with LysC and trypsin was conducted at 30 °C, digestion stopped by adding trifluoroacetic acid. 18 μg peptide mixture were desalted on STAGE Tips, eluted, dried, and reconstituted in 15 μl $0.5\%$ acetic acid-water solution54.
## Untargeted proteomics
Peptides were separated by reverse-phase chromatography on an in- house made 25 cm columns C18-Reprosil-Saphir (Dr. Maisch, inner diameter: 75 μm, particle diameter: 1.9 μm) using nanoflow HPLC system (Agilent 1200, Agilent Technologies, CA, USA), coupled directly via nano-electrospray ion source (Proxeon) to linear ion trap quadrupole (LTQ) Orbitrap Velos (Thermo Fisher Scientific, MA, USA). Mass spectra were acquired in a data-dependent analysis switch between survey MS scan (m/z 300–1700, resolution $R = 60$′000) and MS/MS spectra acquisition. The 20 most intense ions (Top20) of each survey MS scan were selected for fragmentation and MS/MS spectra acquisition. Monocharged ions, potential contaminants, were excluded from analysis.
## Proteomic data analysis
Raw files from the LTQ Exactive was processed using the MaxQuant computational proteomics pipeline and the built in peptide search engine Andromeda55,56, species specific databases were loaded (UniProt rattus norvegius & common contaminants). An untargeted approach was performed, with LFQ settings (label-free quantifications) and unique peptide quantification to ensure isoform-specific calculation57. Other settings included: trypsin as a protease, with cleavage after lysine and arginine (restriction after proline), variable modifications of methionine oxidation and N-terminal acetylation were chosen, and the peptide tolerance was set to 7ppm. Data quality was evaluated before further analysis by using an in-house developed software (PTXQC)58. Afterwards, data clean-up to remove contaminants and perform data normalization the Perseus software was used59, the general workflow is depicted in Supplementary fig 6. To evaluate under- and over-represented protein groups between successful and compromised bone healing, the fold change was calculated. Values with a fold change <0.7 were considered underrepresented in compromised healing, while a fold change of >1.6 was considered overrepresented/ enriched in compromised healing compared to successful bone healing.
## Metabolite extraction
Metabolites were extracted from whole fracture hematoma/callus tissue and blood serum as described before60, with some modifications as described in the following. Frozen fracture tissue was homogenized as described under 4.3 animal sacrifice and weight. Per 50 mg frozen tissue 1 ml of methanol-chloroform-water (MCW; 5:2:1 v/v/v, Merck, Germany) was added, supplemented with 2 µg/ml cinnamic (Sigma-Aldrich, MI, USA) acid serving as internal standard. MCW-sample solution was further homogenized by ultra-sonication for twice for 30 s. Samples were shaken for 30 min at 10 °C, 200 rpm, water was added and shaken again for 5 min before centrifuged for 10 min at 10,000 x g, 4 °C to separate the polar metabolites from the lipid metabolites/phase. The aqueous, polar phase was collected and vacuum dried. For derivatization, 20 µl methoxyamine hydrochloride solution (40 mg/ml in pyrimidine) (Sigma-Aldrich, MI, USA) was added to the dried fracture tissue extracts and incubated for 90 min at 30 °C. Next, 80 µl of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA, VWR, PA, USA), including a retention index mixture (nine alcanes) was added to the mixture and incubated for 60 min at 37 °C, while shaking constantly. Mixture was centrifuged for 5 min at maximum speed and supernatant was split and transferred into glass vials for GC-MS measurement.
## Untargeted metabolomics
All samples were measured by GC-TOF-MS as described60,61. In brief, samples were measured in a 1:5 split with 1 μl injection volume on a gas chromatography coupled to time of flight mass spectrometer (Pegasus III- time-of-flight (TOF)-MS-System, St. Joseph, MI, USA) from LECO®. Quality of injection and alkane peaks were regularly monitored, and temperature regulated. Temperature program during sample injection: 30 s, 80 °C, followed by a gradient with 12 °C/min up to 120 °C. Second ramp with 7 °C/min up to 300 °C and held for 2 min. Gas chromatographic separation was performed on an Agilent 6890 N, equipped with a VF 5 MS column and helium as a carrier gas at a flow rate of 1.2 ml/min. Temperature program after sample injection: 2 min, to 67.5 °C, temperature ramp up to 120 °C with 5 °C steps per min. Second ramp with 7 °C rise per min to 200 °C, followed by 12 °C per min to 320 °C, held for 6 minutes. Sample order was randomized, artefacts due to a longer standing time in experimental groups was thus avoided. Mass spectra were recorded in a mass range of 60 to 600 mass units with 10 spectra/s at a detector voltage of 1650 V.
## Metabolite data analysis
The raw data and peaklists were extracted, baseline corrected, and resampled using the ChromaTOF® software. Data was subsequently read into the in-house analysis software MAUI-VIA for annotation, normalization, and quantification of metabolites by targeted library search for metabolite spectra. For detailed information about the procedure and the software please refer to Kuich et al.26
## Cell culture THP-1 and MP differentiation/polarization and primary CD14+ isolation
Effects of extracellular succinate (50 µM and 500 µM) were studied on M1 and M2-like macrophages differentiated and polarized from the human monocytic cell line THP-1 as described before62. THP-1 (ECACC, Sigma-Aldrich/Merck) monocytes were seeded at 5 × 105–1 × 106 cells per 6 well and stimulated for 72 h with 100 ng/ml Phorbol 12-myristate 13-acetate (PMA) in 1640-RPMI (Sigma-Aldrich, MI, USA) to induce adherence. They were put on cytokine and FCS-free 1640-RPMI media for 24 h before differentiated into M1 macrophages by 100 ng/ml LPS (Sigma-Aldrich, MI, USA) and 20 ng/ml IFNɣ (Biolegend, CA, USA) and into M2 macrophages by using 20 ng/ml IL-4 (Miltenyi, Germany) and 10 ng/ml IL-13 (Miltenyi, Germany) for 72 h. Polarization state and transcriptional changes upon succinate stimulation were confirmed by qPCR. Primary human CD14 + cells were freshly isolated from human blood (commercially acquired buffy coats) by density-gradient isolation and MACs (Miltenyi, Germany) and seeded at 1 × 106 cells per well before stimulated with LPS and succinate (Sigma-Aldrich, MI, USA). IL-1β secretion was analyzed in the culture media after 24 h by human-Interleukin 1β Quantikine ELISA (R&D Systems, MN, USA).
## Gene expression, qPCR analysis
*For* gene expression analysis of fracture tissue samples were harvested as described under 4.3 animal sacrifice. TRIzol Reagent (LifeTechnologies, CA, USA) was added to the homogenized tissue, and RNA isolated according to the manufacturer’s protocol. For cell culture gene expression analysis, cells were lysed in RLT buffer and RNA isolated with RNeasy® Mini Kit (Qiagen, Netherlands) according to the manufacturer’s protocol. RNA concentration was determined using a Nano-Drop spectrophotometer. 25 ng/µl RNA was transcribed to cDNA with iScript reverse transcriptase as suggested by the manufacturer (Bio-Rad Laboratories, CA, USA). Gene transcript expression was determined by qPCR (LightCycler®480, Roche, Switzerland). A list of all genes tested, and the primer sequences can be found in Supplementary Table 2. Primer sequences were generated and tested for specificity using the NCBI website Primer-BLAST. Gene expression was analyzed according to the ddCT method with respective adjustment to primer efficiency and normalization to housekeeping genes by using the REST software63. Rat: the housekeeping gene Tata-box binding protein (Tbp) was tested against others (Gapdh, Actb, Eif4e, and B2m) and found the most stable across all samples. Human: the housekeeping gene RPL13A was found the most stable-expressed in all sample and against other housekeeping genes tested (TBP, ACTB, EIF4e and RLP0) and therefore used as a reference gene.
## Tube formation assay
HUVECs (bought: Lonza Switzerland, Lot 220213, 80016, 482213) were cultivated in endothelial growth medium (EGM, EGM BulletKit, Lonza, Switzerland) until $80\%$ confluence. Tube formation was assessed as published64. In brief, 5 × 104 cells were plated per 24 pretreated/coated well with 50 µl growth factor reduced Matrigel (BD Biosciences. NJ, USA). To assess the effect extracellular succinate has on tube formation, cells were cultivated in EGM with growth factors, penicillin/streptomycin, and $2\%$ FCS, ± succinate: 50 µM and 500 µM (+/+), in EGM without growth factors, supplemented with penicillin/streptomycin and $2\%$ FCS ± succinate: 50 µM and 500 µM (−/+) and in EGM without growth factors, without $2\%$ FCS but supplemented with penicillin/streptomycin ± succinate: 50 µM and 500 µM (−/−). Tube formation was documented after 18 h of culture by bright field microscopy. Total tube formation length per well was measured using ImageJ (version 1.44; http://rsbweb.nih.gov/ij/) and effect of succinate compared to control for each condition.
## Migration assay
The migratory behavior of primary human MSCs upon extracellular succinate stimulation was assessed by using a scratch wound healing assay. 1 × 105 MSCs were seeded per 24 well and allowed to attach to a confluent layer overnight. Using a 200 µl pipette tip a scratch disrupting the cell layer was created. Cells were washed with PBS and media supplemented with penicillin/streptomycin and ±$\frac{50}{500}$ µM succinate but without FCS was added to the respective wells. Cell migration was tracked for 24 h, with a picture taken every 30 min using a bright field microscope (inverted DMI600B, Leica, Germany) with a life cell imaging system. Migration was analyzed using TScratch software65 in a blinded approach for the different groups.
## Osteogenic differentiation of primary human MSCs
Primary human MSCs were isolated from patient bone marrow, which was obtained during surgical procedures of hip or joint replacement, according to the ethics approval EA$\frac{099}{10}$ and after written informed consent once contagious maladies were excluded. To that end, bone marrow mononuclear cells were isolated by density gradient centrifugation and plastic adherence according to Pittenger et al.66 Cells were expanded in DMEM for 14 days, then split and reseeded as passage 1. Cells were routinely checked for the prerequisites for human MSCs according to the International Society for Cellular Therapy67,68. For all experiments, MSCs in passage 3 were used.
Osteogenic differentiation was conducted was adapted after Krause et al. and performed as described before66. In brief, 2.05 × 103 cells were seeded per well (96-well plate) and were allowed to attach and form a confluent layer over night. Osteogenic media (DMEM low glucose, Gibco, NY, USA) was supplemented with $10\%$ FCS (Biochrome, Germany), $1\%$ Glutamax (Gibco, NY, USA), $1\%$ penicillin/streptomycin (Biochrome, Germany), 0.1 µM dexamethasone (Sigma-Aldrich, MI, USA), 10 mM β-glycerolphosphate disodium salt hydrate (Sigma-Aldrich, MI, USA) and 50 µM L-ascorbic acid 2-phosphate sequimagnesium salt hydrate (Sigma-Aldrich, MI, USA) and additional substances as indicated (succinate, IL-1β). Control wells received normal MSCs expansion media (DMEM low glucose, $10\%$ FCS, $1\%$ Glutamax, $1\%$ penicillin/streptomycin) and additional substances as indicated. Osteogenic differentiation was performed for a total of 18 days. Early osteogenic induction was assessed by alkaline phosphatase (ALP) activity at day 7, while late osteogenic differentiation was analyzed by matrix mineralization via alizarin red S staining at day 18.
Alkaline phosphatase activity was determined by colorimetric measurement. To that end, 4-nitrophenylphosphate was given to each well and the production of 4-nitrophenolate by ALP was determined by color intensity and optical density (OD) λ = 405 nm using a Tecan plate reader. Enzyme activity was calculated using the absorbance coefficient (ε). With ε = 18450 L x mol-1 x cm-1 and $d = 0.3294$ cm. C = (E−E0)/ε×d, were E = absorbance (OD), E0 = absorbance blank (OD), c = molar concentration [mol/L], ε = molar absorbance coefficient [L x mol-1 x cm-1] and d = thickness of layer [cm]—e.g., 0.3294 cm for a 96-well, filled with 100 μL.
Matrix mineralization was visualized by alizarin red (AR) staining and quantified by colorimetry at OD of 562 nm. First, the cell number of each well was determined by Hoechst staining at λ = 364 nm/460 nm. Afterwards, wells were washed with H2O and overlaid with $0.5\%$ alizarin red solution. Wells were washed 3 times with H2O to remove any excess or unbound staining solution. Last, the stained matrix was dissolved by adding cetylpyridnium chloride (Sigma-Aldrich, MI, USA) and determined by colorimetric analysis at λ = 562 nm. Treatment conditions (succinate, IL-1β) were compared to control/untreated wells.
## Osteogenic differentiation of primary human MSCs using conditioned media from macrophage cultures
Osteogenic differentiation using conditioned medium from macrophage polarization cultures with and without the presence of succinate was performed as described under 4.16. Macrophage polarization medium was harvested, centrifuged, the supernatant aliquoted, and stored at −80 °C until use during osteogenic culture. The additives in the osteogenic differentiation medium were increased to a 2x concentration and mixed 1:2 with the conditioned medium from macrophage cultures respectively before adding it to the culture wells containing the hMSCs. Experiments were carried out with cells from three independent donors and each condition was tested in triplicates.
## ELISA
Cytokine secretion from cell culture were measured using the human-Interleukin IL-1β Quantikine ELISA kit from R&D Systems, USA, MN (DLB50). Quantification was performed according to the manufacturer’s instructions.
## Statistics and reproducibility
Determined values are depicted as bar charts (with single values) or dot plots showing mean ± standard deviation. For statistical analysis GraphPad Prism 8.4. was used. All data were analyzed for normality distribution and subsequently tested with Welch’s t-test or ANOVA using Tukey post-hoc correction for multiple testing. If normal distribution could not be confirmed within a data set, a non-parametric test was performed instead, either Man Whitney U Test or multiple pairwise comparison with Dunn’s test. All test were performed as two-sided tests. P-values ≤ 0.05 were considered significant, trends are indicated by p-value in the respective figure. For each figure, the applied statistical method and amount of biological or experimental replicates are indicated in the figure legend.
## Supplementary information
Peer Review File Supplementary Information Description of Additional Supplementary Files Dataset 1 Dataset 2 Dataset 3 Dataset 4 Dataset 5 Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 Dataset 11 Dataset 12 Dataset 13 Dataset 14 Dataset 15 Dataset 16 Dataset 17 nr-reporting-summary_COMMSBIO-22-0835A_final_submission The online version contains supplementary material available at 10.1038/s42003-023-04652-1.
## Peer review information
Communications Biology thanks Elizabeth Redina-Ruedy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Martina Rauner and David Favero. Peer reviewer reports are available.
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|
---
title: The association between bacteria and outcome and the influence of sampling
method, in people with a diabetic foot infection
authors:
- Meryl Cinzía Tila Tamara Gramberg
- Shaya Krishnaa Normadevi Mahadew
- Birgit Ilja Lissenberg-Witte
- Marielle Petra Bleijenberg
- Jara Rebekka de la Court
- Jarne Marijn van Hattem
- Louise Willy Elizabeth Sabelis
- Rimke Sabine Lagrand
- Vincent de Groot
- Martin Den Heijer
- Edgar Josephus Gerardus Peters
journal: Infection
year: 2022
pmcid: PMC10042898
doi: 10.1007/s15010-022-01884-x
license: CC BY 4.0
---
# The association between bacteria and outcome and the influence of sampling method, in people with a diabetic foot infection
## Abstract
### Purpose
Different bacteria lead to divers diabetic foot infections (DFIs), and some bacteria probably lead to higher amputation and mortality risks. We assessed mortality and amputation risk in relation to bacterial profiles in people DFI and investigated the role of sampling method.
### Methods
We included people (> 18 years) with DFI in this retrospective study (2011–2020) at a Dutch tertiary care hospital. We retrieved cultures according to best sampling method: [1] bone biopsy; [2] ulcer bed biopsy; and [3] swab. We aggregated data into a composite determinant, consisting of unrepeated bacteria of one episode of infection, clustered into 5 profiles: [1] *Streptococcus and* Staphylococcus aureus; [2] coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium and Enterococcus; [3] gram-negative; [4] Anaerobic; and [5] less common gram-positive bacteria. We calculated Hazard Ratio’s (HR’s) using time-dependent-Cox regression for the analyses and investigated effect modification by sampling method.
### Results
We included 139 people, with 447 person-years follow-up and 459 episodes of infection. Sampling method modified the association between bacterial profiles and amputation for profile 2. HR’s ($95\%$ CI’s) for amputation for bacterial profiles 1–5: 0.7 (0.39–1.1); stratified analysis for profile 2: bone biopsy 0.84 (0.26–2.7), ulcer bed biopsy 0.89 (0.34–2.3), swab 5.9*(2.9–11.8); 1.3 (0.78–2.1); 1.6 (0.91–2.6); 1.6 (0.58–4.5). HR’s ($95\%$ CI’s) for mortality for bacterial profiles 1–5: 0.89 (0.49–1.6); 0.73 (0.38–1.4); 2.6*(1.4–4.8); 1.1(0.58–2.2); 0.80(0.19–3.3).
### Conclusions
In people with DFI, there was no association between bacterial profiles in ulcer bed and bone biopsies and amputation. Only in swab cultures, low-pathogenic bacteria (profile 2), were associated with a higher amputation risk. Infection with gram-negative bacteria was associated with a higher mortality risk. This study underlined the possible negative outcome of DFI treatment based on swabs cultures.
## Introduction
In people with diabetes, an ulcer with foot infection is the most prominent risk factor for lower extremity amputation [1–3]. Different bacterial species are thought to lead to clinically diverse infections with subsequent amputation risks [4–8]. In severe infections (including osteomyelitis), grade 4 of the International Working Group on the Diabetic Foot (IWGDF) diabetic foot infection classification, 77 to $90\%$ of patients will undergo amputation [9–11]. Five-year mortality after (major) amputation is as high as $90\%$ [12–15]. Insight in the association between different bacterial species and outcome is, therefore, of great importance, and might have implications for everyday practice.
In temperate climates, *Staphylococcus aureus* and (beta-haemolytic) *Streptococcus are* the most commonly identified pathogens in diabetic foot infections (DFIs) [6, 16]. Infections are usually acute, classified as mild to moderate and formation of pus is possible [5]. In daily practice, coagulase-negative Staphylococcus (CNS), Corynebacterium, Enterococcus and Cutibacteria (formerly Propionibacterium) are commonly present in ulcer cultures, but are usually considered colonizing, rather than pathogenic bacteria. However, when present in deep infections including osteomyelitis, they can be pathogens [6, 16–18]. Gram-negative bacteria cause approximately one third of DFIs, and anaerobes are found in a smaller minority of DFIs [4, 17, 19, 20]. Infections with these micro-organisms are usually chronic and more severe [21].
The presence of clinical and systemic signs of inflammation are used in the diagnosis of a skin and soft tissue DFI. Since all ulcers are colonized with bacteria, culture results of ulcers are used to guide antimicrobial therapy only, and cannot be used for diagnosis. Reference standard for osteomyelitis is the presence of signs of inflammation combined with a positive aseptically obtained percutaneous bone biopsy obtained through intact skin, adjacent the ulcer [22, 23]. In case of osteomyelitis, such a bone biopsy is preferred, and ulcer bed biopsy is considered second best. This is, however, based on limited data [23]. Whether bone culture directed antimicrobial therapy leads to better outcomes than ulcer bed culture directed therapy, is currently under investigation [24]. Ulcer bed biopsy is preferred in case of soft tissue infection without osteomyelitis [22, 23]. Swabs, although often used in cases with and without osteomyelitis, are considered inferior for detection of causative bacteria of DFI. Swabs are prone to culturing colonizing flora and causative bacteria could be missed [20, 25–28]. Proper identification of causative bacteria is essential to guide antibiotic therapy and to lower the risk of adverse outcomes. Therefore, sampling method (bone biopsy, ulcer bed biopsy or swab), is an important factor to consider, while studying the relation between bacterial profiles and amputation and mortality. In this retrospective study, we investigated associations between bacterial profiles and amputation and mortality risk, and we investigated the role of sampling method in this relation.
## Methods
We conducted a retrospective cohort study at the Amsterdam University Medical Centres location VUmc. We retrieved medical records from patients (≥ 18 years of age) who presented with a DFI between March the 16th 2011 and January 1st 2020 using the International Classification of Diseases codes 10th edition and the Dutch Diagnosis Treatment Code for ‘diabetes’ and ‘infection’. We included a person if DFI was diagnosed and DFI cultures were available. The diagnosis of infection was stated by an internist, rehabilitation physician or vascular surgeon and based on a combination of clinical findings of infection (swelling, pain, redness, warmth, purulent discharge, foul smell), increased systemic inflammatory biomarkers and/or abnormalities of the foot on imaging suggesting tissue and/or bone infection [23]. If no material for culture was taken or if culture results were missing, a participant was excluded. Participant follow-up started the date cultures were taken, and follow-up was until database closure (January 1st 2020) or until the participant deceased or was lost to follow up.
Most participants were treated by a multidisciplinary team consisting of an internist, rehabilitation physician, vascular surgeon, podiatrist, orthopaedic shoe technician and casting technician. Treatment consisted of antimicrobial therapy with or without surgery, in combination with appropriate offloading, vascular intervention if required and proper ulcer dressing.
## Demographics, sampling methods, cultures and episodes
We obtained participant characteristics (gender, age, type of diabetes, duration of diabetes and history of amputation) using electronic medical records. Sampling methods were: percutaneous bone biopsy, taken adjacent to the ulcer through intact disinfected skin or in the operating theatre, ulcer bed biopsy of debrided and rinsed ulcer beds, or swabs of debrided and rinsed ulcer beds. Material obtained by any of these methods was cultured, using standard techniques for culturing, identification of bacteria and to test antimicrobial sensitivity. These techniques included inoculating the sample on agar plates and in broth, followed by incubation in aerobic and anaerobic environment. We recorded sampling method (bone biopsy, ulcer bed biopsy, swab), if osteomyelitis as stated by the treating physician was present, and culture results. Similar bacterial species that were cultured at the same time in material from different sampling methods of the same location, were only counted in the best available sampling method. Best available sampling method according to the International Working Group on the Diabetic Foot (IWGDF) was bone biopsy in case of osteomyelitis, and ulcer bed biopsy if bone biopsy was not performed or if the participant had soft tissue infection without osteomyelitis [23]. We only used cultures from swabs if neither bone biopsy nor ulcer bed biopsy cultures were available.
We grouped cultures in episodes of infection. We defined one episode as all cultures from the same DFI within 3 month time. For the analysis of data we constructed a composite determinant. The composite determinant consisted of pooled unrepeated bacteria of one episode (Fig. 1).Fig. 1Step 1: cultures obtained from one participant during the course of 1 infection episode. There were 3 cultures obtained during this episode. 1. Swab: E.coli. 2. Ulcer bed: S. aureus and bone biopsy: E. coli. 3. Swab: Corynebacterium and bone biopsy: S. aureus. Step 2. Selecting best sampling method. Bone biopsy prevails over swab and ulcer bed biopsy, we therefore discarded the results of swab and ulcer bed biopsy (red crosses). Step 3. We created a composite determinant consisting of bacteria from best sampling method, and in which we removed duplicate bacterial species, e.g., if a S. aureus was cultured twice during one infective episode, we only counted this bacterial species ones (unrepeated bacterial species). After deduplication of bacterial species we grouped these bacteria into one of the five profiles. S. aureus causes acute, usually mild, infections and is grouped with *Streptococcus in* profile 1, and E. coli is a gram-negative bacterium, causing usually severe infection and is grouped in profile 3. For the analyses we used profile 1 and profile 3 in this example
## Outcomes
We recorded the number of episodes of infection for all participants during follow-up.
Primary outcomes were amputation and mortality. We used surgical procedure codes to identify patients who underwent lower extremity amputation during the study period, excluding amputation due to gangrene due to ischemia without infection, and we recorded level of amputation: minor (distal to the malleoli) or major (proximal to the malleoli) [29], and we gathered information regarding mortality from medical records.
We also recorded if a participant was lost to follow up. The last documentation date in the participant’s medical records, counted as date of censoring.
## Analyses
We analysed data using R version 4.0.3. To explore bacterial profiles, we grouped individual bacterial species into 5 profiles, according to perceived virulence and morphology. Profile 1: *Streptococcus and* Staphylococcus, infections are usually acute, and mild and the formation of pus is possible. Profile 2: Coagulase-negative staphylococcus, Cutibacterium, Corynebacterium and Enterococcus, these bacteria are usually considered low-pathogenic. Profile 3: gram-negative bacteria, usually these infections are considered more severe and polymicrobial. Profile 4: anaerobic bacteria, infections are usually considered severe. Profile 5: less common gram-positive bacteria (e.g., Actinomyces, Lactococcus) [5]. We collected culture results of one infective episode of one participant, than we selected only the results of the best available sampling method, and we aggregated these selected results into a composite determinant, and used this composite determinant for the analyses, i.e., if in one participant respective of one episode more than one profile was encountered in the best sampling method (e.g., S. aureus (profile 1) and E. coli (profile 3) were encountered both in bone biopsy), than both these profiles were used for the analyses. Aggregation of data into bacterial profiles and the composite determinant is shown in Fig. 1.
We calculated incidence rates for infection per profile per person year for profiles 1–5, and used time-dependent Cox regression to investigate the association between bacterial profiles (the exposure) and time to amputation and death (the outcomes). For this type of regression analyses we used the exact dates of culturing and the appearance of the outcome, in this way we included the time component in the analyses. Effect sizes were reported as Hazard Ratios (HRs) including $95\%$ confidence intervals ($95\%$ CI’s). For each of the bacterial profiles we created a dichotomous variable and scored them as being present in the culture or not. We investigated effect modification for the variables: best sampling method and the presence of osteomyelitis. As a sensitivity analysis, we investigated the relation between bacterial profiles and amputation and mortality in participants without a history of amputation.
## Baseline characteristics and cultures
We screened records of 298 people with suitable International Classification of Disease-10 and Diagnosis Treatment Codes, of which 202 had a registered foot ulcer. Of these people, we could obtain microbiological data of 139 people, and included them in the study. Baseline demographics are displayed in Tables 1. Table 2 shows culture results, and bacterial profile frequencies in positive cultures. Table 1Baseline characteristicsBaseline characteristicsN, (%)—unless otherwise specifiedMale gender95 (68.3)aAge in years, mean (SD)71.3 (11.8)Type 2 diabetes126 (90.6)aDuration of diabetes in years, median (interquartile range)b18 (8–29)History of amputation38 (27.3)aaPercentage of the total population of 139 peoplebDuration of diabetes calculated from the study start dateTable 2Cultures, episodes, follow-up and outcomesCulturesTotal amount of cultures taken during study3310Positive cultures2238 (67.6)aPolymicrobial1554 (69.4)bCultures of best sampling method after deduplication1408Frequencies of bacterial profiles in cultures ProfileFrequency 1478 (20.5)b 2248 (11.1)b 3473 (21.1)b 4168 (7.5)b 541 (1.8)bEpisodes of infection Total episodes459 Median episodes per participant (interquartile range)2 (1–4) Episodes of infection with osteomyelitis155 (33.8)cFollow-up Total follow-up, person-years447 Lost to follow up15 (10.8)dOutcomesMortality Total50 (36.0)d Men32 (64.0)eAmputations Total of 1st amputations71 (51.1)d Minor55 (77.5)e Subsequent amputation64 (90.1)faPercentage of the total of 3310 culturesbPercentage of the total of 2238 positive culturescPercentage of the total number of 459 episodesdPercentage of the total population of 139 peopleePercentage of the 50 deceased peoplefPercentage of total of 71, 1st amputationgPercentage of total of 71 participants with a 1st amputation
## Episodes, sampling method and outcomes
The second part of Table 2 shows the total number of episodes during the study and the median and interquartile range of these episodes per person. Of 155 episodes with osteomyelitis, the best sampling method was bone biopsy in 48 ($31\%$); ulcer bed biopsy in 55 ($35.5\%$) and swab in 52 ($33.5\%$) of the episodes. Data regarding the presence of osteomyelitis were missing for 10 episodes ($2.2\%$).
## Association between bacterial profiles and, amputation and mortality
Incidence rates and $95\%$ confidence intervals for infection per profile per person year for profiles 1–5 were: 1.1 (0.99–1.2); 0.48 (0.41–0.57); 0.99 (0.88–1.1); 0.37 (0.31–0.45); 0.09 (0.06–0.13), respectively. HR’s of the time-dependent Cox regression for the association between bacterial profiles and, amputation and mortality including $95\%$ CI’s are displayed in Table 3. Best sampling method appeared to be an effect modifier in the relation between infection with bacterial profile 2 and amputation risk; therefore, stratified results are presented for profile 2 (Table 3). In these stratified analyses we found for coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2) in swabs a HR of 5.87 (2.9–11.8) for amputation. For gram-negative bacteria (profile 3), we found a HR of 2.6 (1.4–4.8) for mortality. Best sampling method did not appear an effect modifier in the association between bacterial profiles and mortality. Osteomyelitis was not an effect modifier in either association. Table 3Association between bacterial profiles and, amputation and mortalityAmputationMortalityProfileHR ($95\%$ CI)HR ($95\%$ CI)10.66 (0.39–1.1)0.89 (0.49–1.6)22.8 (1.7–4.6)0.73 (0.38–1.4)Bone biopsy 0.84 (0.26–2.7)Ulcer bed biopsy 0.89 (0.34–2.3)Swab 5.9 (2.9–11.8)a31.3 (0.78–2.1)2.6 (1.4–4.8)B41.6 (0.91–2.6)1.1 (0.58–2.2)51.6 (0.58–4.5)0.80 (0.19–3.3)Table 3 shows Hazard Ratios (HR’s) and $95\%$ Confidence Intervals ($95\%$ CI’s) for amputation and mortality according to bacterial profile. The association between profile 2 and amputation risk was modified by sampling method; therefore, separate HR’s for bone-, ulcer bed biopsy and swab are displayed. Profile 2 when present in swabaCultures were significantly associated with a higher amputation risk. BCultures were significantly associated with a higher mortality risk.
In the sensitivity analyses, we included 313 episodes in participants without history of amputation. These analyses showed similar associations between bacterial profiles and amputation or mortality risk as the primary analyses.
## Discussion
This study showed that in people with DFI, there was no association between any of the bacterial species (profiles 1–5) when found in ulcer bed or bone cultures as best sampling method, and risk of amputation. We did find an increased risk of amputation in people with DFI with coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2), when best sampling method was swab. We also found that infection with gram-negative bacteria (profile 3) increased mortality risk. The method of sampling did not modify this association.
There was no association between any of the bacterial species (profiles 1–5) and risk of amputation when found in the sampling methods recommended by international guidelines, i.e., bone and ulcer bed biopsies. Only when these samples were not available, we looked at bacteria in swab cultures as best available sampling method. We found an association between culture of a swab sample with coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2) and a higher amputation risk. This was an interesting observation, since these bacterial species, when found in swabs, are considered not pathogenic, colonizers or contaminants and are not always reported nor treated. Furthermore, these species are usually difficult to treat due to inherent or acquired antimicrobial resistance. However, when found in an aseptically obtained bone biopsy, these bacteria are considered pathogens and treated accordingly. Previous studies show that the concordance between swab and ulcer bed biopsy and bone biopsy is low [17, 20, 28]. Superficial cultures can miss pathogens causing infection deeper in the tissue and might identify colonizing agents as pathogens. Determining whether a cultured bacterium is a true pathogen or not, based on swab cultures is, therefore, difficult and will likely lead to over and under treatment and worse outcomes [30, 31]. This implicates, that the role of coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, and Enterococcus in DFI can be underestimated based on swab cultures, and stresses the importance of obtaining adequate samples, i.e., ulcer bed- and bone biopsies [5, 23–25, 32, 33]. Interpretation of these cultures is less ambiguous and will lead to better guided treatment [19]. Another possible explanation for a higher amputation risk for bacteria found in swabs is, that people with only swab cultures, were treated differently and probably not by our multidisciplinary foot care team, where ulcer bed biopsies and bone biopsies are common practice.
We found higher mortality rates in people with infection with gram-negative bacteria (profile 3), regardless of sampling method. The latter probably because gram-negative bacteria are always interpreted as pathogenic bacteria and treated accordingly. The association we found, is in line with the retrospective studies of Lipsky et al. and Shaheen et al [34, 35]. It is unclear whether gram-negative bacteria increase the risk of mortality directly or indirectly. The risk of mortality could be increased directly due to the pathogenicity of the gram negative bacteria, or indirectly, because people with an increased mortality risk due to comorbidities (e.g., peripheral artery diseases) are prone for infection with gram-negative bacteria.
Staphylococcus aureus and gram-negative bacteria are reported in a meta-analysis of Macdonald et al., as the main pathogen in DFI [36]. Our study confirmed these results, as profile 1 (consisting of *Staphylococcus aureus* and Streptococcus) and 3 (gram-negative bacteria), were most frequently cultured. In accordance with other studies we found that *Streptococcus and* *Staphylococcus aureus* (profile 1) do not increase amputation or mortality risk [7, 8, 34, 35]. Possible explanations are: the type of infection these bacteria cause, usually acute and mild, as a result of which infection will be recognised in an early stage and treatment starts promptly. Streptococcus and *Staphylococcus aureus* are always recognised as pathogens regardless of sampling method, these pathogens are covered by empirical treatment, and these bacteria are predominantly seen in people not recently treated with antibiotic therapy [5, 22, 37, 38]. Recent and past antibiotic use (long term or frequently), can influence culture results, e.g., due to bacterial selection, induction of antibiotic resistance or bacterial suppression [39, 40]. If bacterial growth is suppressed, and bacteria are not fully eradicated, this might lead to false negative culture results and inadequate treatment. To date there is limited research on the role of recent or past antibiotic use and the influence on culture results and infection remission in people with DFI.
It is difficult to investigate the association between a single bacterium and outcome. To be able to investigate this association, extremely large databases are needed to control for smalls groups (not all bacteria are cultured frequently), and multiple testing. To overcome these difficulties, we aggregated bacterial species into five profiles according to morphology and perceived virulence. The strength of this approach is, that we created 5 common profiles that are applicable for everyday practice, and results of the study are apprehensible. We also aggregated data into episodes of infection. Cultures taken from the same patients in 3 month time were considered as one episode. DFI’s usually occur in slow healing ulcers, by creating episodes we prevented that multiple cultures over time, of the same DFI, of one participant were in included in the analyses as separate infections. The weakness of this approach is, that it is somewhat artificial, and the cutoff, of 3 months as one infection episode, can be arbitrary in some cases of ongoing osteomyelitis or frequently relapsing infection.
Although infection increases the risk of amputation and amputation increases the risk of mortality, [12–15] we did not find an increased mortality risk in participants with profile 2 infections. Mortality depends on a combination of factors, and type of causative bacteria of DFI, is one of these factors. Other factors are, e.g., the presence of vascular disease and immobility [13]. Comorbidities might differ between participants with coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2) and gram-negative infection (profile 3). Another explanation lies in the nature of the analyses. Participants with infection with one profile, could also have infection with other bacterial profiles, the combinations of these profiles vary between the separate analyses, and therefore, results cannot be combined.
In conclusion, in this retrospective cohort study in people with DFI, we found that there was no association between *Streptococcus and* *Staphylococcus aureus* (profile 1), coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2), gram negative- (profile 3), anaerobe- (profile 4) and other gram positive bacteria (profile 5) and amputation when found in ulcer bed- or bone biopsies. Only when found in swab cultures, coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium, Enterococcus (profile 2) were associated with a higher amputation risk. Infection with gram negative bacteria (profile 3) was associated with a higher mortality risk, regardless of sampling method. The results of this study propagate the importance of using a sampling method recommended by international guidelines to obtain cultures to optimize treatment and to lower the risks of amputation and mortality.
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|
---
title: Ergebnisse eines monozentrischen Gefäßscreeningprogramms in Deutschland
authors:
- K. Passek
- U. Ronellenfitsch
- K. Meisenbacher
- A. Peters
- D. Böckler
journal: Chirurgie (Heidelberg, Germany)
year: 2023
pmcid: PMC10042912
doi: 10.1007/s00104-023-01821-0
license: CC BY 4.0
---
# Ergebnisse eines monozentrischen Gefäßscreeningprogramms in Deutschland
## Abstract
### Hintergrund
Kardiovaskuläre Erkrankungen sind die häufigste Todesursache in Europa mit relevanter sozioökonomischer Belastung. Ein Screeningprogramm für Gefäßerkrankungen bei asymptomatischen Personen mit definierter Risikokonstellation kann zu einer frühen Diagnose führen.
### Ziel der Arbeit
Die Studie untersucht ein Screeningprogramm auf Karotisstenosen, periphere arterielle Verschlusskrankheit (pAVK) und abdominelle Aortenaneurysmen (AAA) bei Menschen ohne anamnestisch bekannte arterielle Gefäßerkrankungen hinsichtlich demografischer Daten, Risikofaktoren, Vorerkrankungen, Medikamenteneinnahme sowie Detektion und Therapie pathologischer Befunde.
### Material und Methoden
Probanden wurden durch verschiedene Informationsmaterialien eingeladen, ein Fragebogen erfasste kardiovaskuläre Risikofaktoren. Das Screening erfolgte mit ABI-Messung und Duplexsonographie als monozentrische, prospektive, einarmige Studie innerhalb eines Jahres. Endpunkte waren die Prävalenz von Risikofaktoren, pathologische und/oder behandlungsbedürftige Befunde.
### Ergebnisse
Es nahmen 391 Personen teil, bei 36,0 % bestand mindestens ein kardiovaskulärer Risikofaktor, 35,5 % wiesen zwei und 14,4 % drei oder mehr auf. Aus der Sonographie der Karotiden resultierte bei 9 % ein kontrollbedürftiger Befund mit einer < $50\%$igen bis > $75\%$igen Stenose oder eines Verschlusses. Ein AAA mit einem Durchmesser von 3,0–4,5 cm wurde bei 0,9 % nachgewiesen, ein pathologischer ABI < 0,9 oder > 1,3 bei 12,3 %. Bei 17,0 % bestand befundbasiert die Indikation einer Pharmakotherapie, bei keinem die einer Operation.
### Diskussion
Es zeigte sich die Durchführbarkeit eines Screeningprogramms auf das Vorliegen einer Karotisstenose, einer pAVK und eines AAA bei definierten Risikopersonen. Es wurden kaum behandlungsbedürftige Gefäßpathologien im Einzugsgebiet der Klinik nachgewiesen, sodass ein Einsatz des Programms in Deutschland in dieser Form derzeit auf Basis der Daten nicht empfohlen werden kann.
## Background
Cardiovascular diseases are the main cause of death in Europe with a relevant socioeconomic burden. A screening program for vascular diseases in asymptomatic persons with a defined risk constellation can lead to an early diagnosis.
## Objective
The study examined a screening program for carotid stenosis, peripheral arterial occlusive disease (PAOD) and abdominal aortic aneurysms (AAA) in persons without any known vascular disease with respect to demographic data, risk factors, pre-existing conditions, medication intake, detection of pathological findings and/or findings requiring treatment.
## Material and methods
Test subjects were invited using various information material and filled in a questionnaire on cardiovascular risk factors. The screening took place with measurement of the ABI and duplex sonography as a monocentric prospective single arm study within 1 year. Endpoints were the prevalence of risk factors and pathological and/or results requiring treatment.
## Results
A total of 391 persons participated, $36\%$ presented with at least 1 cardiovascular risk factor, $35.5\%$ with 2 and $14.4\%$ with 3 or more. The sonography showed results requiring control with a carotid stenosis of < 50–> $75\%$ or occlusion in $9\%$. An AAA with a diameter of 3.0–4.5 cm was diagnosed in $0.9\%$ and a pathological ABI < 0.9 or > 1.3 in $12.3\%$. The indications for a pharmacotherapy were found in $17\%$ and no operation was recommended.
## Conclusion
The practicability of a screening program for carotid stenosis, PAOD and AAA of a defined risk population was shown. Vascular pathologies that required treatment were hardly found in the catchment area of the hospital. Consequently, the implementation of this screening program in Germany based on the collected data cannot currently be recommended in this form.
## Hintergrund und Fragestellung
Kardiovaskuläre Erkrankungen stellen in Europa die häufigste Todesursache mit etwa 4 Mio. Todesfällen (43 % aller Todesfälle) im Jahr 2016 dar [5, 10, 24, 25]. Im Jahr 2019 starben in *Deutschland etwa* 44.000 Menschen an einem Myokardinfarkt, dem Endstadium der koronaren Herzkrankheit [19]. Zusätzlich zur Mortalität sind kardiovaskuläre Erkrankungen aufgrund der hohen Behandlungskosten sowie der Morbidität mit Funktionseinschränkungen ein relevantes sozioökonomisches Problem. Jährlich erleiden mehr als 200.000 Menschen in Deutschland einen Apoplex, ursächlich hierfür kann eine Stenose der A. carotis interna sein. Auch zeigte sich in den letzten Jahrzehnten eine stetig relevante Zunahme der Prävalenz der peripheren arteriellen Verschlusskrankheit (pAVK), wobei von einer progredienten Entwicklung auszugehen ist [17]. In der 2001 in Deutschland durchgeführten Querschnittstudie „get ABI“ fanden sich bei nahezu 20 % der Teilnehmer nach Vollendung des 65. Lebensjahres eine pAVK [12]. Klinische Manifestationen der pAVK sind belastungsabhängige Schmerzen der unteren Extremität sowie trophische Störungen und Ulzerationen mit einer Gefährdung der Extremität und des Lebens durch mögliche septische Komplikationen. Eine weitere potenziell lebensbedrohliche kardiovaskuläre Erkrankung ist das abdominelle Aortenaneurysma (AAA). Seine Prävalenz wird in Registerstudien mit 4–8 % bei über 65-jährigen Männern und 0,5–1,5 % bei gleichaltrigen Frauen angegeben [9]. Das vom Maximaldurchmesser abhängige Rupturrisiko beträgt 3 % pro Jahr bei einem Durchmesser von 5 cm und 6 % pro Jahr bei einem Durchmesser von mehr als 7 cm [23]. Eine Ruptur ist trotz Fortschritten in den Behandlungsmethoden nach wie vor mit einer Gesamtletalität außer- und innerhalb des Krankenhauses von deutlich über 50 % behaftet.
Ähnliche Risikofaktoren begünstigen das Auftreten der beschriebenen kardiovaskulären Erkrankungen. Diese verlaufen in frühen Stadien meist asymptomatisch und werden oft erst spät diagnostiziert. Eine frühe Diagnosestellung ist essenziell, da diese zu deutlich besseren Ergebnissen führt als eine spätere oder eine Behandlung in einer Notfallsituation [5, 26]. Beispielhaft sind hier die Karotisendarteriektomie bei asymptomatischer Stenose zur Apoplexprophylaxe oder die elektive Ausschaltung eines AAA zur Rupturprophylaxe ebenso wie die pharmakologische Therapie von Risikofaktoren der Arteriosklerose zur Sekundärprophylaxe genannt [13, 18].
Aus dem geschilderten epidemiologischen Hintergrund ergibt sich die Annahme, dass ein Screening auf Gefäßerkrankungen bei asymptomatischen Personen, die eine definierte Risikokonstellation aufweisen, sinnvoll sein könnte. Gemäß Beschluss des Gemeinsamen Bundesausschlusses 2016 und Gesundheitsuntersuchungsrichtlinie 2020 haben gesetzlich krankenversicherte Männer ab 65 Jahren Anspruch auf eine einmalige Ultraschalluntersuchung der Bauchaorta hinsichtlich eines Aneurysmas [6, 7]. Ein Screening für andere Bevölkerungsgruppen wie z. B. weibliche Personen sowie ein Screening auf Stenosen der A. carotis oder eine pAVK sind nicht vorgesehen und somit für Versicherte in der gesetzlichen Krankenversicherung nicht zugänglich. Von privaten Krankenversicherungen werden Kosten dafür in der Regel nicht übernommen. Ein solches Screening könnte nichtinvasiv mittels Doppler‑/Duplexsonographie für die Karotisstenose sowie durch Bestimmung des Knöchel-Arm-Indexes (ABI) für die pAVK erfolgen. Beide Verfahren liefern wie auch das Ultraschallscreening auf das Vorliegen eines AAA in der Hand des erfahrenen Untersuchers schnell valide Aussagen über das Vorliegen der Erkrankungen Karotisstenose bzw. pAVK [14, 22]. Herausforderungen für ein solches Programm sind die Definition einer Population mit relevantem Risiko für ein Vorliegen der genannten Erkrankungen und es muss gewährleistet sein, dass möglichst viele Personen in dieser Population an der Untersuchung teilnehmen. In den USA wurde Anfang der 2000er-Jahre das Programm Dare to C.A.R.E, das ein Screening auf Karotisstenosen, AAA, pAVK und Nierenarterienstenosen einschloss, durchgeführt. Es zeigte sich hier ein relevanter Anteil behandlungsbedürftiger Gefäßerkrankungen [21].
Die vorliegende Studie soll ein Screeningprogramm auf das Vorliegen von Karotisstenosen, einer pAVK und eines AAA bei Menschen ohne bekannte arterielle Gefäßerkrankungen hinsichtlich Teilnahme, Detektion behandlungsbedürftiger Befunde und möglicher Weiterbehandlung evaluieren.
## Studiendesign und Untersuchungsmethoden
Das Screeningprogramm wurde in einer monozentrischen, prospektiven, einarmigen Studie explorativ evaluiert.
## Einschluss- und Zielkriterien
Die Einschluss- und Zielkriterien finden sich in Tab. 1.Primäres ZielkriteriumPrävalenz mindestens einer relevanten Gefäßerkrankung: Stenose der A. carotis interna ≥ 60 % gemäß NASCET-Kriterien [16] oder pAVK mit ABI ≤ 0,9 oder Erweiterung der abdominellen Aorta auf einen maximalen Transversaldurchmesser von ≥ 3 cmSekundäre ZielkriterienAlterGeschlechtGrad einer Karotisstenose gemäß NASCET-KriterienABIMaximaler Transversaldurchmesser der abdominellen AortaPrävalenz kardiovaskulärer RisikofaktorenHäufigkeit von Eingriffen infolge der Screeningbefunde: Start einer pharmakologischen Therapie, Operation bzw. endovaskuläre Therapie der Karotisstenose, des AAA und/oder aufgrund einer pAVK, kardiologische Intervention (z. B. Koronarangiographie)EinschlusskriterienEinwilligung in die Studienteilnahme nach ausführlicher Aufklärung bei gegebener AufklärungsfähigkeitKeine bekannte arterielle Gefäßerkrankung (Karotisstenose, AAA, pAVK)Kein gefäßspezifisches Screening innerhalb von 5 Jahren vor StudienteilnahmeAlter ≥ 60 Jahre oder Alter ≥ 50 Jahre und Vorliegen mindestens eines der folgenden anamnestischen Risikofaktoren: Hyperlipidämie, arterielle Hypertonie, aktiver Nikotinabusus, positive Familienanamnese für kardiovaskuläre Erkrankungen oder Alter ≥ 40 Jahre und Vorliegen eines Diabetes mellitusNASCET „North American Symptomatic Carotid Endarterectomy Trial“, pAVK periphere arterielle Verschlusskrankheit, ABI Ancle-Brachia-Index, AAA abdominelles Aortenaneurysma
## Screeningprogramm
Das Screeningprogramm stellt eine Adaptation des ursprünglich in den USA etablierten Dare-to‑C.A.R.E.-Programms dar [21]. Von jedem Studienteilnehmer wurden mithilfe eines strukturierten Fragebogens (Abb. 1) relevante anamnestische Vorbefunde sowie demografische Daten erhoben, dann erfolgte das aus drei Komponenten bestehende Screening. Durch eine standardisierte B‑Bild-Sonographie sowie Doppler- und farbkodierte Duplexsonographie wurden beide Aa. carotides mit einem Linearschallkopf mit 5–7 MHz von DEGUM-Stufe I bzw. DEGUM-Stufe II (Deutsche Gesellschaft für Ultraschall in der Medizin) zertifizierten Fachärzten untersucht und der Grad einer eventuell vorliegenden Stenose gemäß der NASCET(„North American Symptomatic Carotid Endarterectomy Trial“)-Kriterien quantifiziert [2]. Die abdominelle Aorta wurde ebenfalls mittels B‑Bild-Sonographie mit einem Konvexschallkopf mit 2–8 MHz beurteilt und ihr maximaler Transversaldurchmesser bestimmt. Die ABI-Bestimmung erfolgte durch Blutdruckmessung und Tasten von Pulsen bzw. Ableitung eines CW-Dopplersignals mittels Stiftsonde an beiden oberen und unteren Extremitäten.
Die erhobenen Befunde wurden sowohl direkt mit dem Studienteilnehmer besprochen als auch durch einen Bericht, der weitere Behandlungsempfehlungen einschloss, an den Haus- bzw. Facharzt übermittelt. Eventuell erforderliche weitere diagnostische oder therapeutische Maßnahmen wurden durch den behandelnden Arzt in Absprache mit dem Studienteilnehmer geplant. Die Durchführung solcher Maßnahmen war nicht unmittelbar Gegenstand der Studie. Falls sich bei den Screeninguntersuchungen dringend behandlungsbedürftige Befunde ergaben, so wurde neben einer dezidierten Aufklärung des Studienteilnehmers telefonisch Kontakt mit dem primär behandelnden Arzt aufgenommen und ggf. auch eine sofortige stationäre Weiterbehandlung veranlasst.
## Rekrutierung und Aufklärung
Die Rekrutierung der Studienteilnehmer gelang primär über allgemein- und fachärztliche Praxen. Durch Poster und Informationsbroschüren für das Studienprojekt wurden die Praxen über die Möglichkeit der Studienteilnahme ihrer Patienten informiert. Zusätzlich wurden potenzielle Studienteilnehmer durch öffentliche Informationsveranstaltungen, Pressemitteilungen und soziale Medien zur Teilnahme eingeladen. Auch innerhalb des Universitätsklinikums Heidelberg wurden Informationsbroschüren für Patienten, Angehörige, Besucher und Mitarbeiter ausgelegt. Die ausführliche Aufklärung über Ziele und Ablauf der Studie erfolgte zum Zeitpunkt der Vorstellung im Studienzentrum vor Durchführung der studienspezifischen Maßnahmen.
## Statistische Verfahren
Aufgrund des explorativen Studiencharakters wurden die Zielgrößen rein deskriptiv ausgewertet. Für normal verteilte Zielkriterien wurden Mittelwert, Median sowie Standardabweichung und 95 %-Konfidenzintervalle bestimmt. Für nicht normalverteilte kontinuierliche Zielkriterien wurden Median, Minimum, Maximum sowie Interquartilsabstand, für diskret verteilte Zielkriterien Häufigkeiten mit 95 %-Konfidenzintervallen ermittelt. Die Prävalenzen von Erkrankungen und Risikofaktoren bzw. Häufigkeiten von Eingriffen wurden errechnet durch die Anzahl der Studienteilnehmer mit dem entsprechenden Merkmal dividiert durch die Gesamtzahl dieser. Da es sich um eine explorative Studie handelte, wurde auf eine spezifische Hypothese mit einhergehender Fallzahlberechnung verzichtet. Der Untersuchungszeitraum wurde auf ein Jahr limitiert.
## Rechtliche und ethische Aspekte
Die Untersuchung wurde in Übereinstimmung mit der Deklaration von Helsinki in ihrer aktuellen Fassung durchgeführt. Vor Studienbeginn wurde ein zustimmendes Votum der Ethikkommission der Medizinischen Fakultät Heidelberg eingeholt (S-$\frac{719}{2017}$).
## Studienteilnehmer
Neben den allgemeinen Informationsmaßnahmen in den Medien sowie innerhalb des Universitätsklinikums Heidelberg wurden 1256 niedergelassene Fachärzte (417 Allgemeinmediziner, 84 Urologen, 755 Internisten) angeschrieben. An diese wurden 15.000 Flyer zur Auslage in den Praxen und Weitergabe an Patienten versandt. Im Studienzeitraum vereinbarten 439 Personen einen Termin in der Studiensprechstunde. Von diesen wurden 391 Personen (89,1 %) in die Studie eingeschlossen. 48 Personen (10,9 %) erschienen entweder nicht zum vereinbarten Termin oder erfüllten die Einschlusskriterien nicht. Der Hauptanteil der Studienteilnehmer folgte der Einladung über eine direkte Empfehlung bzw. den Erhalt von Informationsmaterial beim Haus- oder niedergelassenen Facharzt, wobei der jeweilige Rekrutierungsweg in Tab. 2 zusammengefasst ist. Zuweisungs- bzw. RekrutierungswegAnzahlAnteil (%)Haus-/niedergelassener Facharzt$\frac{183}{39146}$,8Mundpropaganda$\frac{94}{39124}$,0Spezifische Infomedien (Presse)$\frac{20}{3915}$,1Sonstige (Krankenhausmitarbeiter, Internet, andere)$\frac{28}{3917}$,1Keine Angabe$\frac{66}{39116}$,9 Die demografischen Merkmale der Studienteilnehmer sowie die jeweiligen Einschlusskriterien sind in Tab. 3 aufgeführt. GeschlechtAnzahl (Anteil)Weiblich$\frac{227}{391}$ (58,1 %)Männlich$\frac{164}{391}$ (41,9 %)AlterMittelwert65,1 JahreSpannweite40–84 JahreEinschlusskriterien40–49 Jahre, Diabetes mellitus$\frac{4}{391}$ (1,0 %)50–59 Jahre, ein kardiovaskulärer Risikofaktor$\frac{110}{391}$ (28,1 %)≥ 60 Jahre$\frac{272}{391}$ (69,5 %)Einschlusskriterien formal nicht erfüllt$\frac{5}{391}$ (1,3 %) Bei 36,0 % der Studienteilnehmer bestand eigenanamnestisch mindestens ein kardiovaskulärer Risikofaktor, 35,5 % wiesen zwei und 14,4 % drei oder mehr Risikofaktoren auf (Tab. 4).Anzahl kardiovaskulärer RisikofaktorenAnzahl (Anteil)055 (14,1 %)1140 (36,0 %)2138 (35,5 %)347 (12,1 %)49 (2,3 %) Die Häufigkeiten der in den Screeninguntersuchungen erhobenen Befunde zeigt Tab. 5. Für alle Untersuchungen fällt ein geringer Anteil weiter abklärungsbedürftiger Befunde auf. Aus der Sonographie der Karotiden resultierte bei 9 % der Teilnehmer ein kontrollbedürftiger Befund, bei keinem Teilnehmer eine Pathologie, bei der eine starke Empfehlung zu einer operativen oder interventionellen Therapie bestand. Bei 2 Teilnehmern zeigte sich mit einer Karotisstenose von 60–75 % ein Befund, bei dem eine operative oder interventionelle Behandlung in Erwägung zu ziehen war. Bei etwa 1 % der Teilnehmer fand sich ein AAA, für das Kontrollen empfohlen werden mussten. Ein AAA mit einem Durchmesser, bei dem gemäß der gültigen deutschen S3-Leitlinie eine operative oder endovaskuläre Behandlung empfohlen wurde, lag bei keinem Teilnehmer vor. Für den ABI wurde bei etwa 12,3 % der Teilnehmer eine Pathologie erhoben, wobei zu fast gleichen Teilen falsch hohe Indizes als Zeichen einer Mediasklerose und eines möglichen *Diabetes mellitus* als auch erniedrigte Indizes als Zeichen einer pAVK auftraten. Bei keinem Teilnehmer wurde ein Eingriff unmittelbar basierend auf den Screeningbefunden empfohlen. Bei 2 Studienteilnehmern mit einer Karotisstenose von 60–75 % Stenosegrad wurde angeraten, eine Intervention bzw. Operation in Erwägung zu ziehen. Bei 67 Studienteilnehmern (17,0 %) war der Beginn einer Pharmakotherapie indiziert. BefundHäufigkeitArteria carotis interna rechts/linksKeine Stenose$\frac{705}{782}$ (90,2 %)Stenose < 50 % (Kontrolle empfohlen)$\frac{61}{782}$ (7,8 %)Stenose 50–60 % (Kontrolle empfohlen)$\frac{5}{782}$ (0,6 %)Stenose 60–75 % (Intervention zu erwägen)$\frac{2}{782}$ (0,3 %)Stenose > 75 % (Intervention empfohlen)$\frac{0}{782}$ (0 %)Verschluss (angiologische Abklärung empfohlen)$\frac{2}{782}$ (0,3 %)Kein Befund erhoben$\frac{7}{782}$ (0,9 %)Maximaler Transversaldurchmesser der Aorta abdominalis< 3 cm (kein Aneurysma)$\frac{387}{391}$ (99,0 %)3,0–3,9 cm (Kontrolle innerhalb 24 Monaten)$\frac{3}{391}$ (0,8 %)4,0–4,5 cm (Kontrolle innerhalb 12 Monaten)$\frac{1}{391}$ (0,3 %)> 4,5 cm (Kontrolle innerhalb 6 Monaten)$\frac{0}{391}$ (0 %)> 5 cm (Intervention zu erwägen)$\frac{0}{391}$ (0 %)ABIBeide Werte 0,9–1,3 (nicht pathologisch)$\frac{111}{391}$ (85,2 %)Mindestens ein Wert 0,75–0,89 (leicht erniedrigt, angiologische Abklärung empfohlen)$\frac{23}{391}$ (5,9 %)Mindestens ein Wert 0,5–0,74 (deutlich erniedrigt, angiologische Abklärung empfohlen)$\frac{5}{391}$ (1,3 %)Mindestens ein Wert < 0,5 (extrem erniedrigt, angiologische Abklärung empfohlen)$\frac{0}{391}$ (0 %)> 1,3 (falsch hoch, Verdacht auf Mediasklerose, Abklärung hinsichtlich *Diabetes mellitus* empfohlen)$\frac{20}{391}$ (5,1 %)Kein Befund erhoben$\frac{10}{391}$ (2,6 %)ABI Ancle-Brachial-Index
## Diskussion
Die vorliegende Studie demonstriert die Durchführbarkeit eines Screenings auf Gefäßerkrankungen in Deutschland mit einer auf die Screeningkapazität bezogenen hohen Nachfrage in einer asymptomatischen Population, die anhand spezifischer Risikokriterien definiert wird. Eine genaue Responserate ließ sich nicht feststellen, da aufgrund der spezifischen Informationsmaßnahmen über niedergelassene Ärzte sowie Auslagen im Klinikum und persönliche Kontakte die exakte Anzahl der Personen, die eine Einladung zur Studienteilnahme erhalten hatten, nicht zu ermitteln war. Die geplante Anzahl an Teilnehmern wurde nahezu vollständig erreicht, wobei diese eher durch die Sprechstundenkapazitäten und die Laufzeit der Untersuchung als durch die Nachfrage limitiert wurde. Im *Vergleich zum* initialen Dare-to‑C.A.R.E.-Screeningprogramm in den USA, welches 2007 im ersten Jahr mit 125 Probanden begann, wurde hier eine deutlich höhere Anzahl erreicht [21]. Größere Teilnehmerzahlen wären ähnlich wie bei anderen Erhebungen bei Verlängerung des Untersuchungszeitraumes zu erwarten [5]. Da die Studie einen explorativen Charakter hatte, wurde keine *Fallzahl a* priori definiert. Basierend auf der in die Auswertungen einbezogenen Anzahl an Studienteilnehmern lassen sich nichtsdestotrotz Aussagen hinsichtlich Teilnahmebereitschaft, Profil der Teilnehmer sowie primärer und sekundärer Zielgrößen der Studie treffen.
Inwiefern die Population der Studienteilnehmer repräsentativ für die Bevölkerung der Untersuchungsregion ist, kann mit den vorliegenden Daten nicht beantwortet werden, da keine Informationen zur Grundgesamtheit der eingeladenen Personen bzw. zur Grundgesamtheit der Bevölkerung in der Untersuchungsregion vorliegen. Durch die Einladung potenzieller Studienteilnehmer über Haus- und Facharztpraxen sollten möglichst zielgerichtet Menschen erreicht werden, die die Einschlusskriterien für das Screeningprogramm hinsichtlich Alter und kardiovaskulärer Risikofaktoren erfüllen. In der überwiegenden Mehrzahl der Fälle stellen Teilnehmer an Screeningprogrammen jedoch eine spezifische Selektion der eingeladenen Population dar, da die Bereitschaft zur Teilnahme an Screeningprogrammen regelhaft mit demografischen und sozioökonomischen Charakteristika sowie dem Risikoprofil der einzelnen Screeningteilnehmer assoziiert ist [1, 8].
Sowohl Einladungen über Arztpraxen als auch Mundpropaganda waren für die Rekrutierung maßgeblich. Die vorgegebenen Einschlusskriterien prägten die Charakteristika der Studienteilnehmer, Frauen waren ähnlich wie in anderen Screeningprogrammen auf Gefäßerkrankungen überproportional häufig repräsentiert [3]. Unter den Teilnehmern befanden sich kaum Diabetiker zwischen 40 und 49 Jahren, die Mehrzahl der Teilnehmer erfüllte das Einschlusskriterium des Alters von mindestens 60 Jahren.
Die niedrige Prävalenz von Gefäßerkrankungen bzw. -veränderungen bei den Studienteilnehmern erscheint überraschend, da Ergebnisse des Dare-to-C.A.R.E.-Programms und anderer kombinierter Gefäßscreenings aus anderen Ländern eine deutlich höhere Prävalenz abklärungs- und interventionsbedürftiger Befunde aufwiesen [21, 26]. Ein Grund hierfür könnte das unterschiedliche spezifische Risikoprofil der gescreenten Populationen darstellen. So waren in der ursprünglichen Kohorte des Dare-to‑C.A.R.E.-Programms in den USA 59 % der Personen aktive oder ehemalige Raucher, 46,7 % litten an einer arteriellen Hypertonie, 49,4 % an einer Hyperlipidämie, 11,9 % der Teilnehmer hatten anamnestisch einen Myokardinfarkt erlitten. Dies stellt eine deutlich höhere Prävalenz kardiovaskulärer Risikofaktoren bzw. Vorerkrankungen als in der vorliegenden Studienpopulation dar. Die überproportional häufige Teilnahme von Frauen an unserem Screeningprogramm könnte ein weiterer Grund für die niedrige Prävalenz weiter abklärungs- und behandlungsbedürftiger Befunde sein. Da bei Frauen arteriosklerotische Veränderungen wie das AAA seltener und erst in höherem Alter auftreten, wird für sie in der Regel eine Teilnahme an von Gesundheitssystemen getragenen AAA-Screenings nicht empfohlen. Auch *Studien zum* Screening mit einem hohen Anteil an Frauen in der untersuchten Population sind selten [11, 13, 27]. Darüber hinaus könnte eine im Länder- und Systemvergleich unterschiedliche Bereitschaft von Menschen mit einem besonders hohen oder niedrigen Risiko für Gefäßerkrankungen, an Screeningprogrammen teilzunehmen, eine Rolle spielen [28]. Auch die Struktur der primären Gesundheitsversorgung differiert stark zwischen verschiedenen Systemen. In Deutschland ist die haus- und fachärztliche Versorgung im ambulanten Sektor sehr umfassend ausgeprägt, sodass bei vielen Personen ein informales Screening auf kardiovaskuläre Risikofaktoren und in gewissem Umfang auch bei asymptomatischen Personen auf das Vorliegen von Gefäßerkrankungen durchgeführt wird, die Diagnosestellung somit außerhalb eines institutionalisierten Screeningprogramms erfolgt. Es existieren auch innerhalb Deutschlands geografische Unterschiede u. a. in Bezug auf eine geringere Ärztedichte besonders in ländlichen Gebieten sowie im Vergleich der neuen und alten Bundesländer [4].
Andere nichtselektive *Screeningprogramme aus* den USA und europäischen Ländern zeigten hingegen Ergebnisse, die den unsrigen wesentlich ähnlicher waren. So wurden in Kalifornien in einem Zeitraum von 18 Monaten 1719 Personen auf das Vorliegen eines AAA, einer Karotisstenose und einer pAVK gescreent. Hiervon wiesen 0,3 % eine > 60 %ige Stenose, 1 % ein behandlungsbedürftiges AAA und 5,8 % einen ABI < 0,9 auf [3]. Die Prävalenz eines maximalen transversalen Aortendurchmessers > 3 cm bei erstmals zum Screening eingeladenen Männern in England und Schweden betrug 1,3–1,7 % und ist somit ebenfalls mit den Ergebnissen der vorliegenden Studie vergleichbar [11, 13, 27].
Eines der Ziele des Screeningprogramms stellte die Patientenbindung an die durchführende Einrichtung dar. Erhobene behandlungsbedürftige Befunde sollten mit den Hausärzten der Teilnehmer rückgekoppelt werden. Selbstverständlich hätte keine Verpflichtung bestanden, den Patienten dann dem Studienzentrum zur regulären Behandlung zuzuweisen. Es war aber davon auszugehen, dass dies für die meisten Patienten und Hausärzte die primäre Anlaufstelle ist. Letztendlich wurde jedoch in der gesamten Studienpopulation lediglich bei 2 Teilnehmern ein Befund (Stenose der A. carotis interna) festgestellt, bei dem eine Intervention zumindest in Erwägung zu ziehen war. Zusammenfassend muss konstatiert werden, dass das untersuchte Screeningprogramm in seiner vorliegenden Form im deutschen Gesundheitssystem keine Portalfunktion für die durchführende Klinik erfüllt. Inwiefern Menschen mit einer zu erwartenden höheren Prävalenz an Gefäßerkrankungen durch Infomaßnahmen erreicht und besser zur Wahrnehmung von Screeninguntersuchungen motiviert werden könnten, müssten weiterführende Studien klären.
Screeningprogramme werden häufig kontrovers beurteilt. Ihr Nutzen muss sich an patientenrelevanten Endpunkten wie beispielsweise der Reduktion der Gesamtmorbidität messen lassen. Sie sollten nicht ungerichtet in einer unselektionierten Population angewendet werden [15, 20, 29]. Die vorliegende Studie erlaubt keine Quantifizierung des potenziellen Nutzens des eingesetzten Gefäßscreeningprogramms. Vielmehr sollte in einem explorativen Design die Machbarkeit des Programms evaluiert werden und durch explorative Analysen Daten zu Charakteristika der Teilnehmer des Programms sowie erste Informationen zur Prävalenz der als Zielkriterien definierten Risikofaktoren und Gefäßerkrankungen gewonnen werden.
Der Nachweis einer Erkrankung im Rahmen eines Screenings kann zu psychischen Belastungen in Form von Krankheitsängsten und existenziellen Sorgen führen [11]. Dieser Aspekt konnte in unserer Studie durch die erhobenen Daten nicht näher betrachtet werden, muss aber bei Screeningprogrammen immer Beachtung finden und unter Umständen durch spezifische Aufklärungs- und Betreuungsmaßnahmen adressiert werden.
## Schlussfolgerung
Zusammenfassend zeigte diese Studie die Durchführbarkeit eines Screeningprogramms auf das Vorliegen einer Karotisstenose, einer pAVK und eines AAA bei Personen, die über spezifische Infomaßnahmen in Haus- und Facharztpraxen, im Krankenhaus und in konventionellen und sozialen Medien eingeladen wurden. Im Rahmen des Programms wurden kaum behandlungsbedürftige Gefäßpathologien nachgewiesen, sodass ein flächendeckender Einsatz in Deutschland in der vorliegenden Form auf Basis der verfügbaren Daten derzeit nicht empfohlen werden kann.
## Funding
Die Durchführung der Studie wurde auf institutioneller Ebene finanziell und mit Sachmitteln durch die Medtronic GmbH unterstützt. Die Autoren erhielten kein persönliches Honorar bzw. keine Vergütung.
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|
---
title: Validation of a Farsi version of the Eating Pathology Symptoms Inventory (F-EPSI)
among Iranian adolescents
authors:
- Reza N. Sahlan
- Jessica F. Saunders
- Ellen E. Fitzsimmons-Craft
journal: Eating and Weight Disorders
year: 2023
pmcid: PMC10042940
doi: 10.1007/s40519-023-01561-4
license: CC BY 4.0
---
# Validation of a Farsi version of the Eating Pathology Symptoms Inventory (F-EPSI) among Iranian adolescents
## Abstract
### Purpose
Limited research has validated eating pathology assessments in Iranian adolescent boys and girls. In particular, the measures that have been validated do not capture both boys’ and girls’ eating behaviors in adolescence. The purpose of the current study was to validate a Farsi version of the Eating Pathology Symptoms Inventory (F-EPSI) for use in Iranian adolescents.
### Methods
Participants ($$n = 913$$; $85.3\%$ girls) were adolescents who completed a battery of questionnaires including the F-EPSI. In addition, F-EPSI data for Iranian adolescents were compared with those of previously published data of adult Iranian college students.
### Results
Confirmatory Factor Analysis (CFA) indicated that the F-EPSI had an acceptable fit to the data and supported the eight-factor model. The scale was invariant by gender, weight status, eating disorder, and age groups. Boys reported higher scores than girls on the Excessive Exercise, Muscle Building, Body Dissatisfaction, and Binge Eating subscales. Adolescents with higher weight and eating disorder symptoms endorsed higher scores on the F-EPSI subscales. Older adolescents and adults reported higher scores than younger adolescents and adolescents, respectively. Adolescents had higher scores than adults on Restricting and Excessive Exercise subscales. The F-EPSI demonstrated good convergent validity through correlations with other eating pathology symptoms. The F-EPSI subscales were associated with depression and body mass index (zBMI) in expected directions that indicate criterion validity of the scale.
### Conclusions
Findings suggest that the F-EPSI is a reliable and valid measure in Iranian non-clinical adolescents. The F-EPSI will enable researchers to examine a broad array of eating pathology symptoms in adolescents for whom *Farsi is* their official language.
### Level of evidence
Level V; Cross-sectional descriptive study.
## Introduction
Studies reported that eating pathology is common among Iranian adolescents, with potentially important gender differences [1, 2]. For example, $20\%$ of adolescents reported recurrent (≥ 4 times during the past 28 days) binge eating and 4–$7\%$ reported current purging behaviors (self-induced vomiting, laxative misuse) [2]. In addition, levels of dietary restriction and cognitive features of eating pathology1 are higher in adolescent girls than boys [1, 2]. However, binge eating, purging behaviors, and excessive exercise levels have been found to be comparable across boys and girls [1], as has eating pathology-related impairment (i.e., $17.4\%$; [3]). Therefore, it is important to have measures that are psychometrically valid and equivalent across genders to improve our understanding of eating pathology in adolescents.
To our knowledge, there are three Farsi scales assessing eating pathology among Iranian preadolescents and adolescents: the Eating Disorder Examination Questionnaire (EDE-Q; [1, 4]), Eating Attitudes Test-26 (EAT-26; [5]), and Children EAT-20 (ChEAT-2; [6]). However, there are some limitations of existing scales in Iran. First, the original factor structure of the EDE-Q has not yet been examined in Iran. Second, the EDE-Q, EAT-26, and ChEAT-20 assess eating pathology symptoms (e.g., thinness/dietary restraint) that are common in girls and fail to assess those that are more common in boys (e.g., muscularity/exercise; [7]). Third, the EDE-Q, EAT-26, and ChEAT-20 assess behavioral features of eating pathology using single items. Importantly, with respect to the EDE-Q, behavioral features of eating pathology (i.e., binge eating, purging, excessive exercise) are assessed using count variables that do not allow researchers to examine their factor structure [8, 9]. Thus, authors have only been able to examine the factor structure of the four EDE-Q subscales [8]. Therefore, these findings indicate the need for measures that assess a broader spectrum of eating pathology to be administered in Iranian adolescents.
The Eating Pathology Symptoms Inventory (EPSI) was validated among adolescent boys and girls (age 13 through 17) in the United States (US) to address the above-mentioned limitations by capturing multiple dimensions of eating pathology cognitions and behaviors, including body dissatisfaction, binge eating, cognitive restraint, purging, restricting, and excessive exercise [10]. The EPSI has demonstrated a robust six-factor structure among adolescents in the US [10]. In addition, there have been some gender differences identified among adolescents [10]; however, measurement invariance across genders was not established. Despite this limitation, the scale was invariant across age groups (i.e., younger adolescents vs. older adolescents and adolescents vs. adults) [10]. There have been some age group (i.e., adolescents vs. adults) differences on the EPSI subscales. The EPSI was invariant across younger vs. older adolescents; however, mean level differences were not examined across these two age groups. Mond et al. [ 11] reported that older adolescents reported higher eating pathology symptoms (assessed through the EDE-Q; [12] than younger adolescents. Relatedly, Sahlan et al. [ 6] reported that age had a positive association with eating pathology in preadolescents. Similarly, Hatami et al. [ 13] found that increasing body dissatisfaction was associated with increasing age and weight status in Iranian adolescents; however, the measurement invariance of this scale was not established by either age or weight status. To our knowledge, there are three studies in which eating pathology assessments such as Loss of Control Over Eating Scale (LOCES; [14]), Clinical Impairment Assessment (CIA; [3]), and ChEAT-20 [6] were invariant by weight status and age among Iranian preadolescents and adolescents.
Regarding the importance of the EPSI within an Iranian context, Richson et al.’ [ 10] study had two main limitations. Given that two EPSI subscales, muscle building and negative attitudes toward obesity, were omitted by the creators of the recovery record mobile phone application, Richson et al. [ 10] did not include those subscales upon assessing adolescents’ eating pathology. However, muscle building and negative attitudes toward obesity are of importance in adolescents. Ricciardelli and McCabe [15] reported that muscularity is of importance while examining eating pathology in adolescents. Relatedly, a few studies demonstrated that muscularity was associated with eating pathology symptoms in adolescent boys and girls and boys endorsed higher muscularity than girls [16, 17]. In line with Western adolescents, it has been found that body-related physical functioning was one of the main physical components of perceived body image among adolescents in Iran [18]. For example, body change behavior, expressed through drive for muscularity, was common among adolescents ($41.1\%$) in Iran [19], thus demonstrating the effects of body image concerns on muscularity among this population [20]. In addition, Bahrami et al. [ 21] reported that 5–$10\%$ of adolescents in Iran consumed dietary supplements meant to reduce weight. These findings may indicate the importance of muscularity and related components that could contribute to eating pathology which is common among this population in Iran. In accordance with muscularity, $43\%$ of Western adolescents found that it is disgusting when a fat person wears a bathing suit at the beach, which indicates the importance of negative attitudes toward obesity in adolescence [22]. The negative attitudes toward obesity were not related to body mass index (BMI) in adolescents and adolescent boys reported higher negative attitudes toward obesity than adolescent girls [22]. In a qualitative study, Kavehfarsani et al. [ 23] found that adolescent boys and girls in Iran had negative attitudes (i.e., ridicule, etc.) toward individuals with higher weight. Altogether, it may be crucial to examine both muscularity and negative attitudes toward obesity subscales, along with other subscales upon testing the EPSI’s factor structure in adolescents.
To our knowledge, there is only one study in which the EPSI in Farsi version (F-EPSI) was administered to Iranian college men and women [24]. The results indicate that the EPSI subscales demonstrate a robust eight-factor structure in college students [24]. In addition, the EPSI subscales were correlated with loss of control over eating, clinical impairment, and BMI, in support of the EPSI’s convergent validity in this population [24]. However, these findings are not generalizable to adolescents. Notably, in spite of the prevalence of eating pathology among adolescents and adults [2], it is unclear whether adults vs. adolescents and individuals with and without eating disorder symptoms would conceptualize eating pathology symptoms in the same way. Moreover, two studies in Iranian adolescents [14] and college students [24] reported that individuals’ weight status related to eating pathology symptoms and individuals with higher weight reported higher loss of control eating [14]. However, with respect to other eating pathology symptoms, little is known within an Iranian literature. There is one study in which Forbush et al. [ 25] found that the EPSI was invariant by weight status among the US adults. Taken together, examining the measurement invariance of the EPSI subscales by age (i.e., adolescents vs. adults), BMI, and eating disorder groups is warranted. Notably, there have been some differences across groups while assessing eating pathology. However, it is imperative to know whether the scales perform similarly across groups and that observed group differences are due to true discrepancies and not measurement error [26]. More precisely, having a consistent measure of eating pathology across the lifespan would better characterize developmental changes in eating pathology in both research and clinical settings. Therefore, measurement invariance would be warranted to respond to these gaps. Given the strong psychometric properties of the EPSI and its potential to capture diverse eating pathology symptoms in both boys and girls, it would be beneficial to use this assessment to examine eating pathology in Iranian adolescents. The purpose of the current study was to examine the factor structure, measurement invariance, and other psychometric properties (i.e., convergent validity; internal consistency) of the F-EPSI. We postulated the original eight-factor structure of the EPSI would replicate in the Farsi version ([27]; H1) among adolescents. Consistent with previous research among the US adolescents [10, 16], adults [25], and Iranian college samples [24], we expected that the F-EPSI factor structure would be invariant across genders, weight status, and age (i.e., younger vs. older adolescents; adolescents vs. adults) groups (H2). Furthermore, we hypothesized that the EPSI subscales would demonstrate strong reliability (i.e., Cronbach’s alpha, McDonald’s ω; ≥.70) in adolescents ([10]; H3). If the scale was invariant across groups, in addition to our primary objective, we had three secondary objectives related to the validity of the F-EPSI. We first posited that girls would report higher scores on body dissatisfaction and cognitive restraint, while boys would endorse higher restricting and muscularity [10, 16]. Second, individuals with higher weight would report higher scores on the F-EPSI subscales, except for restricting and muscle building [24]. Third, adolescents would endorse higher scores on body dissatisfaction, restricting, cognitive restraint, purging, and excessive exercise compared to adults, while adults would report higher scores on binge eating compared to adolescents ([10]; H4). In accordance with the EPSI’s data among US [25, 27], Chinese [28], and Iranian [24] adults, we also expected that most of the F-EPSI subscales would have a strong, positive correlation (r ≥.40) with a global score of the EDE-Q, binge eating, purging, over-exercise, depression, and BMI, while the restricting and muscle building subscales would demonstrate weak, negative correlations with BMI (r ≥ −.10 [29]; H5). Finally, we explored measurement invariance of the F-EPSI subscales among adolescents with and without clinically significant eating disorder symptoms. If the scale is invariant across groups, adolescents with clinically significant eating disorder symptoms may report higher scores on the F-EPSI subscales. This final analysis was exploratory.
## Participants and procedure
Participants ($$n = 913$$; $85.3\%$ girls) were adolescents who were recruited from three cities with diverse geographic zones and ethnicities (i.e., Tehran $$n = 288$$ ($31.5\%$) [Capital, Persian], Tabriz $$n = 308$$ ($33.7\%$) [North-Western, Turkish], and Kurdistan $$n = 317$$ ($34.7\%$) [West, Kurdish]). The age range of participants was 10–19 years ($M = 15.07$, SD = 1.90). Standardized body mass index (zBMI) ranged from − 2.20 to 5.70. Analysis of variance (ANOVA) indicated no significant differences in age (F [2910] = 1.11, $$p \leq .329$$) or zBMI (F [2910] =.09, $$p \leq .919$$) by city. Using an online method (i.e., Google forms) advertised in schools’ social media groups (i.e., Shad program,2 WhatsApp, Telegram), interested adolescents completed a battery of questionnaires without any financial remuneration from March 2021 to February 2022. All questionnaires were anonymous with no identifying information collected to protect the confidentiality of participants. The school and regional administrators approved the research procedures. Parental consent was obtained prior to participation, and adolescents provided assent. The study was approved by the institutional review board from a large Iranian university.
For the comparisons with Iranian adults, we used Sahlan et al. ’s data [24] in which college students ($$n = 765$$) were recruited from Iran. College students ranged in age from 18 to 28 years ($M = 21.12$, SD = 1.20) and self-reported BMI ranged 15.16–34.95 kg/m2 ($M = 23.40$, SD = 3.81; see Sahlan et al. for review; [24]).
## Demographic information
Participants reported their age, gender, height, and weight (used to derive BMI; kg/m2; [30] on a study-specific questionnaire. Aligned with the World Health Organization guidelines for children and adolescents aged 5–19 years, we converted BMI to age- and sex-specific zBMI scores.
## Farsi Eating Pathology Symptoms Inventory (F-EPSI)
We used a Farsi version of the F-EPSI (F-EPSI; [24]) that assesses eight subscales: Body Dissatisfaction, Binge Eating, Cognitive Restraint, Excessive Exercise, Restricting, Purging, Muscle Building, and Negative Attitudes toward Obesity. The items are rated on a five-point scale ranging from 0 (Never) to 4 (Very often) and higher scores indicate a greater level of eating pathology [10, 25, 27]. The validity and reliability of the F-EPSI has been supported among Iranian college samples [24] for the first time, using a scale that underwent translation and back-translation procedures. In addition, prior to conducting the study, we piloted the F-EPSI with a small group of adolescents (boy $$n = 20$$, girl $$n = 19$$) to ensure adolescents could read and comprehend all the items and found that all the items were understandable to adolescents.
## Farsi-Eating Disorder Examination Questionnaire (F-EDE-Q)
We used a Farsi version of the EDE-Q (F-EDE-Q; [1, 4]) that assesses ED symptoms over the past 28 days. Twenty-two items are rated on a seven-point scale ranging from 0 (No days) to 6 (Every day). Five items assess the frequency of ED behaviors. Higher scores indicate a greater level of pathology. The validity and reliability of the F-EDE-Q has been supported in Iran [1, 4]. In the current study, we focused on a global score of the F-EDE-Q, binge eating, purging, and over-exercise. In line with a previous study in Iran [31], we summed self-induced vomiting and laxative misuse items to provide one purging frequency index. Cronbach’s α (.93) and McDonald’s ω (.94) were reported for a global score of the F-EDE-Q in the current sample. McDonald’s ω was.85 for purging in the current sample.
## Farsi-Revised Children Anxiety and Depression Scale (F-RCADS)
We used a Farsi version of the RCADS (F-RCADS; [32]) that assesses anxiety and depressive symptoms. The Depression subscale includes ten items that are rated on a four-point scale ranging from 0 (Never) to 3 (Always). In the current study, we used the Depression subscale. The validity and reliability of the F-RCADS has been supported in Iran [32]. Higher scores indicate a greater level of depression. Cronbach’s α (.91) and McDonald’s ω (.92) were reported in the current sample.
## Analytic plan
There were no missing data on any of the scales administered. We first conducted confirmatory factor analysis (CFA) to test the original 8-factor structure of the F-EPSI in adolescents (H1). Consistent with prior research [25], we employed robust Diagonally Weighted Least Squares (DWLS), given the ordinal nature of the data. The weighted least squares estimator (WLSMV) is a robust version of the DWLS in JASP [33]. As suggested in the literature [34, 35], model fit was assessed using the following indices: root mean square error of approximation (RMSEA; <.08 indicates a good model fit), the comparative fit index (CFI; >.90 indicates an acceptable model fit), the Tucker–Lewis index (TLI; >.90 indicates an acceptable model fit), and the standardized root mean square residual (SRMR; <.08 indicates a good model fit).
We used multi-group confirmatory factor analysis (MGCFA) to test the measurement invariance by gender, zBMI status (i.e., low-to-average weight [zBMI < + 1SD] vs. higher weight [zBMI > + 1SD]), eating disorder symptom clinical cutoffs, and age (i.e., younger adolescents [10–15] vs. older adolescents [16–19]; adolescents vs. college samples), to assess whether the scale performed similarly for individuals by gender, weight status, and age groups (H2; [36]). With respect to eating disorder groups, we used a cutoff of EDE-Q global score (i.e., ≥ 2.50; [37]) in differentiating adolescents with and without clinically significant eating disorder symptoms. Measurement invariance was assessed at the configural, metric, scalar, and strict levels [38]. Configural invariance implies that the latent F-EPSI variable(s) and the pattern of loadings of the latent variable(s) on indicators are similar across genders (i.e., the unconstrained latent model should fit the data well in both groups). Metric invariance implies that the magnitude of the loadings is similar across genders; this is tested by comparing two nested models consisting of a baseline model and an invariance model. Scalar invariance implies that both the item loadings and item intercepts are similar across genders and is examined using the same nested-model comparison strategy as with metric invariance [29]. Finally, strict invariance implies that factor loadings, intercepts, and residual variances are fixed across groups [39]. Following the recommendations of Cheung and Rensvold [40] and Chen [41], we accepted ΔCFI ≤.010, ΔTLI ≤.010, and ΔRMSEA ≤.015 or ΔSRMR ≤.010 (.030 for measurement invariance) as evidence of invariance, as chi-square is not a robust indicator in large samples. We used JASP version 0.14 to conduct CFA and measurement invariance.
Cronbach’s alpha and classical McDonald’s ω (i.e., for categorical variables) assessed internal consistencies (H3; [42, 43]). The following cutoffs were used to indicate good internal consistency: Cronbach’s α and McDonald’s ω ≥.70 [43, 44]. We calculated item reliability to provide an estimate of the extent to which an individual item is a reliable indicator of the underlying construct by squaring each standardized factor loading from the CFA.
Upon establishing measurement invariance across groups using MGCFA, independent sample t tests (Normality of data) and Mann–Whitney U (Non-normality of data) tests examined mean differences of the F-EPSI subscales scores by gender, weight status (H4), eating disorder groups, and age across groups. As proposed by Cohen [29], $d = 0.20$ is interpreted as small effects, $d = 0.50$ as medium effects, and $d = 0.80$ as large effects for both independent sample t tests and Mann–Whitney U tests. We used SPSS 25.0 for these analyses.
To establish convergent validity of the F-EPSI (H5), Pearson (Normality of data) and Spearman (Non-normality of data)’s correlations examined associations between F-EPSI subscales, eating disorder symptoms, binge eating, and purging. To test criterion validity of the F-EPSI (H5), Pearson’s correlations examined associations between F-EPSI subscales, zBMI, and depression. According to Cohen [29], correlation coefficients of.10,.30, and.50 are considered to be small, medium, and large correlations, respectively. We used SPSS 25.0 for these analyses.
## Confirmatory factor analysis
We tested normality of the F-EPSI items using skewness, and kurtosis values, and found that purging and muscle building subscales were not normal (see Table 1 for more information). In support of H1, the original 8-factor structure of the EPSI demonstrated acceptable fit to the data in the overall sample. Standardized factor loadings ranging from.43 to.82 (see Table 1 for more information). Model fit statistics and change in fit values from the multi-group CFAs are included in Table 2.Table 1Means, standard deviations, skewness, kurtosis, standardized factor loadings, and item reliability of the F-EPSI itemsMSDSkewnessKurtosisStandardized factor loadingsItem reliabilityBody dissatisfactionEPSI-11.181.13.74−.14.46.21EPSI-12.861.121.17.42.70.49EPSI-181.211.26.76−.52.82.68EPSI-23.721.101.501.36.50.25EPSI-241.101.36.95−.44.75.57EPSI-251.451.44.56− 1.04.80.65EPSI-34.841.231.41.88.66.44Binge eatingEPSI-31.111.14.78−.30.59.35EPSI-9.981.05.89.06.53.28EPSI-19.761.011.291.0.73.53EPSI-28.721.041.511.60.64.41EPSI-37.921.051.18.89.51.26EPSI-39.40.822.446.16.57.32EPSI-44.53.901.853.07.66.43EPSI-45.66.871.261.10.70.48Cognitive restraintEPSI-22.021.16−.03−.72.60.36EPSI-21.871.041.08.52.65.43EPSI-401.441.20.48−.66.76.58PurgingEPSI-11.09.435.6234.97.63.40EPSI-13.26.692.988.94.48.23EPSI-16.25.673.059.69.58.33EPSI-17.09.374.5720.76.68.47EPSI-27.06.306.8058.64.61.37EPSI42.05.307.3364.68.71.51RestrictingEPSI-41.341.25.61−.67.46.21EPSI-61.211.16.69−.42.54.29EPSI-102.041.05−.06−.51.48.23EPSI-331.601.11.31−.64.70.49EPSI-361.321.22.61−.62.59.35EPSI-431.121.09.70−.37.50.25Excessive exerciseEPSI-52.211.25−.14−.92.49.24EPSI-81.111.19.81−.33.57.33EPSI-221.311.16.60−.44.59.35EPSI-31.821.061.271.0.76.58EPSI-411.371.22.56−.61.73.53Negative attitudes toward obesityEPSI-14.931.181.11.26.46.21EPSI-201.261.20.68−.44.51.26EPSI-261.681.49.34− 1.30.64.41EPSI-301.331.40.68−.85.75.57EPSI-38.831.201.39.89.73.54Muscle buildingEPSI-7.19.553.4013.35.64.41EPSI-15.28.652.859.18.80.64EPSI-29.27.652.727.51.79.62EPSI-32.75.93.98.04.43.19EPSI-35.44.831.872.86.46.21Table 2Goodness of fit indices found for the 8-factor model of the eating pathology symptoms inventory and measurement invariance in different groups of Iranian adolescentsConfirmatory factor analysisχ2pdfCFITLIRMSEA$90\%$ CISRMRCFA2313.261.001917.924.918.041[.039–.043].056Measurement invarianceModelχ2pdfCFITLIRMSEA$90\%$ CISRMRModel ComparisonΔCFIΔTLIΔRMSEAΔSRMRGender Configural3102.622.0011834.930.925.039[.037–.041].064––––– Metric3270.691.0011871.923.918.041[.038–.043].065Configural vs. Metric.007.007.002.001 Scalar3339.564.0011908.921.918.041[.038–.043].064Metric vs. Scalar.002.000.000.001 Strict3434.854.0011953.918.917.041[.039–.043].066Scalar vs. Strict.003.001.000.002 zBMI status, average weight vs. overweight (i.e., ≤ + 1SD and > + 1SD) Configural2826.782.0011834.942.937.034[.032–.037].062––––– Metric2961.453.0011871.936.932.036[.033–.038].063Configural vs. Metric.006.005.002.001 Scalar3108.001.0011908.930.927.037[.035–.040].062Metric vs. Scalar.006.005.001.001 Strict3242.692.0011953.924.923.038[.036–.040].065Scalar vs. Strict.006.004.001.003ED groups, adolescents with and without ED (i.e., EDE-Q global score, ≤ 2.5) a Configural2777.442.0011834.936.930.034[.031–.036].062––––– Metric2969.576.0011871.925.921.036[.033–.038].062Configural vs. Metric.011.009.002.000 Scalar3120.425.0011908.917.914.037[.035–.040].062Metric vs. Scalar.008.007.001.000 Strict3327.594.0011953.906.905.039[.037–.042].065Scalar vs. Strict.011.009.002.003ED groups, adolescents with and without ED (i.e., EDE-Q global score, ≤ 2.5)b Configural2642.888.0011828.944.940.031[.029–.034].060––––– Metric2834.972.0011865.934.930.034[.031–.036].061Configural vs. Metric.010.010.003.001 Scalar2986.847.0011902.926.923.035[.033–.038].060Metric vs. Scalar.008.007.001.001 Strict3201.380.0011950.915.913.038[.035–.040].064Scalar vs. Strict.011.010.003.004Age groups, younger adolescents (10–15) vs. older adolescents (16–19) Configural2969.487.0011834.935.930.037[.034–.039].064––––– Metric3139.488.0011871.928.924.039[.036–.041].066Configural vs. Metric.007.006.002.002 Scalar3186.959.0011908.927.924.038[.036–.041].065Metric vs. Scalar.001.000.001.001 Strict3268.224.0011953.925.924.038[.036–.041].069Scalar vs. Strict.002.000.000.004Age groups, adolescents vs. college samples1 Configural4467.928.0011834.952.948.041[.040–.043].058––––– Metric4952.038.0011871.943.940.044[.043–.046].061Configural vs. Metric.009.008.003.003 Scalar5051.758.0011908.942.940.044[.043–.046].060Metric vs. Scalar.001.000.000.001 Strict5306.770.0011953.938.937.045[.044–.047].065Scalar vs. Strict.004.003.001.005CFI Comparative fit index. TLI Tucker–Lewis index. RMSEA root mean square error of approximation. SRMR Standardised root mean square residual. 1Data were used from Sahlan et al. ’s study [24]. aBefore freeing an error covariance among three items. bAfter freeing an error covariance among three item
## Measurement invariance by gender
As shown in Table 2, change in fit indices (i.e., CFI, TLI, RMSEA, SRMR) between the configural and metric models met the criteria offered by Cheung and Rensvold [40] and Chen [41], providing support for metric invariance and suggesting that factor loadings were equivalent by gender. In addition, change in fit indices between the scalar and strict models provided evidence for strict invariance by gender, which supported H2.
## Measurement invariance by weight status
In support of H2, change in fit indices (i.e., CFI, TLI, RMSEA, SRMR) between the configural and metric models met the criteria offered by Cheung and Rensvold [40] and Chen [41], providing support for metric invariance and suggesting that factor loadings were equivalent by weight status. In addition, change in fit indices between the scalar and strict models provided evidence for strict invariance by weight status (see Table 2).
## Measurement invariance by clinical eating disorder group
As summarized in Table 2, change in fit indices (i.e., CFI, TLI, RMSEA, SRMR) between the configural and metric models and between the metric and scalar, and between scalar and strict models indicate that the scale is partially invariant among adolescents with and without clinically significant eating disorder symptoms. The model fit of metric invariance was not strong based on the ΔCFI (.011). As such, modification indices (MIs) were evaluated and applied to improve the fit of metric invariance. MIs suggested freeing an error covariance among 1–3 items; however, the model fit did not improve among one (ΔCFI =.011) and two (ΔCFI =.011) items. Thus, we examined freeing an error covariance among three items, which slightly improved model fit (ΔCFI =.10) (see Table 2).
## Measurement invariance by age (younger adolescents vs. older adolescents)
In support of H2, change in fit indices (i.e., CFI, TLI, RMSEA, SRMR) between the configural and metric models met the criteria [40, 41], providing support for metric invariance and suggesting that factor loadings were equivalent by age. In addition, change in fit indices between the scalar and strict models provided evidence for strict invariance by age (see Table 2).
## Measurement invariance by age (adolescents vs. college students)
In support of H2, change in fit indices (i.e., CFI, TLI, RMSEA, SRMR) between the configural and metric models met the criteria [40, 41], providing support for metric invariance and suggesting that factor loadings were equivalent by age. In addition, change in fit indices between the scalar and strict models provided support for strict invariance by age (see Table 2).
## Item reliability and internal consistency
Item reliability values range from.19 to.68 (see Table 1 for more information). In support of H3, the F-EPSI subscales demonstrated good internal consistency based on both Cronbach’s alphas and McDonald’s ω, with Cronbach’s alpha values ranging from.71 to.85 and classical McDonald’s ω ranging from.71 to.88 (see Table 4).
## Gender and weight status differences
Contrary to H4, adolescent boys endorsed significantly higher scores than girls on the Body Dissatisfaction and Binge Eating subscales. In line with H4, adolescent boys reported higher scores than girls on the Excessive Exercise and Muscle Building subscales. The other mean subscale differences were not significant across genders (see Table 3).Table 3Means (standard deviations), mean rank (sum of ranks), and t/z tests by groups for the F-EPSI subscalesBoys ($$n = 134$$)Girls ($$n = 779$$)t/zpEffect sizeaSubscalesM (SD)/Mean rank (Sum of ranks)M (SD)/Mean rank (Sum of ranks)Body dissatisfaction8.64 (6.17)7.13 (6.34)2.61.010.24Binge eating6.96 (4.93)5.91 (5.37)2.26.025.20Cognitive restraint4.21 (2.30)4.35 (2.77).54.59.05Purging464.01 (62,177.50)455.79 (355,063.50).43.67.14Restricting8.78 (4.05)8.59 (4.49).50.62.04Excessive exercise9.14 (4.88)6.43 (3.95)7.08.001.61Negative attitudes toward obesity6.25 (4.48)6.0 (4.66).59.55.05Muscle building578.29 (77,491.0)436.14 (339,750.0)5.98.001.20zBMI score < 1($$n = 774$$)zBMI score > 1($$n = 139$$)Body dissatisfaction6.47 (5.85)12.24 (6.74)10.44.001.91Binge eating5.61 (5.0)8.59 (6.26)6.22.001.53Cognitive restraint4.19 (2.69)5.10 (2.65)3.73.001.34Purging432.01 (334,372.0)596.18 (82,869.0)8.71.001.29Restricting8.64 (4.56)8.47 (3.60).41.68.04Excessive exercise6.58 (4.24)8.19 (3.76)4.57.001.40Negative attitudes toward obesity5.85 (4.62)7.03 (4.61)2.77.006.26Muscle building463.54 (358,778.50)420.59 (58,462.50)1.58.067.05Without eating disorder($$n = 765$$)With eating disorder($$n = 148$$)Body dissatisfaction5.94 (5.36)14.64 (6.02)17.70.0011.53Binge eating5.34 (4.76)9.78 (6.38)9.76.001.79Cognitive restraint4.20 (2.71)4.97 (2.56)3.33.001.29Purging429.20 (328,339.50)600.69 (88,901.50)9.33.001.31Restricting8.43 (4.52)9.60 (3.78)2.96.003.28Excessive exercise6.61 (4.20)7.97 (4.05)3.74.001.33Negative attitudes toward obesity5.54 (4.42)8.57 (4.90)7.0.001.65Muscle building457.92 (350,305.50)452.27 (66,935.50).62.80.02Younger adolescents($$n = 566$$)Older adolescents($$n = 347$$)Body dissatisfaction6.39 (6.06)8.91 (6.48)5.85.001.40Binge eating5.38 (4.97)7.17 (5.67)5.0.001.34Cognitive restraint4.43 (2.69)4.15 (2.72)1.53.13.10Purging445.15 (251,956.0)476.33 (165,285.0)2.24.025.07Restricting8.33 (4.53)9.09 (4.22)2.59.010.17Excessive exercise6.57 (3.97)7.24 (4.54)2.32.020.16Negative attitudes toward obesity6.08 (4.70)5.96 (4.53).36.72.03Muscle building427.95 (242,219.0)504.39 (175,022.0)4.41.001.15Adolescents($$n = 913$$)College samples($$n = 765$$)Body dissatisfaction7.35 (6.34)8.15 (6.17)2.62.009.13Binge eating6.06 (5.31)6.09 (5.09).10.92.01Cognitive restraint4.33 (2.70)4.08 (2.54)1.89.058.10Purging775.90 (708,392.50)915.41 (700,288.50)7.04.001.00Restricting8.62 (4.43)7.85 (4.62)3.46.001.17Excessive exercise6.83 (4.21)6.04 (4.19)3.84.001.19Negative attitudes toward obesity6.03 (4.63)6.87 (4.78)3.64.001.18muscle building819.25 (747,978.0)863.66 (660,703.0)1.94.053.00aEffect size = We used this formula (z/√N [49]) to assess effect size of purging and muscle building subscales. We used Cohen’s d for the other subscales. Regarding purging and muscle building, we used z (Whitney U) tests, and for the other subscales, we used t (independent sample) tests. Regarding purging and muscle building, we used Mean Rank (Sum of Ranks), and for the other subscales, we used Means (Standard Deviations) In support of H4, adolescents with higher weight reported higher scores on most of the F-EPSI subscales. Restricting subscale scores were not different among adolescents with low-to-average weight vs. higher weight. In addition, Muscle Building subscale scores were not significantly different among adolescents with low-to-average weight vs. higher weight (see Table 3).
## Clinically significant eating disorder symptoms groups
As presented in Table 3, adolescents with clinically significant eating disorder symptoms endorsed higher scores on most of the F-EPSI subscales compared to adolescents without clinically significant eating disorder symptoms. Muscle Building subscale scores were not different among adolescents with and without clinically significant eating disorder symptoms.
## Age groups
In support of H4, older adolescents reported higher scores than younger adolescents on most of the F-EPSI subscales. Cognitive Restraint and Negative Attitudes toward Obesity subscales were not significantly different across younger adolescents and older adolescents (see Table 3).
In support of H4, younger adolescents reported higher scores than older adolescents on Restricting and Excessive Exercise subscales. The college sample endorsed higher scores than adolescents on most of the F-EPSI subscales (see Table 3). Binge eating, Cognitive Restraint, and Muscle Building subscales were not significantly different across adolescents and college samples.
## Convergent validity
In support of H5, most of the F-EPSI subscales demonstrated significant, positive associations with eating disorder symptoms (F-EDE-Q global score), binge eating (assessed through the F-EDE-Q), purging (assessed through the F-EDE-Q), and over-exercise (assessed through the F-EDE-Q) (see Table 4).Table 4Means, standard deviations, internal consistency, and correlations between F-EPSI Subscales, zBMI, eating disorder-related measures, and depression in adolescentsBody dissatisfactionBinge eatingCognitive restraintPurgingRestrictingExcessive exerciseNegative attitudes toward obesityMuscle buildingzBMI.39***.19***.18***.24***−.09**.19***.07*−.12***Eating disorder symptoms.62***.35***.17***.36***.14***.26***.26***.12***Binge eating.25***.44***.00.29***.02.08**.19***.14***Purging.10**.10**.03.26***.02.09**.08**.15***Over-exercise.16***.06.13***.30***.01.30***.02.14***Depression.50***.36***−.18***.17***.32***.03.16***.27***α.85.83.71.73.72.76.76.73ω.86.83.71.74.72.73.77.73M7.356.064.33.798.626.836.031.93SD6.345.312.701.924.434.214.632.53zBMI Body Mass Index. Eating disorder symptoms Farsi Eating Disorder Examination Questionnaire (i.e., F-EDE-Q; combined restraint, eating concern, weight concern, and shape concern subscales). Purging Summed self-induced vomiting and laxative misuse items. Depression Subscale of the Revised Children Anxiety and Depression Scale (RCADS). Regarding the associations of purging and muscle building subscales with the variables, we used Spearman correlations, and regarding the other F-EPSI subscales, we used Pearson correlations. α Cronbach’s α. ω McDonald’s ω. * $p \leq .05$, **$p \leq .01$, ***$p \leq .001$
## Criterion validity
In support of H5, most of the F-EPSI subscales demonstrated significant, positive associations with zBMI and depression. The Restricting and Muscle Building subscales had significant, negative associations with zBMI. Cognitive Restraint subscale was significantly and negatively associated with depression (see Table 4).
## Discussion
The majority of our five hypotheses were supported by the F-EPSI adolescent data. In support of H1, the original 8-factor structure of the EPSI demonstrated acceptable fit to the data in the overall sample. The scale was invariant across genders, weight status, and age groups, supporting H2. Measurement invariance of the scale by clinically eating disorder group required freeing error covariances of one to three items in the eating disorder symptoms group. The items for which covariances were freed contained content related to body dissatisfaction, weight stigma, dietary restriction, and exercise, all of which are implicated in global eating pathology. The subscales demonstrated acceptable internal consistency and supported H3. Hypothesis four (H4) was partially supported; adolescent boys reported higher scores on the Excessive Exercise and Muscle Building subscales compared to adolescent girls, adolescents with higher weight reported higher scores on most of the F-EPSI subscales compared to adolescents of low-normal weight, and older adolescents reported higher scores than younger adolescents on most of the F-EPSI subscales. In support of H4, and most sub-scales were significantly correlated with zBMI, eating disorder symptoms, binge eating, purging, over-exercise, and depression, in support of H5. Thus, these findings establish the F-EPSI as a robust measure of multiple facets of eating pathology for use in Iranian adolescent populations.
The adolescent version of the F-EPSI overcomes multiple limitations inherent to the existing measures of eating pathology translated to Farsi. Previously, only the F-EDE-Q [1], F-EAT-26 [5], and F-ChEAT-20 [6] were available for administration among preadolescents and adolescents in Farsi. In validating the F-EPSI, the current study provides a psychometrically sound tool to assess the eating pathology symptoms more common in boys, such as muscularity and excessive exercise [7] and the behavioral features of eating pathology, including binge eating and purging. In line with our hypothesis and previous research among adolescents, boys endorsed higher muscularity and excessive exercise than girls [10, 16]. However, adolescent boys endorsed higher Body Dissatisfaction and Binge Eating scores than girls that were not in line with previous research among the US [10] and Iranian samples [1, 13]. One possible explanation could be the importance of muscularity which is common among Iranian adolescent boys ($41.1\%$; 19). This urgency to attain muscularity may encourage adolescent boys to engage in binge eating to increase massive muscular bulk as it is applauded among Iranian men [45]. In addition, Iranian adolescent boys tend to consume dietary supplements; however, this consumption would increase boys’ weight that in turn may result in elevated body dissatisfaction in boys [21]. Notably, boys and girls reported comparable scores on restricting and cognitive restraint that was not in line with Richson et al.’ [ 10] study. It may be noted that boys tend to engage dieting behaviors to attain better shape and that is another reason boys endorsed higher body dissatisfaction. Furthermore, it is likely that body dissatisfaction and muscle building are inter-related. Thus, boys may endorse higher dissatisfaction with their body and that is why they may need to engage in muscle building to be satisfied with their bodies or vice versa. More precisely, recent studies [45, 46] reported that Western societal norms such as thin-ideal internalization, pressures for thinness, and social comparison were extended to Iranian boys. Relatedly, the effects of societal norms on eating pathology are comparable between Iranian and US cultures [47]. These societal norms sometimes may not be attainable and that, in turn, would result in higher eating pathology among adolescent boys. Taken together, the inclusion of this population in health promotion and prevention programs may, therefore, be increasingly important among adolescent boys. In addition, the F-EPSI assesses weight stigma and muscle building, both important components of eating pathology that are typically not quantified within existing eating pathology measures. The present findings extended previous studies in Western adolescents [16, 17, 22] and noted that weight stigma and muscle building related to other facets of eating pathology symptoms and depression in adolescents.
To our knowledge, this is only the second study to examine the factor structure and group level differences on EPSI sub-scale scores in an adolescent sample, and first to examine these in a non-clinical sample. Richson et al. [ 10] found the six-factor EPSI to be invariant across age as well as age-related differences in body dissatisfaction, restricting, cognitive restraint, purging, excessive exercise, and binge eating in a sample of recovery app users. Similarly, the current study demonstrated metric invariance by age, and that older adolescents scored higher than younger adolescents, in line with prior work by Mond and colleagues [11]. In accordance with a previous research [10] and our research hypothesis, adolescents endorsed higher restricting and excessive exercise than adults. However, contrary to Richson et al.’ [ 10] study and our research hypothesis, the college sub-sample scored higher than the adolescent sub-sample on most F-EPSI sub-scale scores than adolescents. Notably, both adolescents and college students endorsed similar scores on binge eating, cognitive restraint, and muscle building subscales. Overall, these findings indicate the importance of using the EPSI while assessing eating pathology symptoms across developmental periods in the future. Given that multiple facets of eating pathology are not similar in prevalence across developmental periods, clinicians need to consider it upon working on individuals with eating pathology in therapeutic settings in Iran. In addition, the current study extends Richson et al. ’s [10] findings by establishing measurement invariance not only by developmentally, but also by gender, weight status, and clinically significant eating disorder symptomology status, making the F-EPSI a useful tool for both researchers and clinicians. In line with a previous study in Iran [14], adolescents with higher weight tended to report higher eating pathology symptoms.
The F-EPSI subscales were associated with zBMI and depression, supporting criterion validity of the scale. Consistent with the Iranian adult study [24], adolescents’ weight was negatively associated with restricting and muscle building. It can be noted that adolescents, irrespective of BMI, tend to use supplements to build muscularity and participate in eating-related restriction. The present study extended a previous studiy in Iran [14] and reported that F-EPSI and zBMI were inter-correlated, which may suggest that eating pathology is implicated more in Iranian adolescents’ zBMI, compared to Western societies [25]. In line with previous studies among patients with eating disorders [25] and college students [28], the F-EPSI subscales were related to depression. Interestingly, the associations between body dissatisfaction and depression were higher than previous studies among college students in China ($r = .25$; [28]) and general psychiatric patients in US ($r = .29$; [25]). It can be noted that adolescence is a period in which eating pathology could lead to higher depression or vice versa [48]. Thus, longitudinal studies need to examine the directionality of these associations. More precisely, cognitive restraint was negatively associated with depression which was not in line with a previous study among patients with eating disorders in US [25]. Thus, Iranian adolescents, regardless of depressive symptoms, may tend to engage in cognitive restraint to control body weight and shape. Future studies need to examine how other psychological symptoms such as anxiety symptoms affect cognitive restraint among adolescents.
## Strength and limits
This study is the first to empirically examine the eight-factor version of the F-EPSI in Iranian adolescents while assessing measurement invariance, reliability, and validity. The current study benefits from a large sample size, which provided the ability to examine the F-EPSI across gender and developmental periods. However, some limitations of the current study should be addressed in the future. First, the F-EPSI was not administered longitudinally, which prohibited the examination of both test–retest reliability and within-person change over time. However, Sahlan et al. [ 24] found that the F-EPSI scores are stable over time among adults in Iran. Second, the F-EPSI has only shown to be valid among younger adolescents, older adolescents, and emerging adults. It will be necessary to validate the F-EPSI in child (i.e., < 10), and adult (i.e., 28 >) populations, and patients with eating disorders, to ensure each conceptualizes eating disorder symptomology measured by the EPSI in the same way. Finally, we examined clinically significant eating disorder symptoms group using a cutoff of the EDE-Q global score established in Norway [37]. Thus, future studies need to validate a cutoff of the EDE-Q global score for use within an Iranian context. Relatedly, future studies need to include clinical samples to identify cutoffs through ROC curves for the F-EPSI subscales. These limitations notwithstanding, the current study provides a novel tool for assessing multiple facets of eating pathology among Iranian adolescent boys and girls.
## Conclusions
The F-EPSI was a psychometrically valid and reliable scale when applied to Iranian adolescents and could be useful in both research and therapeutic settings. This scale assesses eating pathology in both boys and girls and in our study it was invariant across genders, weight status, and eating disorder groups. In this way, the F-EPSI can be used across multiple developmental periods (e.g., younger and older adolescence and emerging adulthood). The findings suggest the F-EPSI as a robust measure of dietary pathology for use in Iranian adolescent populations.
## What is already known on this subject?
The majority of eating pathology measures focuses on girls’ eating pathology (e.g., thinness/dietary restraint) and do not reliably examine eating pathology more commonly associated with boys (e.g., muscularity/exercise). The EPSI is a relatively new assessment of eating pathology that addresses the limitations of previous eating pathology scales by capturing multiple dimensions of eating pathology cognitions and behaviors relevant to both boys and girls.
## What this study adds?
This study assessed the validation of the Farsi version of the EPSI (F-EPSI). The F-EPSI’s 8-factor structure was supported among adolescents. The F-EPSI was found to operate similarly across gender, age, weight status, age, and eating disorder groups, suggesting that scores on the scale can be compared across those groups. Furthermore, the scale demonstrated both reliability and validity.
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|
---
title: Lipotoxicity-induced mtDNA release promotes diabetic cardiomyopathy by activating
the cGAS-STING pathway in obesity-related diabetes
authors:
- Xiu Mei Ma
- Kang Geng
- Betty Yuen-Kwan Law
- Peng Wang
- Yue Li Pu
- Qing Chen
- Hui Wen Xu
- Xiao Zhen Tan
- Zong Zhe Jiang
- Yong Xu
journal: Cell Biology and Toxicology
year: 2022
pmcid: PMC10042943
doi: 10.1007/s10565-021-09692-z
license: CC BY 4.0
---
# Lipotoxicity-induced mtDNA release promotes diabetic cardiomyopathy by activating the cGAS-STING pathway in obesity-related diabetes
## Abstract
Diabetic cardiomyopathy (DCM) is characterized by lipid accumulation, mitochondrial dysfunction, and aseptic inflammatory activation. Mitochondria-derived cytosolic DNA has been reported to induce inflammation by activating cyclic GMP-AMP synthase (cGAS)/the stimulator of interferon genes (STING) pathway in the adipose, liver, and kidney tissues. However, the role of cytosolic mtDNA in the progression of DCM is unclear. In this study, with an obesity-related DCM mouse model established by feeding db/db mice with a high-fat diet (HFD), we observed increased mtDNA in the cytosol and activated cGAS-STING signaling pathway during DCM, as well as the downstream targets, IRF3, NF-κB, IL-18, and IL-1β. In a further study with a palmitic acid (PA)-induced lipotoxic cell model established in H9C2 cells, we revealed that the cytosolic mtDNA was the result of PA-induced overproduction of mitochondrial ROS, which also led to the activation of the cGAS/STING system and its downstream targets. Notably, treatment of extracted mtDNA alone was sufficient to activate the cGAS-STING signaling pathway in cultured H9C2 cells. Besides, both knockdown of STING in PA-induced H9C2 cells and inhibition of STING by C-176 injection in the DCM mouse model could remarkably block the inflammation and apoptosis of cardiomyocytes. In conclusion, our study elucidated the critical role of cytosolic mtDNA-induced cGAS-STING activation in the pathogenesis of obesity-related DCM and provided preclinical validation for using a STING inhibitor as a new potential therapeutic strategy for the treatment of DCM.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10565-021-09692-z.
The online version contains supplementary material available at 10.1007/s10565-021-09692-z.
## Highlights
Mitochondria-derived cytosolic DNA acts as a critical linker between hyperlipidemia-induced mitochondrial dysfunction and pathogenesis of DCM.cGAS-STING pathway mediates the lipotoxicity-induced myocardial dysfunction through sensing released cytosolic mtDNA.STING was identified as a new potential therapeutic target for the treatment of DCM.
## Introduction
The International Diabetes Federation estimates that by 2040, nearly 500 million people will be overweight and insulin resistant, and 642 million people will be affected by type 2 diabetes (T2D) (Ogurtsova et al. 2017). Chronic complications of T2D are the main hazards of diabetes, which often involve the heart, brain, kidney, and other vital organs. Among them, DCM is an important cause of heart failure in diabetic patients (Isfort et al. 2014; Seferović and Paulus 2015). T2D has many harmful effects on the heart, including lipid accumulation, abnormal energy metabolism, oxidative stress, inflammation, apoptosis, changes in fibrosis gene expression, and decreased left ventricular function (Peterson and Gropler 2020). In diabetic animal models, the increase of mitochondria-mediated cardiac apoptosis is a major event in DCM development (Bhagani et al. 2020; Cai et al. 2002).
As the energy metabolism center of sugar, fat, and protein, mitochondria account for nearly $30\%$ of the volume of mature cardiomyocytes. Besides, mitochondria are also the places where intracellular signals integrate and regulate cell homeostasis (Mottis et al. 2019). Under stress conditions, damaged mitochondria can release some pro-inflammatory signals, such as reactive oxygen species (ROS), in response to changes in the intracellular environment (Chen and Zweier 2014; Dan Dunn et al. 2015). In some pathological conditions, such as auto-immune diseases and obesity, increased mitochondrial metabolic stress can lead to excessive ROS production and destruction of mitochondria, which triggers the release of mitochondrial DNA (mtDNA) into the cytoplasm (Bai et al. 2017; Ishikawa et al. 2009). In patients with T2D disease, the increase of myocardial triglyceride content is significantly related to the impairment of left ventricular diastolic function (Rijzewijk et al. 2008; Schulze et al. 2016). Previous studies have shown that DCM induced by fatty acids is associated with mitochondrial dysfunction, oxidative stress, and inflammation. However, the exact molecular mechanism of fatty acid-induced inflammation and cell death in DCM is still unclear.
cGMP-AMP (cGAMP) synthase (cGAS, also known as MB21D1) is considered to be a cytoplasmic DNA biosensor that recognizes DNA from pathogens (bacteria, viruses, etc.). It activates type I interferon response by synthesizing secondary messenger 2′3′-cGAMP in eukaryotic cells in response to the virus and microbial infection (Cheng et al. 2020; Ma and Damania 2016; Morehouse et al. 2020). cGAMP and its junction protein interferon gene stimulating protein (STING, also known as TMEM173) binding promote the translocation of STING from the endoplasmic reticulum to Golgi and form a complex with tank-binding kinase 1 (TBK1), which is transferred to the internal lysosome where TBK1 phosphorylates transcription factors, including interferon regulatory factor 3 (IRF3) and nuclear factor-kappa B (NF-κB), to initiate signal cascade activation of innate immunity-related genes, including type I interferon (IFN) (Ding et al. 2020; Lam et al. 2014; Tanaka and Chen 2012; Zhang et al. 2019). The activation of cGAS-STING protects cells from various pathogens and cancers by enhancing the immune response. However, recent studies have shown that in addition to DNA of microbial origin, the cGAS-STING pathway can also be activated by its cytoplasmic mtDNA (Liu et al. 2016; West and Shadel 2017).
Given that the mitochondrial metabolic stress can lead to mtDNA release and the cGAS-STING system can be activated by cytosolic mtDNA, it is worth noting whether impaired mitochondria contribute DCM through mtDNA-mediated activation of the cGAS-STING pathway. Here, we established an obesity-related DCM mouse model and observed the presence of cytosolic mtDNA, activation of cGAS/STING, and its downstream targets during DCM. Further analysis in palmitic acid (PA)-induced lipotoxic cell model showed PA-induced increase of cytosolic double-stranded DNA (dsDNA) and activation of cGAS/STING pathway in a dose-dependent manner. Knockdown of STING in PA-treated H9C2 cells and treatment with STING inhibitor in high-fat diet (HFD)-fed db/db mice can respectively block cell death and cardiac dysfunction. Our novel observations suggest that cytosolic mtDNA contributes to DCM through activation of cGAS/STING-mediated inflammatory pathway, indicating that functional inhibition of STING could be a potential therapeutic strategy for DCM patients.
## Materials
Palmitic acid (PA) and N-acetyl-l-cysteine (NAC) were obtained from Sigma (St. Louis, MO, USA). Mitochondria-targeted superoxide dismutase mimetic (mito-TEMPO) was purchased from Santa Cruz Biotechnology (Dallas, TX, USA). STING siRNA and the scrambled siRNA were acquired from RiboBio, Guangzhou, China. The fluorescein isothiocyanate (FITC) and cyanine dye 3 (Cy3) secondary antibodies used in immunofluorescence staining were purchased from Bioss, Beijing, China. In situ cell death detection kit was obtained from Roche, Switzerland. The whole gene DNA extraction kit was purchased from FOREGENE, Chengdu, China. The mitochondrial DNA extraction kit was purchased from BioVision, USA. Other chemicals in this study were of analytical grade.
## Cell culture and treatment
Cells were cultured as described previously (Zhao et al. 2016). Rat myocardial cells (H9C2) were subcloned from a cloned cell line of BD1X rat embryonic heart tissue, provided by the Institute of Myocardial Electrophysiology of Southwest Medical University in Luzhou, China. H9C2 cells were cultured in DMEM (Hyclone, USA) with $10\%$ fetal bovine serum (Sciencell, USA), 100 IU ml−1 penicillin, and 100 μg ml−1 streptomycin (Beyotime, China) under $5\%$ CO2 and ambient O2 at 37 °C (Thermo Scientific, USA).
## Animals
Male db/db and db/ + mice (4–5 weeks old) were got from Teng Xin, Chongqin, China. All mice were raised in a specific-pathogen-free (SPF) environment (humidity 50 ± $5\%$, temperature 20–22 °C). db/db were fed with a 60 kcal% fat diet (HFKbio, China) for 8–12 weeks to establish diabetic cardiomyopathy. The body weight of mice was measured every week, and fasting blood glucose was measured every 2 weeks. Mice treated with STING inhibitor were injected intraperitoneally with 750 nmol C-176 (Selleck, USA) per mouse daily in 200 μl corn oil (Selleck, USA) for 8 weeks. In this study, all animal experiment procedures were in accordance with the guidelines of the National Institutes of Health (NIH, Bethesda, USA) and Southwest Medical University (approval number: 201903–59).
## Echocardiography
Echocardiography was performed as described before (Wei et al. 2018). Briefly, echocardiograms were performed by a Vevo'3100 ultrasound (VisualSonics, Canada). Mice were anesthetized with 1.5–$2\%$ isoflurane before echocardiography. Cardiac function parameters were collected, including ejection fraction (EF), fractional shortening (FS), and peak E/A ratio.
## Serum triglycerides’ and inflammatory cytokines’ assays
Blood samples in each group were kept at room temperature for 30 min, and then centrifuged at 3000 g for 15 min (4 °C). After then, the plasma samples were packed in Eppendorf tubes (EP tubes) and stored at − 80 °C for the subsequent analyses. The serum triglyceride level was determined by a rapid, convenient, and sensitive triglyceride detection kit (Nanjing Jiancheng Bioengineering Research Institute, China). The serum inflammatory cytokines IL-1β and IL-18 were detected using ELISA kits from Andy Gene, Beijing, China.
## Histological analysis
Histological changes were analyzed using hematoxylin–eosin (H&E) staining, TdT-mediated dUTP Nick-End Labeling (TUNEL) staining, and immunohistochemical staining as previously reported (Liu et al. 2017; Xiao et al. 2018). Simply, the tissue was fixed with $4\%$ paraformaldehyde, and then dehydrated and paraffin-embedded. The hearts were cut into slices with a thickness of 4 μm and incubated overnight in a thermostat at 37 °C. Then, the slices were deparaffinized and rehydrated. After that, the morphology of cardiomyocytes was observed by H&E staining (Solebo). Besides, cardiomyocyte apoptosis was observed by TUNEL staining (Roche, Switzerland). Furthermore, myocardial fibrosis was evaluated by immunohistochemical staining. After being incubated with $3\%$ H2O2 and $10\%$ goat serum for 20 min and 1 h at room temperature respectively, the slices were incubated overnight with anti-CTGF (1:100, Santa Cruz, USA) and anti-COL1A1 (1:100, Santa Cruz, USA) at 4 °C and incubated with anti-mouse horseradish peroxidase reagent (37 °C, 1 h) and 3,3 N-diaminobenzidine tertrahydrochloride (DAB, room temperature, 5 min). Finally, the slices were observed with an optical microscope.
## DNA isolation and mtDNA analysis
The experiment was carried out as described previously (Bai et al. 2017). Briefly, the cultured cardiomyocytes and the freshly purified mouse cardiac tissue were divided into two equal volumes. Whole-cell genomic DNA was extracted by centrifugation column using a DNA extraction kit (FOREGENE, China). The other used a mitochondrial DNA (mtDNA) extraction kit (BioVision, USA) to extract and purify mtDNA. Cytoplasm free from nuclear, mitochondrial, and endoplasmic reticulum contamination was obtained by high-speed centrifugation. DNA was then isolated from these pure cytoplasmic components using a QIA Quick nucleotide removal column (QIAGEN, Germany). Quantitative PCR was performed using nuclear DNA primers (Tert) and mtDNA primers (Dloop1–3 and mtND4) for whole-cell extracts and cytoplasmic portions. The cycle threshold (CT) of mtDNA abundance in whole-cell extracts was used as normalized control, which effectively standardizes the sample and controls any change in the total amount of mtDNA in the sample.
## Western blot
Western blot analysis was performed as described before (Costantino et al. 2019). Total proteins in cells or tissues were lysed using RIPA buffer (Beyotime, China) and protein concentrations in cell lysates were determined using a bicinchoninic acid kit (BCA, Beyotime, China). The samples were separated by sodium salt-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to 0.45-μm polyvinylidene fluoride (PVDF, Millipore, USA) membranes. After being blocked with $5\%$ BSA for 1 h, membranes were incubated with primary antibodies including cGAS (1:1000, Santa Cruz, USA), STING (1:1000, CST, USA), p65 (1:500, CST, USA), p-p65 (1:1000, CST, USA), IRF3 (1:1000, Santa Cruz, USA), p-IRF3 (1:1000, CST, USA), IL-1β (1:1000, CST, USA), and Tubulin/GAPDH (1:5000, CST, USA) at 4 °C overnight. Then, the membranes were washed three times with TBST and incubated at room temperature with secondary antibodies for 1 h. Finally, the protein bands were visualized with an ECL luminescence reagent. Cytosolic proteins were normalized to Tubulin or GAPDH.
## Real-time PCR
Real-time PCR was performed as described before (Li et al. 2019a). Samples were homogenized in TRIzol (Invitrogen, USA), and total RNA was extracted from tissues or cells with an RNA extraction kit (TIANGEN, China). Then, 1 µl was taken for RNA OD value, OD$\frac{260}{280}$ and OD$\frac{260}{230}$, and the remaining RNA was reversely transcribed into cDNA and stored at − 80 °C for a long time (QIAGEN, Germany). Quantitative PCR reactions were performed using Applied Biosystems™ SYBR™ Green (Thermo Fisher Scientific, USA) and quantitated using the qTOWER 3G detection system (Germany). Duplicate runs of each sample were normalized to GAPDH to determine relative expression levels. Primer synthesis was carried out by Sangon Biotech, Shanghai, China. The primers used in this study are listed in supplementary table 1.
## Immunofluorescence staining
The experiment was carried out as described (Haag et al. 2018). Briefly, H9C2 were grown to a certain density on a 6-well plate overlay. Then, the cells were fixed in $4\%$ paraformaldehyde and drilled with $0.2\%$ Triton and blocked with $5\%$ bovine serum albumin (BSA, Solarbio, China). Subsequently, the cells were incubated with the primary antibodies including mitofilin (1:100, Abcam, UK), dsDNA (1:100, Santa Cruz, USA), STING (1:100, CST, USA), GM130 (1:100, Santa Cruz, USA), and p65 (1:100, CST, USA) overnight at 4 C, and then incubated with FITC- or CY3-bound secondary antibodies for 1 h. 4,6-Diamidino-2-phenylindole (DAPI, Abcam, UK) was used for nuclear staining. Finally, cells were observed by a confocal microscope (Leica, Germany).
## Statistical analysis
Blots were converted to grayscale and densitometry analysis was performed in ImageJ. Co-localization analysis of immunofluorescence images was conducted using Mander’s overlap coefficient. Statistical analysis was performed by GraphPad Prism 6. For comparison between the two groups, an unpaired two-tailed t test was used. For multiple comparisons, one-way ANOVA was used with Turkey’s test No statistical method was used to predetermine sample size. Statistical significance was set at *$P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001.$
## Diabetic cardiomyopathy occurred in HFD-fed db/db mice
Given that fatty acid (FA) oxidation accounts for 60 to $90\%$ of mitochondrial ATP generation under normal conditions and FA accumulation is characteristic of the diabetic heart, we established obesity-related diabetic mouse model to induce diabetic cardiomyopathy by feeding db/db mice with HFD for 3 months. Our data showed that compared with the db/ + mice, the body weight (BW) and fasting blood glucose (FBG) of db/db mice fed with HFD were significantly higher (Fig. 1A), as well as hemoglobin A1c (HbA1c) and triglyceride (TG) (Fig. 1B). In addition, the detection of inflammatory markers showed that IL-1β and IL-18 in plasma of db/db increased (Fig. 1B). H&E staining showed that compared with db/ +, db/db fed with HFD showed obvious myocardial hypertrophy, narrowing of left ventricular cavity, myocardial fibrosis, and even breakage (Fig. 1C). Immunohistochemical staining showed that the CTGF- and COL1A1-labeled fibers in the myocardial interstitium of db/db were significantly increased, suggesting that HFD-induced myocardial fibrosis in db/db mice (Fig. 1D). An electron microscope showed that the myocardial myofilament bundles of db/ + mice were arranged neatly and the Z-line and M-line were clearly visible. By contrast, the myocardium of db/db mice was disordered or even broken, the Z- and M-lines were blurred (Fig. 1E). In addition, more apoptotic cells were observed by TUNEL staining in the myocardial interstitium of db/db fed with HFD (Fig. 1F). Together, our data indicate that diabetic cardiomyopathy occurred in HFD-fed db/db. Fig. 1Diabetic cardiomyopathy occurred in HFD-fed db/db mice. A Dynamic changes of body weight (BW) and fasting blood glucose (FBG) in db/ + and db/db mice during ND and HFD feeding, respectively ($$n = 8$$, *$P \leq 0.05$ vs 0 week, using unpaired two-tailed t test). B The level of HbA1c, TG, IL-1β, and IL-18 in the blood of two groups of mice ($$n = 4$$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test). C Representative images of the morphological analysis by H&E staining of heart tissue. D CTGF and COL1A1 expressions in hearts of HFD-fed db/db mice were visualized by IHC staining ($$n = 4$$, **$P \leq 0.01$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test). E Representative transmission electron microscopy images of myofilament arrangement in db/ + mice and db/db mice ($$n = 4$$). The arrow indicated Z-line or M-line. F Representative images of cardiomyocyte apoptosis reflected by TUNEL staining, counterstained with DAPI (blue). The arrow indicated apoptotic cell ($$n = 4$$, *$P \leq 0.05$ vs db/ + group, using unpaired two-tailed t test)
## Mitochondria were impaired with mtDNA release in cardiomyocytes from HFD-fed db/db mice
As the cytosolic mtDNA derived from damaged mitochondria is a potential inflammatory mediator, we sought to identify the mitochondrial morphology and mtDNA release in DCM. We firstly performed electron microscopic analysis. In db/ +, the structure of myocardial mitochondria was complete, the shape was round or oval, and the mitochondrial cristae were complete, rich, and arranged in parallel, whereas in db/db, the arrangement of mitochondria was disordered, swollen, and irregular, and most of the cristae were broken, fused, exfoliated, or even myelinated, and some vacuoles could be seen (Fig. 2A). These data confirmed that the mitochondria in cardiomyocytes from DCM were severely impaired. Subsequently, we performed co-immunostaining of mitofilin, the inner membrane protein, and dsDNA to assess the mtDNA release. As expected, we found that compared with db/ + mice, the signals of mitofilin in cardiomyocytes of db/db mice were significantly decreased. Interestingly, we observed a significant increase in the number of free dsDNA in the cytoplasm of cardiomyocytes from db/db mice (Fig. 2B). To quantitatively characterize the spatial relationship between mitochondria and free dsDNA, we calculated Mander’s overlap coefficients (MOC). As shown in Fig. 2B, the values of tM1 and tM2 in the db/db group were lower than those in the db/ + group, suggesting that the co-localization of mitochondria and free DNA decreased. To quantify the mtDNA release amount, we separated mitochondria and cytosol from the whole cell for the qRT-PCR experiment. The primer Tert and primer Loop 1–3 were used to detect nuclear DNA and mitochondrial DNA, respectively (Fig. 2C). Our results showed that Tert was not detected in the isolated and purified myocardial cytoplasmic DNA (Fig. 2D), suggesting that the free dsDNA in the cytoplasm was not the nuclear source, and the cytoplasmic DNA extracted in this study was of high purity, and no obvious nucleolysis occurred. After that, we used primer Loop 1–3 to detect mitochondrial DNA in the isolated and purified cytoplasmic DNA. Consistently, the levels of free Loop1, Loop2, and Loop3 in the cytoplasm of db/db mice were significantly higher than those of db/ + mice (Fig. 2E), indicating that the free dsDNA in the cytoplasm was mainly derived from mitochondria. Taken together, mitochondria were impaired with mtDNA release in cardiomyocytes from HFD-fed db/db. Fig. 2Mitochondria were impaired with mtDNA release in cardiomyocytes from HFD-fed db/db mice. A Representative transmission electron microscopy images of cardiomyocyte mitochondria in db/ + mice and db/db mice ($$n = 4$$). B Detection and quantification of dsDNA and mitofilin in cytoplasm of cardiomyocytes in two groups of mice by immunofluorescence double labeling, labeled mitochondria with mitofilin (red), labeled dsDNA with anti-dsDNA (green), and labeled nucleus with DAPI (blue). Co-localization analysis was done by Mander’s overlap coefficient for mitofilin with dsDNA (tM1) and dsNDA with mitofilin (tM2) ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.005$ vs db/ + group, using unpaired two-tailed t test, the arrow indicated free dsDNA). C Schematic diagram of extraction and detection of whole-cell DNA, mitochondrial DNA, and cytoplasmic free DNA. D *Quantitative analysis* of nuclear gene Tert expression in whole cell and cytoplasm of myocardial tissue in two groups of mice ($$n = 9$$). E Cytosolic mtDNA content in freshly purified cardiomyocytes of db/ + mice and db/db mice ($$n = 9$$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test)
## The cGAS-STING-IRF3/NF-κB pathway was activated in hearts of HFD-fed db/db mice
Given that mitochondrial damage led to mtDNA release into cytoplasm and cGAS is considered to be a cytoplasmic DNA biosensor, we next tested whether the cGAS-STING pathway was activated in hearts from HFD-fed diabetic mice. Expectedly, we found that the expression of cGAS and STING increased significantly in the cardiomyocytes of HFD-fed db/db mice (Fig. 3A). Immunofluorescence also showed that the expression of cGAS and STING was upregulated and clustered around the nucleus (Fig. 3B, C). In addition to activation of the cGAS and STING, the downstream targets, NF-κB and IRF3, were also activated in increased phosphorylated form (Fig. 3A, D, E), as well as the expression of NF-κB/IRF3-regulated IL-1β in the cardiomyocytes of HFD-fed db/db mice (Fig. 3A). Moreover, we found that the co-localization of NF-κB/IRF3 and nucleus increased by immunofluorescence, which further suggested their activation (Fig. 3D, E). Likewise, the increased mRNA levels of cGAS and STING in HFD-fed db/db mice were confirmed by RT-PCR (Fig. 3F), as well as the IL-1β and IL-18 (Fig. 3G). Taken together, these results suggested that the cGAS-STING-IRF3/NF-κB pathway was activated in hearts of HFD-fed db/db mice. Fig. 3The cGAS-STING-IRF3/NF-κB pathway was activated in hearts of HFD-fed db/db mice. A The protein levels of cGAS, STING, p-IRF3/IRF3, p-p65/p65, and IL-1β in mouse myocardium of each group ($$n = 6$$, *$P \leq 0.05$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test). B–E Detection and quantification of cGAS, STING, NF-κB, and IRF3 in mouse heart of each group by immunofluorescence. The arrow indicated the activated NF-κB and IRF3. The Mander’s tM1 indicated that NF-κB or IRF3 co-localized with nucleus, whereas the Mander’s tM2 indicated nucleus that co-localized with NF-κB or IRF3 ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.005$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test). F, G Relative mRNA level of cGAS, STING, IL-1β, and IL-18 in mouse myocardium of each group ($$n = 6$$, ***$P \leq 0.001$ vs db/ + group, using unpaired two-tailed t test)
## PA-induced mitochondrial ROS led to mitochondrial damage and mtDNA release in H9C2 cells
To investigate whether the lipotoxicity mediates the activation of the cGAS-STING-IRF3/NF-κB pathway in hearts of HFD-fed db/db mice, we next used the H9C2 cell line treated by PA as a high fat-induced lipotoxic cell model. As shown in Fig. 4A, PA treatment led to an increase of ROS level and mitochondrial damage, which were both reversed by NAC, an inhibitor of ROS, indicating that PA-induced ROS led to mitochondrial damage. To confirm that PA treatment leads to mtDNA release into the cytoplasm, we performed co-immunostaining of mitochondria and dsDNA. As shown in Fig. 4B, PA induced an increase in cytoplasmic free dsDNA in a dose-dependent manner. Further study by qRT-PCR analysis revealed that the increased cytosolic dsDNA induced by PA was derived from mitochondria (Fig. 4C). To investigate the source of ROS in the process of mitochondrial injury induced by PA, we pre-treated H9C2 cells with mitochondrial-specific ROS scavenger mito-TEMPO. We found that mito-TEMPO could significantly reduce PA-induced intracellular ROS activation and improve mitochondrial membrane potential (Fig. 4D). In addition, we evaluated the leakage of mtDNA by fluorescence confocal analysis of dsDNA, mitochondria, and nucleus in PA-treated H9C2 cells. The results showed that mtDNA leakage in the cytoplasm of H9C2 cells treated with PA increased, while mito-TEMPO treatment of H9C2 cells in advance could significantly reduce mtDNA leakage induced by PA (Fig. 4E). In summary, these data showed that PA caused mitochondrial damage and mtDNA leakage mainly by activating mitochondrial ROS.Fig. 4PA-induced mitochondrial ROS led to mitochondrial damage and mtDNA release in H9C2 cells. A ROS accumulation and the mitochondrial membrane potential (MMP) in H9C2 cells treated with PA for 24 h. ROS level was measured by DCFH-DA fluorescence and MMP was detected by JC-1 staining ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.005$, ***$P \leq 0.001$ vs indicated group, using one-way ANOVA followed by Turkey’s test). B Confocal fluorescence microscopic images of H9C2 cells after 24-h PA treatment, labeling dsDNA with anti-dsDNA (green), mitochondria with mito-tracker (red), and nuclei with DAPI (blue). The Mander’s tM1 indicated that mitochondria co-localized with the dsDNA, whereas the Mander’s tM2 indicated dsDNA that co-localized with mitochondria ($$n = 5$$, ***$P \leq 0.001$ vs ctrl group, using unpaired two-tailed t test). The arrow indicated free dsDNA. C Nuclear-encoded *Tert* gene expression in whole-cell and cytosolic extracts, and cytosolic mtDNA content from PA-treated H9C2 cells ($$n = 9$$, ***$P \leq 0.001$ vs ctrl group, using unpaired two-tailed t test). D ROS accumulation and MMP in PA-treated H9C2 cells ($$n = 4$$, PA: 0.2 mM for 2 h, mito-TEMPO: 0.1 mM for 2 h, *$P \leq 0.05$, ***$P \leq 0.001$ vs indicated group, using one-way ANOVA followed by Turkey’s test). E Confocal fluorescence microscopic images of H9C2 cells after PA treatment, labeling dsDNA with anti-dsDNA (green), mitochondria with anti-mitofilin (red), and nuclei with DAPI (blue). The Mander’s tM1 indicated that mitochondria co-localized with dsDNA, whereas the Mander’s tM2 indicated dsDNA that co-localized with mitochondria ($$n = 4$$, PA: 0.2 mM for 2 h, mito-TEMPO: 0.1 mM for 2 h, *$P \leq 0.05$, **$P \leq 0.005$, ***$P \leq 0.001$ vs indicated group, using one-way ANOVA followed by Turkey’s test)
## PA-induced activation of the cGAS-STING pathway in H9C2 cells
To elucidate the effect of PA-induced mtDNA release, we next evaluated the activation of the cGAS-STING in PA-treated H9C2. As shown in Fig. 5A, PA treatment led to an elevated protein level of cGAS and STING in a dose-dependent manner in H9C2 cells. In addition, the downstream targets, phosphorylated IRF3 and NF-κB, were also activated by PA treatment in a dose-dependent manner, as well as IL-1β, which was regulated by IRF3 and NF-κB (Fig. 5A). Given that the function of STING is determined not only by its content but also by its location. We next performed co-immunostaining of STING and Golgi matrix protein 130 (GM130), a Golgi marker. In H9C2 cells without PA treatment, STING was weakly co-localized with GM130, while PA treatment induced strong co-localization, which directly indicated the functional activation of STING (Fig. 5B). Consistently, IL-1β and IL-18 in the supernatant of H9C2 cells after PA treatment were also increased in a dose-dependent manner (Fig. 5C), as well as the mRNA levels of cGAS, STING, IL-1β, and IL-18 (Fig. 5D). Taken together, these results indicated that activation of the cGAS-STING pathway is involved in PA-induced myocardial inflammation. Fig. 5PA-induced activation of the cGAS-STING pathway in H9C2 cells. A The protein levels of cGAS, STING, p-IRF3/ IRF3, p-p65/p65, and IL-1β in H9C2 treated with PA ($$n = 6$$, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs ctrl group, NS: no significance, using unpaired two-tailed t test). B Confocal fluorescence microscopic images of H9C2 cells after treatment with 0.1-mM PA for 24 h, labeling STING with anti-STING (green), Golgi with GM130 (red), and nuclei with DAPI (blue). Quantification of STING was detected by integrated density (IntDen). The Mander’s tM1 indicated that Golgi co-localized with STING, whereas the Mander’s tM2 indicated STING that co-localized with Golgi ($$n = 5$$, ***$P \leq 0.001$ vs ctrl group, using unpaired two-tailed t test). C The concentrations of IL-1β and IL-18 in the supernatant after H9C2 cells were stimulated by PA with concentration gradient for 24 h ($$n = 6$$, **$P \leq 0.01$, ***$P \leq 0.001$ vs ctrl group, using unpaired two-tailed t test). D Relative mRNA level of cGAS, STING, and inflammatory genes IL-1β and IL-18 in H9C2 cells treated with PA for 24 h ($$n = 6$$, *$P \leq 0.05$, ***$P \leq 0.001$ vs ctrl group, using unpaired two-tailed t test)
## Extracted mtDNA is sufficient to activate cGAS-STING signaling in H9C2 cells
As the cGAS is not the mtDNA-specific DNA sensor, other sorts of DNA can also activate it. To confirm that mitochondria-derived mtDNA can activate the cGAS-STING pathway, we isolated and purified mtDNA to transfect into H9C2 cells. Then, the activation of the cGAS-STING pathway and downstream inflammatory activation level were detected by western blot and qRT-PCR. As shown in Fig. 6 A and C, cGAS and STING expression was activated after mtDNA transfection, accompanying the increased expression of IL-1β and IL-18. In addition, we performed co-immunostaining of STING and Golgi in PA-treated H9C2 cells to evaluate the activation of STING. The results indicated that STING aggregation to Golgi was significantly increased in mtDNA-transfected H9C2 cells (Fig. 6B), suggesting that STING was functionally activated by mtDNA treatment. In summary, these data showed that in PA-induced myocardial inflammation, the released cytoplasmic mtDNA acted as the ligand of the cGAS-STING system. Fig. 6Extracted mtDNA is sufficient to activate cGAS-STING signaling in H9C2 cells. A Flow chart of mitochondrial DNA extraction and transfection. B The protein levels of cGAS, STING, and IL-1β after mtDNA transfection of H9C2 cells ($$n = 6$$, mtDNA 3 μg for 24 h, **$P \leq 0.01$, ***$P \leq 0.001$ vs mtDNA[-] group, using unpaired two-tailed t test). C Confocal fluorescence microscopic images of H9C2 cells after transfected with 3 μg mtDNA for 24 h, labeling STING with anti-STING (green), Golgi with GM130 (red), and nuclei with DAPI (blue). Quantification of STING was detected by integrated density (IntDen). The Mander’s tM1 indicated that Golgi co-localized with STING, whereas the Mander’s tM2 indicated STING that co-localized with Golgi ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs mtDNA[-] group, using unpaired two-tailed t test). D Relative mRNA level of cGAS, STING, and inflammatory genes IL-1β and IL-18 after mtDNA transfection of H9C2 cells ($$n = 6$$, ***$P \leq 0.001$ vs mtDNA[-] group, using unpaired two-tailed t test)
## Knockdown of STING blocked the PA-induced inflammation and apoptosis in H9C2 cells
Given that STING functions as an effector in the cGAS-STING system, we sought to identify whether the inhibition of STING can reverse the effect of PA treatment in H9C2 cells. We employed siRNA to knock down the STING mRNA. In H9C2 cells transfected with STING siRNA, the expression of STING protein was significantly decreased (Fig. 7A, D), and the localization of STING in Golgi was significantly decreased (Fig. 7D), indicating that the transfection of STING siRNA was effective. As expected, STING knockdown could significantly inhibit the activation of NF-κB and the increase of IL-1β in H9C2 cells treated by PA for 24 h (Fig. 7A, E). Meanwhile, we have pre-treated H9C2 cells with C-176 (a small molecule inhibitor of palmitoylation of STING) and quantify IL-1β and p-p65 levels. The results showed that C176 could reduce the expression of IL-1β and phosphorylation of p65 induced by palmitic acid, which was similar to that of STING siRNA (Fig. 7B). In addition, STING knockdown could also significantly block the elevated secretion of IL-1β and IL-18 induced by PA treatment in the supernatant of H9C2 cells (Fig. 7C). Moreover, we also observed a significant anti-apoptotic effect of STING knockdown on PA-treated H9C2 cells (Fig. 7F). Taken together, these data directly indicated that knockdown of STING blocked the PA-induced inflammation and apoptosis in H9C2 cells. Fig. 7Knockdown of STING blocked the PA-induced inflammation and apoptosis in H9C2 cells. A The protein levels of STING, p-p65/p65, and IL-1β in PA-treated H9C2 cells after STING knockdown by siRNA ($$n = 6$$, ***$P \leq 0.001$ vs PBS group with NC siRNA; ###$P \leq 0.001$ vs PA group with NC siRNA, using one-way ANOVA followed by Turkey’s test). B The protein levels of p-p65/p65 and IL-1β in PA-treated H9C2 cells pre-treated with STING inhibitor C176 ($$n = 4$$, **$P \leq 0.01$, ***$P \leq 0.001$ vs ctrl group; #$P \leq 0.05$, ###$P \leq 0.001$ vs PA group, using one-way ANOVA followed by Turkey’s test). C The concentration of IL-1β and IL-18 in the supernatant of H9C2 cells transfected by STING siRNA ($$n = 6$$, ***$P \leq 0.001$ vs PBS group with NC siRNA; ###$P \leq 0.001$ vs PA group with NC siRNA, using one-way ANOVA followed by Turkey’s test). D Confocal fluorescence microscopic images of PA-treated H9C2 cells after STING knockdown by siRNA, labeling STING with anti-STING (green), Golgi with GM130 (red), and nuclei with DAPI (blue). Quantification of STING was detected by integrated density (IntDen). The Mander’s tM1 indicated that Golgi co-localized with STING, whereas the Mander’s tM2 indicated STING that co-localized with Golgi ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs PBS group with NC siRNA; ###$P \leq 0.001$ vs PA group with NC siRNA, using one-way ANOVA followed by Turkey’s test). E Representative images of immunofluorescence of NF-κB in H9C2 cells transfected by STING siRNA. The Mander’s tM1 indicated that NF-κB co-localized with nucleus, whereas the Mander’s tM2 indicated nucleus that co-localized with NF-κB ($$n = 4$$, *$P \leq 0.05$ vs PBS group with NC siRNA; #$P \leq 0.05$, ###$P \leq 0.001$ vs PA group with NC siRNA, using one-way ANOVA followed by Turkey’s test). F *Representative apoptosis* images of H9C2 cells treated by PA after NC siRNA or STING siRNA transfected, reflected by TUNEL staining, counterstained with DAPI (blue). The arrow indicated apoptotic cell ($$n = 6$$, ***$P \leq 0.001$ vs PBS group with NC siRNA; ###$P \leq 0.001$ vs PA group with NC siRNA, using one-way ANOVA followed by Turkey’s test)
## Inhibition of STING-ameliorated diabetic cardiomyopathy in HFD-fed db/db mice
Since that knockdown of STING blocked the PA-induced inflammation and apoptosis in H9C2 cells, we supposed STING as a potential therapeutic target of DCM. To this end, we used C176, a specific inhibitor of STING, to intraperitoneally inject into HFD-fed db/db mice (Fig. 8A). As the body weight (BW) of db/db mice was stable from the 7 weeks of HFD (Fig. 1A), we intended to start the UCG at the 7 weeks of HFD and perform once a week. Unexpectedly, C176-treated db/db mice exhibited improved cardiac parameters than vehicle-treated db/db at the 7 weeks of HFD. As shown in Fig. 8B, C176 had a slight effect on cardiac diastolic function in db/ + mice, but without statistical significance. However, inhibition of STING can reverse the cardiac dysfunction in db/db mice fed with HFD, showing an increase in E/A ratio and a shortening of isovolumic relaxation time (IVRT), suggesting an improvement in diastolic cardiac function. In addition, inhibition of STING could partially improve myocardial hypertrophy induced by HFD but had no significant effect on myocardial contractile function (Fig. 8B). To further study the pathological changes, we performed H&E staining to observe the cardiac hypertrophy and immunohistochemistry to observe the myocardial fibrosis. The results showed that HFD feeding induced ventricular hypertrophy and myocardial fibrosis in db/db mice, which could be partially reversed by C176 treatment (Fig. 8C). Also, HFD feeding induced the increase of inflammatory cytokine IL-1β in db/db mice, while C176 treatment reduced the production of IL-1β (Fig. 8C), which was also confirmed by western blot (Fig. 8D). Besides, C176 treatment also blocked the HFD feeding-induced activation of NF-κB in db/db mice by inhibition of phosphorylated P65 (Fig. 8E). In a word, these results suggest that STING functions as a potential therapeutic target for diabetic cardiomyopathy. Fig. 8Inhibition of STING-ameliorated diabetic cardiomyopathy in HFD-fed db/db mice. A Flow chart of mouse feeding (C176, STING inhibitor, 750 nmol per mouse daily in 200 μl corn oil, intraperitoneal injection. UCG, ultrasound cardiogram). B Representative echocardiographic images of each group. IVS (interventricular septal thickness), LVPW (posterior wall thickness of left ventricle), EF% (ejection fraction), E/A ratio and IVRT (isovolumic relaxation time) ($$n = 4$$, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs indicated group, NS: no significance, using one-way ANOVA followed by Turkey’s test). C Representative mouse myocardial images of the morphological analysis by H&E staining and fibrosis analysis labeled with CTGF, and COL1A1 by immunohistochemistry staining ($$n = 4$$). D The protein levels of IL-1β in mouse myocardium ($$n = 4$$, ***$P \leq 0.001$ vs indicated group, using one-way ANOVA followed by Turkey’s test). E The protein levels of p-p65/p65 in mouse myocardium ($$n = 4$$, ***$P \leq 0.001$ vs indicated group, using one-way ANOVA followed by Turkey’s test)
## Discussion
As DCM is an important cause of heart failure in diabetic patients (Isfort et al. 2014; Seferović and Paulus 2015), it is critical to identify therapeutic targets to prevent disease progression. Recently, a growing body of evidence has demonstrated that the cGAS-STING system plays a central role in numerous diseases such as obesity, nonalcoholic fatty liver disease (NAFLD), and acute kidney injury (Bai et al. 2017; Luo et al. 2018; Maekawa et al. 2019). In this study, we observed the presence of mitochondrial damage, cytosolic mtDNA, and activation of the cGAS-STING signaling pathway in cardiomyocytes from an obesity-related DCM mouse model. Using a PA-induced lipotoxicity cell model, we determined that PA-induced mtROS overproduction resulted in mtDNA release, which subsequently activated the cGAS/STING signaling pathway and its downstream targets, NF-κB and IRF3. The activated NF-κB/IRF3 finally promoted the expression of inflammatory factors, IL-18 and IL-1β. Notably, either downregulation of STING in H9C2 cells or STING inhibitor injection to HFD-fed db/db mice could block the lipotoxicity-induced inflammation and cell death. These findings suggest that STING is a novel, critical molecule involved in the progression of DCM In mature cardiomyocytes, mitochondria account for nearly $30\%$ of the volume. It is well known that mitochondrial dysfunction plays a vital role in the pathological process of diabetic cardiomyopathy (López-Armada et al. 2013). A clinical study has reported that mitochondria in cardiomyocytes of diabetic patients showed fragmentation (Montaigne et al. 2014). Mitofilin, an essential protein involved in mitochondrial inner crest formation, was reported to be downregulated in the diabetic heart by proteome analysis and transgenic overexpression of mitofilin attenuated diabetes mellitus-associated cardiac and mitochondrial dysfunction (Thapa et al. 2015). However, the molecular mechanism linking mitochondrial dysfunction and cardiac cell death and inflammation is unclear. Here, we also visually observed the damage of the inner mitochondrial membrane of cardiomyocytes in diabetic mice through electron microscopy (Fig. 2). Of note, we found decreased mitofilin and increased cytoplasmic mtDNA in H9C2 cells treated with palmitic acid and myocardial tissue of HFD-fed db/db mice (Figs. 2 and 4), suggesting that mitochondrial damage characterized by mitofilin decreased resulted in mtDNA leakage into the cytosol. In addition, the DNA sensor system, cGAS-STING signaling, was activated in PA-treated H9C2 cells and diabetic hearts (Figs. 3 and 5). The extracted mtDNA treatment alone was sufficient to activate cGAS-STING and the downstream targets in vitro (Fig. 6). Together, these results suggested that mitochondria-derived cytosolic DNA acts as a critical linker between mitochondrial dysfunction and the pathogenesis of DCM.
It is widely recognized that excessive mitochondrial ROS causes mitochondrial dysfunction and induces cellular dysfunction in cardiomyocytes by compromising ATP production (Fauconnier et al. 2007; Zorov et al. 2014). On the one hand, previous studies have shown that myocardial cells in T1D and T2D animal models and diabetic patients generally show increased ROS, as well as mitochondrial morphological changes, mainly including mitochondrial fragmentation, crest fracture, and swelling (Galloway and Yoon 2015; Jarosz et al. 2017). On the other hand, hyperglycemia-induced mitochondrial fragmentation can be reversed by stimulating the antioxidant superoxide dismutase (SOD), suggesting a causal relationship between ROS and mitochondrial dysfunction, and controlling mtROS levels may be a strategy for treating DCM (Schilling 2015; Westermeier et al. 2015). However, the role of hyperlipidemia-induced mtROS in the progression of DCM is unknown. In this study, we observed excessive production of ROS and impaired mitochondria in H9C2 cells treated by palmitic acid in a dose-dependent manner (Fig. 4A). Although incubation with ROS scavenger NAC could effectively reduce the formation of ROS and reverse the mitochondrial function (Fig. 4A), it is not sure whether the overproduction of ROS is derived from mitochondria as the ROS is not only produced by mitochondria. Notably, we further observed that the mitochondria-targeted antioxidant, mito-TEMPO, significantly inhibited the PA-induced myocardial ROS production (Fig. 4D), indicating the overproduction of mtROS in cardiomyocytes under hyperlipidemia. Consistently, a recently published study reported that injection of mito-TEMPO for 30 days reduced cardiomyocyte apoptosis and improved cardiac hypertrophy and dysfunction in diabetic mice (Ni et al. 2016). Furthermore, we discovered that mito-TEMPO could not only block the mtROS overproduction but also significantly reduce the leakage of mitochondrial DNA, suggesting a mechanism of mtROS-induced cytosolic DNA increase (Fig. 4E). Taken together, our data revealed an early regulated axis of lipid/mtROS/mtDNA in obesity-related DCM.
As we have known, mtDNA is thought to be similar to bacterial DNA and contains pro-inflammatory, unmethylated CpG motifs (Collins et al. 2004). Previous studies have shown that escaping mtDNA can inflame the heart and even cause heart failure (Konstantinidis and Kitsis 2012; Oka et al. 2012). In a normal physiological state, escaped mtDNA and damaged mitochondria can be digested and degraded by lysosome-mediated autophagy and mitophagy, whereas in a variety of disease states, such as blood pressure overload and ischemia–reperfusion injury, excess mtDNA accumulates and activates the Toll-like receptor 9 (TLR9), resulting in persistently activated inflammation response (Oka et al. 2012). In addition, it has been shown that oxidized mitochondrial DNA could directly activate pyrin domain-containing protein 3 (NLRP3) during apoptosis (Shimada et al. 2012). Here, we report activation of the cGAS-STING system, accompanied by increased cytoplasmic mtDNA, in HFD-fed db/db and PA-treated H9C2 (Figs. 2, 3, 4, and 5). Moreover, the cGAS-STING pathway could be activated by the extracted mtDNA treatment only in cultured H9C2 cells (Fig. 6). Our study identified cGAS-STING, not TLR9 receptor, as another mtDNA sensor to mediate lipotoxicity-induced myocardial dysfunction.
As a DNA sensor system, the cGAS-STING pathway was first discovered as a mediator of type I IFN inflammatory responses in immune cells to defend against viral and bacterial infections (Ma and Damania 2016; Marinho et al. 2017). A growing body of evidence has shown that the cGAS-STING pathway was also activated by host DNA, which aberrantly localized in the cytosol, contributing to increased sterile inflammation, insulin resistance, and the development of NAFLD (Isfort et al. 2014; Luo et al. 2018). Following the activation of STING signaling, TBK1 is recruited and activated via its phosphorylated C-terminal tail (CTT) (Zhang et al. 2019). The activated TBK1 acts as a scaffold to recruit IRF3, which is then phosphorylated in a TBK1-dependent manner. The phosphorylated IRF3 enters the nucleus and promotes the expression of target genes such as interferon (Li et al. 2019b; Tanaka and Chen 2012). On the other hand, TBK1 also plays a role as the activator of NF-κB, which could promote not only interferon expression but also a transcription of pro-inflammatory and chemokine factors (Abe and Barber 2014). Consistently, our results demonstrated that activation of the cGAS-STING pathway was accompanied by increases of the downstream mediators, IRF3 and p65 (one form of NF-κB), and the downstream inflammatory factors, IL-18 and IL-1β (Figs. 3 and 5). Of note, both knockdowns of STING in PA-treated H9C2 cells (Fig. 7) and inhibition of STING with C176 injection (Fig. 8) can remarkably ameliorate myocardial inflammation and apoptosis. These data suggest cGAS-STING/IRF3/NF-κB axis acts as a mediator in the progression of DCM.
## Conclusion
Our study demonstrated that lipotoxicity-induced mtDNA release led to cardiac cell death and fibrosis by activation of cGAS-STING signaling and subsequent inflammation in the obesity-related DCM mouse model. These findings underline the significance of cGAS/STING signaling as a potential therapeutic target in DCM, and the preclinical efficacy of STING inhibition as a new therapeutic strategy for the treatment of DCM.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 13 KB)
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|
---
title: Whole‐exome sequencing analyses in a Saudi Ischemic Stroke Cohort reveal association
signals, and shows polygenic risk scores are related to Modified Rankin Scale Risk
authors:
- Fahad A. Alkhamis
- Majed M. Alabdali
- Abdulla A. Alsulaiman
- Abdullah S. Alamri
- Rudaynah Alali
- Mohammed S. Akhtar
- Sadiq A. Alsalman
- Cyril Cyrus
- Aishah I. Albakr
- Anas S. Alduhalan
- Divya Gandla
- Khaldoun Al-Romaih
- Mohamed Abouelhoda
- Bao-Li Loza
- Brendan Keating
- Amein K. Al-Ali
journal: Functional & Integrative Genomics
year: 2023
pmcid: PMC10042957
doi: 10.1007/s10142-023-01039-7
license: CC BY 4.0
---
# Whole‐exome sequencing analyses in a Saudi Ischemic Stroke Cohort reveal association signals, and shows polygenic risk scores are related to Modified Rankin Scale Risk
## Abstract
Ischemic stroke represents a significant societal burden across the globe. Rare high penetrant monogenic variants and less pathogenic common single nucleotide polymorphisms (SNPs) have been described as being associated with risk of diseases. Genetic studies in Saudi Arabian patients offer a greater opportunity to detect rare high penetrant mutations enriched in these consanguineous populations. We performed whole exome sequencing on 387 ischemic stroke subjects from Saudi Arabian hospital networks with up to 20,230 controls from the Saudi Human Genome Project and performed gene burden analyses of variants in 177 a priori loci derived from knowledge-driven curation of monogenic and genome-wide association studies of stroke. *Using* gene-burden analyses, we observed significant associations in numerous loci under autosomal dominant and/or recessive modelling. Stroke subjects with modified Rankin Scale (mRSs) above 3 were found to carry greater cumulative polygenic risk score (PRS) from rare variants in stroke genes (standardized PRS mean > 0) compared to the population average (standardized PRS mean = 0). However, patients with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes (OR ($95\%$CI) = 1.79 (1.29–2.49), $$p \leq 0.0005$$), with the means of standardized PRS at or lower than 0. In conclusion, gene burden testing in Saudi stroke populations reveals a number of statistically significant signals under different disease inheritance models. However, interestingly, stroke subjects with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes and therefore, determining the potential mRS cutoffs to use for clinical significance may allow risk stratification of this population.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10142-023-01039-7.
## Introduction
Stroke is a major cause of morbidity and is the second leading cause of death worldwide. Over 13 million people have a stroke each year and around 5.5 million people will die as a result (Virani et al. 2021). Incidence, type of stroke and mortality rates vary markedly between countries and ancestral groups. Ischemic strokes are the most common type of stroke, and typically involve a disruption of blood flow to the brain parenchyma which causes brain cell death from a lack of oxygen. It may result from a number of processes including small vessel occlusions (SVOs) and large-artery atherosclerosis (Adams et al. 1993). More young people are affected with ischemic stroke in low- and middle-income countries than higher income countries (Lehman and Fullerton 2013; Lehman et al. 2018). Initial twin and family studies, primarily using linkage analysis, contributed to the initial knowledge of heritability studies in stroke (Flossmann et al. 2004). While such approaches found causal variants in various genes for monogenic stroke disorders, they had limited value in finding common variants impacting polygenic risk (Ekkert et al. 2021; Li et al. 2021). Genome-Wide Association Studies (GWAS) utilizing genome-wide genotyping arrays and/or whole exome sequencing (WES) have been successful in elucidating rare and common variants in various stroke subtypes (Li et al. 2021; Dichgans et al. 2021; Kumar et al. 2021).
Polygenic risk scores (PRS) utilizing cumulative, weighted risk scores for multiple genetic variants, with specific diseases/phenotypes, typically integrate known clinical risk covariates. They have been used with varying success in common diseases with multifactorial disease risk, including those with common and rare genetic underpinnings. Malik and colleagues combined stroke PRS data with Framingham risk scores and observed a significant association with ischemic stroke risk, although the prognostic value of the PRS was not substantially different from that of conventional clinical risk factors (Malik et al. 2014). However, more recent studies have shown utility with the use of PRSs in stroke-related phenotypes (Hachiya et al. 2020).
Saudi Arabia has seen an unprecedented adverse rise in modifiable risk factors for vascular disease over the last 30–40 years including poor diet, smoking and sedentary lifestyle, resulting in increases in dyslipidemia, type 2 diabetes, and hypertension which further exacerbate stroke and other vascular disease progression (Alhazzani et al. 2021). There are wide incidence differences across reported Saudi stroke studies ranging from ~ 16 to 58 cases per 100,000 person years (Alhazzani et al. 2018; Al Rajeh & Awada 2002). Such differences may be due to an interplay of study ascertainment biases as well as from significant primary and secondary health care delivery differences between private and public payer systems in Saudi Arabia, leading to a higher likelihood of undiagnosed diseases (Alqahtani et al. 2020). Recent studies indicate that the incidence of stroke is increasing rapidly with ischemic stroke being the dominant subtype affecting the Saudi populations (Alhazzani et al. 2018; Alqahtani et al. 2020; Al Rajeh & Awada 2002; Alqahtani et al. 2020).
*Performing* genetic studies in Saudi Arabian populations offers a unique opportunity for the discovery of novel genetic variants impacting disease risk due to a high rate of consanguinity amongst tribal pedigrees that make up the majority of the national population (Kari et al. 2014; Alkuraya 2012). We performed whole exome sequencing on 387 Saudi subjects with clinically diagnosed ischemic stroke. We focused on the analyses of exonic sequence data from a panel of 177 gene regions derived from highly curated stroke studies and utilized approximately 20,230 controls from the Saudi Human Genome Project. We then assessed the association of rare variants, primarily with ischemic stroke, and also evaluated PRSs within our study population.
## Study participants’ samples and data
During 2019–2020, samples and data from 387 subjects (inpatients and outpatients) who had been diagnosed with ischemic stroke and attending the following Neurology Clinics were collected for inclusion in this study: King Fahd Hospital of the University (KFHU), Al Khobar; King Fahd Hospital, Al Hafof; and Al Wajh Hospital, Dammam. Participants ranged in age from 19 to 81. The phenotype data of all subjects were reviewed by a neurology consultant to ascertain and verify the diagnoses and the phenotype uniformity among sites as well as eligibility according to the study criteria. Table 1 outlines the demographic and clinical characteristics of the 387 ischemic stroke subjects included in this study. The subtypes of ischemic stroke were determined according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification (Adams et al. 1993). The functional outcome of stroke patients was determined using modified Rankin Scale (mRS) at admission and at one-month post stroke follow-up. Table 1Saudi Ischemic Stroke Study Participants Clinical Characteristics. Clinical characteristics data collated from 387 ischemic stroke subjects diagnosed at neurology clinics at the following three Saudi Hospitals: King Fahd Hospital University (KFHU), Al-Khobar, and King Fahd Hospital, Alhafof and Al Wajh Hospital, Dammam. * Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classificationParameterTotal number of patients387Age (Mean ± SD)56.5 ± 15.8Age at diagnosis (Mean ± SD)55.6 ± 15.7Male sex N (%)223 (57.6)HospitalAL-WAJAH, Dammam, n = (%)94 (24.3)King Fahd Hospital, Al Hafof, n = (%)107 (27.6)KFHU, Alkhobar, n = (%)186 (48.1)TOAST* classificationLarge artery atherosclerosis, n = (%)78 (20.2)Cardio aortic embolism, n = (%)56 (14.5)Small artery occlusion, n = (%)253 (65.4)Modified Rankin Scale (mRS)0 N (%)51 (13.2)1 N (%)44 (11.4)2 N (%)219 (56.6)3 N (%)38 (9.8)4 N (%)17 (4.4)5 N (%)12 (3.1)6 N (%)6 (1.6)
## DNA extraction and sequencing
Peripheral blood samples were collected into EDTA tubes and stored in − 20 °C freezers at the research laboratories at the College of Medicine, Imam Abdulrahman bin Faisal University. Genomic DNA extraction from all samples was performed using Gentra Puregene Blood kits (Qiagen, USA) according to the manufacturer’s protocol. Whole exome sequencing libraries were generated using the SureSelect Human All Exon Kit v5 (Agilent, CA, USA) and sequenced on a HiSeq instrument (Illumina, CA, USA) using a standard paired-end sequencing protocol for SureSelectXT Library Prep and Target Enrichment System Version B.2 (Illumina, CA, USA).
## Read alignment, variant calling, and QC
Reads in the FASTQ files were aligned to the standard human genome reference (GRCh37) using Illumina’s Dynamic Read Analysis dor GENNomics (DRAGEN) Genomic Pipeline. Resultant BAM files were position-sorted and duplicate reads marked. Single-sample genomic variant call files (gVCF) were generated by the DRAGEN Germline Pipeline, and joint calling of all samples in the study cohort was performed by DRAGEN Joint Genotyping (Illumina, CA, USA).
## Principal components analysis (PCA) and Kinship
The KING algorithm was used for relatedness inference based on the genotype of exome SNPs (minor allele frequency [MAF] > 0.01). Estimated kinship coefficient and number of SNPs with zero shared alleles (identity by state [IBS]0) between a pair of individuals were plotted. Parent-offspring, sibling pairs, and unrelated pairs were visualized on the scatterplot to distinguish any separate clusters. Ancestry and Kinship Toolkit (AKT) was used to calculate PCAs and plot the results using 1000 genome project data. The study participant samples demonstrated a genetically matched background consistent with typical Saudi populations including African admixture, which is known to be evident in Saudi tribes primarily from East Africa (Fernandes et al. 2019).
## Variant annotation, filtering and prioritization
Variants were annotated with a program for annotating and predicting the effects of single nucleotide polymorphisms (SnpEff,v5.0) to predict the effects of the variants. Rare variants were defined as minor allele frequency (MAF) < $1\%$ in The Genome Aggregation Database (gnomAD) (v2.1.1). Intronic, synonymous, 3′ and 5′ UTR, upstream and downstream variants were identified and excluded from the analysis. The remaining rare variants were considered to be potentially deleterious variants. Genetic variants classified in ClinVar as “Likely pathogenic” or “Pathogenic,” and in Human Gene Mutation Database (HGMD) as disease-causing mutations (DM) for stroke were collected and curated together with research literature to serve as the knowledgebase for variant prioritization and classification (Stenson et al. 2003).
## Use of a comprehensive stroke gene panel
Numerous reports have identified genes associated with stroke by using data from monogenic and genome-wide association studies (GWAS). We used a comprehensive collation of genes with associations for monogenic causes of stroke which has been used in many clinical and research studies (Ilinca et al. 2019). Malek et al. in a large multi-ancestry GWAS of up to 67,162 stroke cases and 454,450 controls discovered 22 new stroke risk loci and validated 10 known stroke loci, bringing the total to 32 loci which are encompassed in the panel (Malik et al. 2018).
## Gene burden testing
The open-source software package Test Rare vAriants with Public Data (TRAPD) was used to perform a gene-based burden testing against public control databases. The software allows for adaptable filtering on various quality and frequency fields to ensure a well-controlled burden test. Gene burden test on our ischemic stroke WES cohort datasets was performed against whole exome or whole genome sequencing data available from approximately 20,230 individuals from the Saudi Human Genome Project (https://shgp.kacst.edu.sa).
We were able to utilize exonic data from 177 genes from the initial set of 214 stroke loci panel [48] after removal of mitochondrial genes; non-exonic regions such as 9p.21 and non-coding RNAs, and genes that had poor quality control and quality assurance filtering data in the Saudi Genome Project control dataset including ATP7A, FLNA, GLA, GPR143, PGAM4, and F9. Focusing on the a priori 177 loci derived from the stroke panel, we identified rare putative impactful variants within the cohort (Supplementary Table S1). The equivalent analysis was performed using the TRAPD burden testing pipeline and we observed that our cohort of 387 ischemic stroke subjects was significantly enriched for impactful alleles in several loci on the a priori stroke panel, compared to the control populations of approximately 20,230 Saudi population-based controls derived from the Saudi Genome Project.
The top 20 most significant signals for gene burden analyses under a dominant model ($p \leq 1$ × 10–5) and 14 signals with $p \leq 0.05$) under a recessive model are shown on Table 2 with FDR correction and a full list of association signals under both dominant and recessive models are shown for all 177 genes (Supplementary Table S2). Table 2 (top) shows the top 20 genes with $p \leq 1$ × 10−5 under a dominant model (F13A1, NF1, ACAD9, NOTCH3, MYLK, SH3PXD2A, TSC2, ADAMTS13, COL4A2, APP, FOXC1, COL1A1, TGFBR2, PDE4D, MYH7, DPM1, PGM1, FCGR2C, ZFHX3, PDE3A). Table 2 (top) shows the 14 most significant signals at $p \leq 0.05$ under a recessive model (PDE4D, KCNQ1, TREX1, CYP11B1, F5, HTRA1, CACNA1A, ZCCHC14, NBEAL2, FGA, PCNT, DPM1, LOC100505841, and SH3PXD2A). *Three* genes, SH3PXD2A, PDE4D, and DPM1, were significant under both the dominant and recessive models. Twenty-eight of the genic 32 GWAS loci contained among the 177 genes on the stroke panel were available for analysis and 12 showed gene burden associations at $p \leq 0.05$ under either dominant or recessive models (ZCCHC14, FGA, LOC100505841, SH3PXD2A, PRPF8, PDE3A, ZFHX3, SH2B3, TM4SF4, HDAC9–TWIST1, SMARCA4, and FOXF2).Table 2 (Top) 20 most significant dominant model signals and (bottom) $p \leq 0.05$ Recessive model signals from TRAPD gene burden testing in Saudi Ischemic Stroke subjects compared in up to 20,230 individuals from Saudi Human Genome ProjectGeneHeterozygote casesHomozygote casesHeterozygote controlsHomozygote controlsP: dominant modelP: recessivemodelF13A1100603.13E − 111NF11101224.84E − 101ACAD9901101.52E − 081NOTCH3422411181.77E − 080.405288MYLK310231133.60E − 080.170765SH3PXD2A21112655.16E − 080.042198TSC2480499155.68E − 080.791086ADAMTS13260182101.54E − 070.106345COL4A227121792.70E − 070.435679APP1204421.19E − 061FOXC161461.36E − 060.234242COL1A121014631.39E − 061TGFBR250201.41E − 061PDE4D236253111.58E − 062.20E − 06MYH7701102.16E − 061DPM141303.64E − 060.037399PGM1902513.88E − 060.073402FCGR2C812626.40E − 060.108062ZFHX3PDE3A10514401791768037.23E − 068.31E − 060.3087260.17355GeneHeterozygote CasesHomozygote CasesHeterozygote ControlsHomozygote ControlsP: Dominant modelP: RecessivemodelPDE4D236253111.58E − 062.20E − 06KCNQ1161486150.7468535.99E − 05TREX170126110.2934760.000408CYP11B119020950.0011790.001053F525131480.0006060.001374HTRA1224200.0920680.001395CACNA1A202217140.0002020.009701ZCCHC1411220470.0774870.013894NBEAL272218401180.6233180.015113FGA15125360.0533390.016894PCNT7961374880.0001120.029154DPM141303.64E − 060.037399LOC100505841313900.0758190.037399SH3PXD2A21112655.16E − 080.042198
## Polygenetic risk score generation
Rare, impactful variants (listed in Supplementary Table S1) identified in stroke genes were included to calculate polygenetic risk scores (PRS) for stroke for each individual study subject to represent the cumulative risk of carrying one or more of these rare variants. ( Malik et al. 2018). PRSice-2 software was employed to calculate PRS by setting an equal effect size (beta = 1) for each variant (Choi and O’Reilly 2019). The resulting PRS was standardized for association tests with clinical variables such as age of diagnosis, mRS, and classifications of stroke. It is acknowledged that there may be an under- or over-estimation of the effect size of these rare variants. However, when there is an actual effect, the association, albeit with less accurate estimates, could be detected. The validity of this approach was evident in the initial effort to estimate the combined effect of multiple genetic variants before a more sophisticated statistical approach was developed (Zheng et al. 2008). In addition, as the rare variant PRS was calculated using PRSice-2, it was restricted to the specific set of rare variants that were identified without consideration of other common variants based on LD patterns.
## Principal component analysis
The common variants of the whole exome sequencing data from the stroke study participants show expected clustering when compared to the world’s major populations using a standard principal component analysis pipeline (Fig. 1). This population stratification data was used to mitigate false attributions in the association analyses components. Fig. 1Principal component analysis (PCA) of common SNP genotypes with 1000 genome projects (1KGP) populations reference panel. The x- and y-axes denote the value of two components of PCA (PC1, PC2), with each dot in the figure representing one individual. The color for individuals from 1000 genome projects, Europeans (EUR), East Asians (EAS), Admixed Americans (AMR), South Asians (SAS), and Africans (AFR) are red, blue, green, purple, and orange, respectively. The color for individuals belonging to the Stroke Disease study group is illustrated in black
## Associations of polygenic risk with clinical variables
The overall distribution of polygenic risk score (PRS) representing the overall genetic burden from rare, impactful variants in stroke genes among all 387 ischemic stroke study participants subjected to whole exome sequencing deviated from normality and was right-skewed (Fig. 2) with median [IQR] of 8.61 [6.59,10.53], minimum of 1.53, and maximum of 18.75. PRS was not associated with age of diagnosis, ischemia diagnosis, and classification of stroke such as large artery atherosclerosis, cardio aortic embolism, and small artery occlusion (Table 3). However, a significant association of PRS and mRS, which measures the degree of disability post-stroke, was identified (regression coefficient ($95\%$CI) = 0.15 (0.03–0.27), $$p \leq 0.02$$). PRS was found to increase the risk of developing greater disability post-stroke event (mRS > 3), when compared to patients with lower mRS (mRS ≤ 3) (OR ($95\%$CI) = 1.79 (1.29–2.49), $$p \leq 0.0005$$) (Table 3).Fig. 2Distribution of rare variant Polygenic Risk Score (PRS) among 387 Saudi Ischemic Stroke subjects. PRS statistics: Mean ± SD = 8.7 ± 3.0; Median[IQR] = 8.6 [6.6,10.5]; Min = 1.53; Max = 18.6Table 3Association of clinical phenotypes with polygenic risk score (PRS) constructed with rare impactful variants in stroke genes. The PRS was standardized to mean at 0 and SD of 1Continuous phenotypeRegression coefficient ($95\%$CI)p-valueAge of Diagnosis1.15 (− 0.43–2.72)0.15Modified Rankin Scale (mRS)0.15 (0.03–0.27)0.02Dichotomized phenotypeOR ($95\%$CI)p-valueHigh mRS ($\frac{4}{5}$/6, $$n = 35$$) vs low mRS ($\frac{0}{1}$, $$n = 95$$)1.56 (1.09–2.23)0.015High mRS ($\frac{4}{5}$/6, $$n = 35$$) vs lower mRS ($\frac{0}{1}$/$\frac{2}{3}$, $$n = 351$$)1.79 (1.29–2.49)0.0005Diagnosis (= ischemia)1.14 (0.89–1.47)0.31Large artery atherosclerosis1.00 (0.75–1.34)0.98Cardio aortic embolism0.87 (0.71–1.07)0.2Small artery occlusion1.15 (–0.43–2.72)0.15 Stroke subjects with mRSs above 3 were found to carry greater cumulative genetic risk from rare variants in stroke genes (standardized PRS mean > 0) compared to the population average (standardized PRS mean = 0). However, patients with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes, with the means of standardized PRS at or lower than 0 (Fig. 3).Fig. 3Box-whisker plot of standardized Stroke Polygenic risk score in a Saudi Stroke Cohort by Modified Rankin Scale (mRS) groups. The standardized Polygenic Risk Score (PRS) is illustrated on the y-axis with the Modified Rankin Scale (mRS) groups shown on the x-axis
## Discussion
In this study, WES was performed on 387 individuals diagnosed with ischemic stroke. We initially focused on known, or putative, pathogenic exonic variants evident in 177 genes prioritized from a comprehensive panel of loci associated with monogenic causes of stroke as well as recent GWAS statistically significant signals (Ilinca et al. 2019). This panel has been used in exome sequencing interpretation of individuals from multi-incident stroke families to generate and assess putative pathogenic variants amongst the probands and wider family members (Ilinca et al. 2020). It has also been utilized in cerebral small vessel disease (CSVD) which revealed putative pathogenic variants in multiple loci (Monkare et al. 2022). Using the TRAPD gene burden testing pipeline, filtered on our list of 177 genes, we found significant enrichment for pathogenic or likely pathogenic stroke variants in numerous genes in our cohort using datasets from the Saudi Genome Project as controls.
A number of the top signals in our study are outlined below. The coagulation factor XIII A chain (F13A1) gene encodes the coagulation factor that is the last component activated in the blood coagulation cascade. Factor XIII deficiency is typically classified into two categories: type I deficiency, characterized by the lack of both the A and B subunits; and type II deficiency, characterized by the lack of the A subunit alone. These defects can result in defective wound healing and a tendency for lifelong bleeding. A review of common variants association analyses of F13A1 with stroke phenotypes has shown mixed findings. However, a large Women’s Health Initiative (WHI) study of 2,045 of post-menopausal women, significant risk was observed for both ischemic stroke and for combined ischemic and hemorrhagic between two common F13A1 variants and with hormone replacement therapy (Huang et al. 2012). A Dutch study also showed significant associations between common variants in F13A1 with ischemic stroke in young women with the effect more pronounced with oral contraception use (Pruissen et al. 2008).
Neurofibromin 1 (NF1) gene encodes a negative regulator of the Ras signal transduction pathway and a number of NF1 mutations have been linked to neurofibromatosis type 1 which is commonly associated with malignant tumors and cardiovascular or cerebrovascular complications (Napolitano et al. 2022). Neurofibromatosis type 1 also increases the risk of vasculopathies and arterial wall weakness and can lead to complications such as hemorrhagic stroke, ischemic stroke, and multi-domain cognitive impairment (Napolitano et al. 2022).
Acyl-CoA dehydrogenase family member 9 (ACAD9) encodes a member of the acyl-CoA dehydrogenase family, a family of proteins that are localized in the mitochondria and are involved in beta-oxidation of fatty acyl-CoA. Mutations in this gene cause acyl-CoA dehydrogenase family member type 9 deficiency which often results in intellectual disability and neurologic dysfunction. A homozygous variant in ACAD9 was identified in the proband of a Swedish family where family members reported stroke with intracerebral bleeding and progressive muscle and heart failure (Ilinca et al. 2020). A case report of a death of a teenager with ACAD9 deficiency with Reye-like episode and cerebellar stroke has also been reported (Huang et al. 2012).
Phosphodiesterase 4D (PDE4D) encodes up to 9 different isoforms whose functional proteins degrade the second messenger Cyclic adenosine monophosphate (cAMP), a key signal transduction molecule in multiple cell types, including vascular cells (Munshi & Kaul 2008). Numerous case–control studies have been performed to assess the association between PDE4D variants and ischemic stroke risk among different ancestral populations. In particular, the so-called “SNP83” (rs966221) association with has been robust in many of these studies (Munshi & Kaul 2008; Xu et al. 2010). PDE4D is associated with inflammation and reduced PDE4D is thought to increase the risk of atrial fibrillation, which in turn increases stroke risk (Jørgensen et al. 2015). Several studies have shown a robust association of PDE4D variants with ischemic stroke in young individuals (Yue et al. 2019).
Potassium voltage-gated channel subfamily Q member 1 (KCNQ1) encodes a voltage-gated potassium channel protein which is required for the repolarization phase of the cardiac action potential and forms multimers with KCNE1, KCNE3 and potassium channel proteins. Mutations in KCNQ1 are associated with familial atrial fibrillation and hereditary long QT syndrome 1 which can impact stroke risk (Jørgensen et al. 2015; Lavy et al. 1974). An increased genetic burden of rare deleterious KCNQ1 variants in Polish subjects with large‑vessel ischemic stroke were identified using second-generation sequencing (Janicki et al. 2019).
Three prime repair exonuclease 1 (TREX1) encodes a nuclear protein with 3′ exonuclease activity and play a role in DNA repair and serve as a proofreading function for DNA polymerase. Mutations in this gene result in diseases of the immune system including Aicardi-Goutieres syndrome and other autoimmune-type diseases which can put subjects at a higher risk of ischemic stroke. Uemura 2023 investigating the prevalence of Mendelian stroke genes mutations in monogenic cerebral small vessel stroke patients aged 55 years or younger from a Japanese stroke registry identified a TREX1 pathogenic genetic variants in one stroke subject (Uemura et al. 2023). A large Mendelian Stroke Consortium also identified pathogenic clinical variants in TREX1 (Grami et al. 2020).
The distribution of the polygenic risk score analyses showed that the stroke subjects in this Saudi cohort each, on average, carry over 8 rare, impactful variants. PRS analyses performed on a weighted cumulative risk from rare, impactful variants among ischemic stroke participants with high Modified Rankin Scale (mRS), i.e., 4, 5, 6 versus mRS 0, 1, 2, 3 showed a significant association (OR ($95\%$CI) = 1.79 (1.29–2.49), $$p \leq 0.0005$$). Determining the potential mRS cutoffs to use for clinical significance within a highly consanguineous population like that in Saudi Arabia may yield translational value, such as risk stratification, especially with the additional of common and rare variants to the PRS from ongoing stroke genome-wide studies.
In Saudi Arabia, undetected or untreated vascular disease is a significant health and financial burden,(Walli-Attaei et al. 2020a, b; Bindawas & Vennu 2016). There is a compelling need for implementation of primary and secondary stroke prevention strategies in Saudi Arabia due to an increasing incidence rate with the mortality rate projected to almost double by 2030 (Robert et al. 2018; Bindawas & Vennu 2016). In this study using gene-burden analyses in 387 Saudi Arabian ischemic stroke subjects and 20,230 controls from the Saudi Human Genome Project, we observed significant associations in dozens of loci under autosomal dominant and/or recessive modelling. Stroke subjects with Modified Rankin Scale (mRSs) above 3 were observed to have a greater cumulative PRS from rare variants in stroke genes when compared to the population average. Interestingly, stroke subjects with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes (Alokley and Albakr 2022). Determining the potential mRS cutoffs to use for clinical significance may allow risk stratification of this population.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (XLSX 874 KB)Supplementary file2 (DOCX 43 KB)
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|
---
title: Exploring association between pseudoexfoliation syndrome and ocular aging
authors:
- Ugne Rumelaitiene
- Martynas Speckauskas
- Abdonas Tamosiunas
- Ricardas Radisauskas
- Tunde Peto
- Morten Bøgelund Larsen
- Dalia Zaliūniene
journal: International Ophthalmology
year: 2022
pmcid: PMC10042963
doi: 10.1007/s10792-022-02486-0
license: CC BY 4.0
---
# Exploring association between pseudoexfoliation syndrome and ocular aging
## Abstract
### Purpose
Within a population-based follow-up study, to examine the 10-year incidence of pseudoexfoliation syndrome (PEX), possible risk factors for PEX and its association with ocular aging of the cornea, lens and retina.
### Methods
The baseline examination was conducted in 2006 on a random sample of 1,033 adult participants from Kaunas city (Lithuania) population of whom 631 had ophthalmic examination data at attendance of the 10-year follow-up in 2016. Detailed examination of the anterior and posterior segment of the eye was carried out. After diagnostic mydriasis PEX was diagnosed by the presence of typical grayish-white exfoliation material on the anterior capsule surface of the lens. The participants were divided to PEX and non-PEX groups.
### Results
PEX prevalence increased from 9.8 to $34.2\%$ from baseline to 10-year follow-up. Nuclear cataract was common both in the PEX group ($66.7\%$) and in those without PEX ($72.2\%$), but this difference did not reach statistically significantly increased risk of developing cataract in those with PEX (OR 1.2; $$p \leq 0.61$$). Central corneal thickness (CCT) was thinner in the PEX group (529 ± 34 μm) and in the oldest group (525 ± 36 μm) ($p \leq 0.001$). Compared to baseline, corneal curvature (CC) became flatter in both groups (7.6 ± 0.27 vs 7.7 ± 0.26 mm; $p \leq 0.001$) during the follow-up, but the difference did not reach significance between groups. Corneal astigmatism was most commonly with-the-rule in both groups (37 ($50.0\%$) vs 148 ($68.5\%$); $p \leq 0.05$). Age, sex and PEX had no influence on age-related macular degeneration distribution.
### Conclusion
The prevalence of PEX increased significantly with age in our population, with those with PEX having thinner and flatter corneae, but no difference in cataract and age-related macular degeneration characteristics.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10792-022-02486-0.
## Introduction
Pseudoexfoliation syndrome (PEX) is an age-related disorder in which greyish-white flakes accumulate in the anterior segment of the eye. PEX pathogenesis is not completely understood [1]. PEX fibrils appear to be produced by various unrelated endothelial, epithelial, mesenchymal cell types, including lens epithelial cells, trabecular meshwork cells, ciliary epithelial cells, smooth muscle cells and fibroblasts [2] indicating that PEX might be a part of a generalized ageing disorder [3].
Approximately 0.2–$30.0\%$ of the global population older than 60 years of age is affected by PEX [4, 5]. The highest PEX prevalence rates, as high as $40.6\%$ in those aged 80 years or over [6], have been reported in Nordic countries [6, 7]. Clinically unilateral ocular involvement varies from 48.0 to $76.0\%$ of patients. Progression to bilateral PEX was described in up to 50.0–$71.0\%$ of patients within 5–12 years after diagnosis [8, 9].
A possible correlation has been established between PEX and steeper corneal curvature (CC), central corneal thickness (CCT) and nuclear lens opacifications [10, 11], but not with age-related maculopathy [10].
There is no clearly established gender predilection [12] with conflicting data between Kiliç’s finding for a significant relationship between PEX and advancing age and male sex [13], while the 12-year follow-up of Reykjavik Eye Study, Iceland, found an association with older age and female sex [9]. We are yet to find any follow-up studies that examine PEX and its associations with ophthalmological changes in Baltic countries. In 2006, a population-based epidemiological study that was part of larger Health, Alcohol and Psychosocial Factors In Eastern Europe (HAPIEE) [14] study was conducted in the Hospital of Lithuanian University of Health Sciences. HAPPIE examined the potential associations of ophthalmological and cardiovascular diseases. After 10 years [2016], those willing and able to come back for a follow-up was re-examined.
The aim of this paper was to determine associations of PEX with ocular changes in Lithuanian urban population and identify possible risk factors of PEX 10 years after the baseline study.
## Study sample/study population
During 2006–2008, 7087 subjects from Kaunas city (Lithuania) participated in international Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE) study. In 2006, out of the 7087 invited, 1065 individuals participated in the ophthalmological sub-study [14]. In 2016–2017, 686 subjects were invited and examined in a 10-year follow-up study. The study was approved by the Regional Bioethics Committee and was carried out in accordance with the Declaration of Helsinki. During the study, informed consent was obtained from each participant.
At HAPPIE baseline visit in 2006, ophthalmological examination was carried out on 1,033 participants. Ten years later, 686 returned for a follow-up examination (response rate of $66.4\%$; 239 males ($37.9\%$) and 392 females ($62.1\%$)). Altogether, 347 individuals did not return due to death ($$n = 164$$) and migration/refusal ($$n = 183$$).
Of the 686 examined, 55 respondents were not included in the current data analysis as their PEX status could not be determined due to trauma/phthisis, pseudophakia or aphakia in both eyes and lens subluxation, resulting in a cohort of 631 to be included in this study.
Weight and height were measured with a calibrated medical scale and without shoes or heavy clothes. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (kg/m2). Normal weight was defined as BMI < 25.0 kg/m2, overweight as BMI ≥ 25.0–29.99 kg/m2 and obesity as BMI ≥ 30.0 kg/m2 [15].
## Study instrument
All participants underwent ophthalmological examination according to a standard examination protocol and same methodology as at baseline [16–18]. The examination was carried out by two trained and certified ophthalmologists who had no access to the subjects’ medical history.
PEX was diagnosed by slit-lamp examination after diagnostic mydriasis with 1 drop of $1\%$ cyclopentolate. PEX was confirmed as definite by the presence of typical grayish-white exfoliation material on the anterior capsule surface of the lens (complete or partial peripheral band and⁄ or a central shield). Other changes associated with PEX such as grayish-white deposits elsewhere in the anterior chamber (iris, cornea), precapsular frosting or haze, supported the diagnosis of PEX. PEX was deemed suspect/possible if precapsular frosting or haze was seen. The participants were classified as having PEX if any typical pseudoexfoliation material was present in at least one eye. For statistical analysis, we used data of the respondents with definite PEX diagnosis as the PEX group. Persons with suspected PEX were grouped together with those without any signs of the PEX.
All study respondents answered a standard questionnaire regarding lifestyle, subjective health and ophthalmological pathology (supplement 1).
## Ophthalmological examination
For the 10-year follow-up study, 1262 eyes of 631 individuals' ophthalmic examination results were included. Lens opacification was evaluated at slit lamp by LOCS III international classification: NO/NC—nuclear opalescence/color (evaluation 0.1 to 6.9), C—cortical, P—posterior subcapsular (evaluation 0.1–5.9) [19]. Cataract was evaluated in 1262 eyes.
After diagnostic mydriasis as above, fundus photographs centered on the fovea were taken using Canon CF-60Uvi (Canon Medical Systems, USA). Retinal images were graded by trained and certified ophthalmic graders at Moorfields Eye Hospital Reading Centre in London, UK, primarily based on the International Classification for age-related macular degeneration (AMD). The grading of photographs was carried out by the same graders for both baseline and follow-up studies data [20, 21]. Altogether, 1262 eyes had retina images graded; of these, 45 ($3.6\%$) in baseline and 65 ($5.2\%$) in follow-up had ungradable images due to cataract.
Central corneal pachymetry was measured with pachymeter (ALCON OCUSCAN RxP, Alcon Laboratories inc., USA), in auto mode, averaging 10 readings. In the 10-year follow-up, CCT was measured in 1260 eyes, with only 2 eyes' measurement missing. In the baseline study, eyes were selected randomly for this examination and so only 304 eyes were available for comparison.
Keratometry was taken using the Auto keratorefractometer (ACCUREF-K 9001, SHIN–NIPPON Commerce, inc., Japan). K1, K2, K1-axis, K2-axis and corneal curvature measurements were recorded from keratometry. Corneal astigmatism was calculated as follows: K1–K2 = corneal astigmatism (cylinder (diopters (D)). If the cylinder was < 1.0D, it was considered as no corneal astigmatism; if it was ≥ 1.0D, it was considered as corneal astigmatism. Corneal astigmatism was classified as with-the-rule (ax 90 ͦ ± 30 ͦ), against-the-rule (ax 0 ͦ ± 30 ͦ) and oblique (all the left). The corneal curvature (CC) results were documented from auto keratorefractometer. CC and astigmatism were recorded reliably in 1182 eyes; in 80 eyes dry eye prevented reliable measurements to be taken.
For cataract, AMD, CCT, CC, corneal astigmatism measurements each eye was kept in the analysis. For the incidence of PEX the same eye of the same subjects was evaluated.
## Statistical analysis
Statistical analysis was performed using IBM SPSS Statistics version 20 software. Descriptive statistics were applied for various data points in the PEX and non-PEX groups. Unilateral and bilateral PEX cases were separated into subgroups. Quantitative variables were presented as median, and interquartile range or mean and standard deviation (SD). Categorical data were presented as number (percent).
Data normality detection of continuous variables was checked using Kolmogorov–Smirnov test. In case of non-normality, medians and interquartile ranges (IQR) were calculated and Mann–Whitney U-test was used to compare continuous data between groups. Chi-square (χ2) test or Fisher exact 2-sided test was used to compare categorical variables. For ordinal data χ2 linear-by-linear association test was used for confirmation of the linear trend. The comparison of proportions between groups was performed using z test. McNemar's χ2 test was used to assess the difference between paired proportions. Quantitative variables were compared with Wilcoxon test.
Binary logistic regression analyses were conducted with PEX as predictor controlling for age and gender. Odds ratios (OR) and $95\%$ confidence intervals (CI) of OR were calculated for the risk of new PEX cases and the dependence of risk factors.
## Results
Altogether, 631 participants had full examination (392 ($62.1\%$) male and 239 ($37.9\%$) female). Baseline and follow-up characteristics are presented in Tables 1 and 2. The prevalence of PEX in the baseline cohort (2006–2008) was $9.8\%$ (Table 2) [16].Table 1The distribution of pseudoexfoliation syndrome in follow-up study by sex and affected eye (unilateral, bilateral)Non-PEX, n (%)PEX, n (%)p-valuePEX, n (%)p-valueUnilateralBilateralMale154 (64.4)85 (35.6)$p \leq 0.0535$ (41.2)50 (58.8)$p \leq 0.05$Female261 (66.6)131 (33.4)51 (38.9)80 (61.1)Total415 (65.8)216 (34.2)86 (39.8)130 (60.2)Non-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndromeTable 2The distribution of pseudoexfoliation syndrome in baseline and follow-up studiesNon-PEX, n (%)PEX, n (%)Total, n (%)Unilateral PEX, n (%)Bilateral PEX, n (%)Total, n (%)p-valueBaseline study569 (90.2)62 (9.8)631 (100.0)37 (5.9)25 (4.0)62 (9.8)$p \leq 0.01$Follow-up study415 (65.8)216 (34.2)631 (100.0)86 (13.6)130 (20.6)216 (34.2)$p \leq 0.01$Non-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndrome During 10 years of follow-up (2016–2017) PEX prevalence increased to $34.2\%$ (216 subject, (85 ($39.4\%$) male and 131 ($60.6\%$) female) (Table 1). There was no statistically significant difference of PEX frequency between males and females, 35.6 and $33.4\%$, respectively ($p \leq 0.05$). Mean age was significantly higher in the PEX group compared to the non-PEX group (73.01 ± 7.97 years vs 68.70 ± 8.16 years, $$p \leq 0.001$$).
The subjects’ distribution by sex and PEX occurrence is shown in Table 1.
PEX affected males and females in all age groups equally, while there was an increasing trend for the 10-year age-brackets: $23.3\%$ of participants aged 55–65 years, $32.1\%$ of those aged 66–75 years, and $47.3\%$ of those aged 76–83 years were affected by PEX, this trend was statistically significant ($p \leq 0.001$) (Table 3). Comparing age group 55–65 and 66–75, 55–65 and 76–83, 66–75 and 76–83, significance was reached ($p \leq 0.05$; Table 3).Table 3The distribution of age groups by pseudoexfoliation syndrome in follow-up studyAgeNon-PEX, n (%)PEX, n (%)p-value55–651158 (76.7)48 (23.3)0.00166–752148 (67.9)70 (32.1)76–833109 (52.7)98 (47.3)Mean (± SD)68.70 (8.16)73.01 (7.97)0.001Median68.074.01´2´3Age groups compared: 55–65 and 66–75; 55–65 and 76–83; 66–75 and 76–83Non-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndrome Those with PEX were statistically older in the follow-up study, age median in the PEX group was higher than that in the non-PEX group (74 vs 68 years respectively; $p \leq 0.001$; Table 3), and in the baseline study age medians in the PEX and non-PEX groups were 68 years versus 59 years ($p \leq 0.001$).
At baseline, there were 37 unilateral and 25 bilateral PEX cases, increasing to 86 unilateral ($13.6\%$) and 130 bilateral ($20.6\%$) cases in the 10-year follow-up (Tables 1 and 2). Of those with unilateral PEX at baseline, 20 became bilateral and nineteen ($48.7\%$) remained unilateral during the 10-year follow-up ($p \leq 0.001$). A total of 154 ($71.3\%$) new PEX cases were diagnosed at the 10-year follow-up (Table 2).
PEX risk factors of lifestyle are presented in Table 4. There was no statistically significant difference between those affected by PEX and those who were not PEX-subjects, in univariate logistic regression analysis. Table 4Distribution of life style and sociodemographic factors in pseudoexfoliation syndrome and pseudoexfoliation syndrome not diagnosed groupsRisk factorsNon-PEX, n (%)PEX, n (%)p-valueSmokingNever285 (68.7)161 (74.5)0.128Former86 (20.7)42 (19.4)Current44 (10.6)13 (6.1)Alcohol< 1time/month, never274 (66.0)145 (67.1)0.7421–4 time/month120 (28.9)63 (29.2)> 1 time/week21 (5.1)8 (3.7)BMI (kg/m2)< 25.078 (19.2)33 (15.6)0.50225.0–29.99150 (36.9)79 (37.3)≥ 30178 (43.8)100 (47.2)EducationPrimary32 (7.7)24 (11.1)0.334Secondary143 (34.5)75 (34.7)University240 (57.8)117 (54.2)Marital statusSingle150 (36.1)85 (39.4)0.429Married265 (63.9)131 (60.6)BMI body mass index; Non-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndrome Age, gender, alcohol consumption, BMI, education and marital status did not increase the probability of having PEX.
In multivariate logistic regressions only age increased the risk of having PEX significantly ($p \leq 0.001$). Secondary education, in comparison with primary education showed a tendency for higher risk of having PEX in men ($$p \leq 0.08$$), but did not reach statistical significance.
Adjusting by multivariate risk factors, alcohol consumption 1–4 time/month and alcohol consumption > 1 time/week, marital status—married, former and current smoking, and normal weight reduced the probability of developing PEX in men, but not significantly.
Similarly, secondary and university education, normal weight and overweight reduced the probability of having PEX in women, but not significantly.
## Cataract
Of the available 1262 eyes, some degree of lens opacities was diagnosed in 1116 ($95.1\%$) eyes and no cataract—in 58 ($4.9\%$) eyes. Prevalence of cataract was significantly higher in the oldest age group, there was a $43.4\%$ ($95\%$ CI 5.939–316.572, $p \leq 0.001$) increase in those in the oldest compared to the youngest groups. Comparing the youngest and middle age group, the risk to have cataract significantly increased 3.4 times ($95\%$ CI 1.833–6.330; $p \leq 0.001$). Cataract was diagnosed more frequently in females ($63.3\%$) than in males ($36.7\%$) ($$p \leq 0.01$$). There were statistically significantly more cataract diagnosed in the non-PEX group when compared to the study subjects with PEX (73.4 vs $26.6\%$, $p \leq 0.05$). Nuclear cataract was the most common type ($70.7\%$) with no statistically significant difference between PEX/non-PEX groups. Mixed cataract was found frequently both in the non-PEX and the PEX groups (27.4 vs $28.9\%$; $p \leq 0.05$) (Table 5). We noticed a tendency in the PEX group to have a bigger risk of developing cataract by $21\%$ ($95\%$ CI 0.576–2.574, $$p \leq 0.61$$); however, this trend was not statistically significant. Table 5Cataract distribution in pseudoexfoliation syndrome and pseudoexfoliation syndrome not diagnosed groups in follow-up study*Non-PEX, n (%)PEX, n (%)Total, n (%)p-valueCataract819 (73.4)297 (26.6)1116 [100]$p \leq 0.05$Cataract forms Nuclear591 (72.2)198 (66.7)789 (70.7)$p \leq 0.05$ Cortical2 (0.2)02 (0.2) Subcapsular2 (0.2)02 (0.2) Mixed224 (27.4)99 (33.3)323 (28.9)*Each eye was kept as objectNon-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndrome
## Age-related macular degeneration
Of the available 1197 eyes with comparable fundus imaging, any AMD was diagnosed in 937 ($78.3\%$) eyes and no AMD in 260 ($21.7\%$), respectively (Table 6). In the 10-year follow-up study, there were 383 new any AMD cases. There was no statistically significant difference in the prevalence of AMD between females and males (598 ($63.8\%$) and 339 ($36.2\%$), respectively, $$p \leq 0.18$$). The age-groups we had showed no statistically significant difference in the presence of any AMD nor was any association between PEX and AMD distribution (Table 6).Table 6Age-related macular degeneration distribution in pseudoexfoliation syndrome and pseudoexfoliation syndrome not diagnosed groups in follow-up study*Non-PEXPEXTotal, n (%)p-valueNo AMD, n (%)189 (21.1)71 (23.7)260 (21.7)$p \leq 0.35$AMD, n (%)708 (78.9)229 (76.3)937 (78.3)Total, n (%)897 (100.0)300 (100.0)1.197 (100.0)*Each eye was kept as objectAMD Age-related macular degeneration; Non-PEX pseudoexfoliation syndrome not diagnosed; PEX pseudoexfoliation syndrome
## Central corneal thickness
CCT was measured in 1262 eyes of 631 patients. In the follow-up study, CCT was significantly thinner in the PEX group than in those without (529 ± 34 μm vs 532 ± 33 μm, respectively, $p \leq 0.001$). CCT became significantly thinner with age ($p \leq 0.001$), as it was 537 ± 30 μm in 55–65 years age group, 533 ± 33 μm in those aged 66–75 years, and 525 ± 36 μm in the over 75 s ($p \leq 0.001$). The thinnest central cornea was in the oldest age group both in the PEX and non-PEX groups (521 ± 33 μm; $p \leq 0.001$ vs 527 ± 37 μm; $$p \leq 0.006$$, respectively). Pearson correlation between age and CCT was weak but significant (r = −0,159; $p \leq 0.001$). There was no statistically significant difference between the PEX and non-PEX groups with respect to gender and CCT.
Over the 10 years of follow-up, CCT became statistically significantly thinner compared to baseline (baseline 539 ± 36 μm vs follow-up 531 ± 33 μm, $p \leq 0.001.$ This also proved to be true in the non-PEX group (in baseline 539 ± 35 μm vs in follow-up 530 ± 31 μm, $$p \leq 0.002$$, respectively), but we were not able to confirm statistical significance in the PEX group (in baseline 539 ± 39 μm vs in follow-up 532 ± 36 μm, $$p \leq 0.280$$).
## Corneal curvature
Of the 1182 eyes with corneal curvature (CC) measured, the mean CC was 7.7 ± 0.26 mm (in baseline mean CC 7.6 ± 0.27 mm). CC radius in males was statistically significantly higher than in females (7.8 vs 7.6 mm; $p \leq 0.001$). At follow-up, there was a tendency for CC to decrease with age, but not significantly ($p \leq 0.05$). At the 10-year follow-up, the cornea became statistically significantly flatter compared to baseline (7.6 ± 0.27 vs 7.7 ± 0.26 mm; $p \leq 0.001$), but there was no statistically significant difference between the PEX and non-PEX groups (PEX 7.64 ± 0.24 vs 7.68 ± 0.26, non-PEX 7.66 ± 0.28 vs 7.70 ± 0.27 mm; $$p \leq 0.123$$).
## Corneal astigmatism
Of the 1182 eyes with relevant measurements, 290 cases of corneal astigmatism were found. Females had more corneal astigmatism (62.8 vs $37.2\%$, $$p \leq 0.02$$). In the study population, corneal astigmatism was mostly with-the-rule, and this tendency was evident in both PEX and non-PEX groups other than in the oldest group where against-the-rule corneal astigmatism was statistically significantly more common ($p \leq 0.001$). In the baseline study, this group displayed the same trend, but it was then not statistically significant.
## Discussion
PEX typically affects the anterior segment of the eye and its worldwide prevalence is estimated to be 0.2–$30.0\%$ of people older than 60 years of age[5, 6, 12, 22–26]. Consistent evidence of PEX incidence and prevalence is lacking [3] and previous studies differed based on geographical, ethnic and racial features, as well as in age and gender distributions and diagnostic methods, making comparison difficult.
In Lithuania, PEX prevalence was found to be $47.3\%$ in 76–83-year-old age group, which is slightly higher than a population with similar characteristics in Iceland ($40.6\%$ of those who are 80 years old) [6]. PEX prevalence increased with age in all studies [9, 12, 13, 23, 26–29] and this is in agreement with our finding, as in our study PEX subjects were older than those without it (72.2 ± 8.1 years vs 68.6 ± 8.2 years, respectively). Similarly to our results, in Turkey the highest PEX rate was in the 80 years old patients ($18.4\%$) with an increased odds ratio of 45.78 ($p \leq 0.01$) when compared to the 40–49 age-group [23].
A previous Lithuanian study [17] found that PEX risk increased by $13.5\%$ with each additional year of life ($95\%$ CI 1.1–1.17; $p \leq 0.001$), while a Swedish study established the annual PEX incidence as $1.8\%$ ($95\%$ CI 1.3–2.4) [7]. The Reykjavik Eye Study found that on average there was a $5\%$ increase in the risk of developing definite PEX for every decade of life in people older than 50 years of age (OR = 1.05; $95\%$ CI 1.01–1.09, $$p \leq 0.022$$) [10].
Incidence data have been provided by a few population based studies such as the Reykjavik Eye Study (5- and 12-year follow-up) [9, 30], the study by Aström et al. [ 7] in Skelleftea, Northern Sweden (21-year follow-up), Chennai Eye Disease Incidence Study with 6-year follow-up [31]. In the Thessaloniki study the 12-year incidence of PEX was $19.6\%$ ($95\%$ CI, 17.1-22.2) [32], similar to ours.
Lithuania is situated on the eastern shore of the Baltic Sea in Northern Europe. In our study, PEX prevalence in Lithuania was found to be $34.2\%$, higher than previously noted in the neighboring Nordic countries (Sweden—$23\%$, Finland—$22\%$, Iceland—10,$7\%$) [6, 7, 33]. In the 76–83-year-old age group, Lithuanian subjects had similar prevalence to that of other Scandinavian countries: $47.3\%$ in our study that is only slightly higher than in Iceland ($40.6\%$ of those who are 80 years old) [6].
In other Baltic states, Estonia recorded $35.4\%$ prevalence in 2004 [11] and $25.5\%$ in 2010 [34], while Latvia reported lower prevalence of $21.6\%$ [35]. Our estimates compare well to similar background of the other Baltic states.
The age-related PEX progression from unilateral to bilateral disease is well established [9]. In our study $53.0\%$ of unilateral PEX progressed to bilateral PEX in 10-year period, this is very similar to the progression rate seen both in Iceland and Sweden [7, 9].
In contrast to some other studies we did not find significant gender differences in our cohort, although there are studies that strengthen our observations as well [26, 36, 37]. Clearly, larger and well characterized samples with detailed ophthalmic examination are required to enable better understanding of this issue.
Not many studies have been able to analyze lifestyle and other factors. In a Saudi Arabian study no significant relation was found between education level, occupation and lifestyle of the patients and the prevalence of PEX [37]. While our study showed some trends in lifestyle and demographic characteristics, none were significant, this is in agreement with the findings of the Thessaloniki Eye Study [32]. It might be worthwhile though pursuing these further if a suitably large cohort is established.
Reykjavik Eye Study had additional data analyzed for PEX risk factors older age, female sex, increased iris pigmentation, moderate use of alcohol, and asthma were all associated with higher PEX prevalence, whereas the consumption of vegetables and fruit was associated with lower PEX prevalence [38]. Five years later, analyzing the same risk factors, significant associations were found only with age and the consumption of fruit [38], but by 12 years there were no statistically significant associations [9, 10, 32]. Being married was shown to be a protective factor for definite PEX in a univariate analysis, but after stratification by the effect of age, the association disappeared [9, 30].
Chennai Eye Disease Incidence Study showed a significant relationship between older age, illiteracy, rural residence, pseudophakia and nuclear cataract and the 6-year incidence of PEX [32, 39]. In our study secondary education for men showed a tendency higher risk of having PEX. Controlling for risk factors (former and current smoking, and normal weight) in a multivariate logistic regression showed the tendency to reduce the probability of having the PEX.
Radius of CC was found to be age-independent and significantly steeper in females than in males [40] and our findings agreed with these. In our study the cornea became flatter during the 10-year follow-up (7.6 vs 7.7 mm; $p \leq 0.001$). And Hepsen et al. reported significantly steeper corneal curvature in PEX eyes compared to those without PEX [41] but we were unable to confirm this in our study [42].
In Reykjavik Eye Study CCT was found to be independent of age and gender [40]: While our findings regarding gender were the same, we did notice that CCT became significantly thinner with aging ($p \leq 0.001$). Some studies claimed that in PEX, CCT values were significantly lower than those in non-PEX eyes [43, 44] and our findings are in agreement with these. However, Krysik, for instance, found CCT to be thicker in the PEX group [45]. Many other authors reported that there was no significant difference in CCT between the PEX and non-PEX groups [10, 41, 42, 46–48]. Again, a large enough cohort might provide an answer to these conflicting data.
Lens opacification occurs in a high proportion of PEX eyes, resulting in cataract surgeries [34] most commonly nuclear [49]. Nuclear sclerosis was predominant in PEX eyes, compared to those without PEX (57.6 and $36.9\%$, respectively) [11], confirmed by Blue Mountains Eye Study [50]. In contrast, while we found a large number of nuclear cataract both in the PEX and non-PEX groups, we could not confirm significant difference in our population. In contrast, Gunes found that mixed cataract was the most common cataract type in the PEX patients in Turkey [51]. In a 30-year follow-up study Ekström et al. found that PEX was the second most important predictor for cataract surgery after lens opacities, accounting for a 2.38-fold increased risk with man having a lower risk for cataract formation [52].
In a Greek study the presence of AMD was strongly related to PEX (x2 = 13.675, $$p \leq 0.003$$; Mantel-Haenszel test=13.66, $$p \leq 0.002$$) [53], while in a Turkish population, the prevalence of AMD was found to be significantly higher in the PEX group than in the non-PEX (17.9 vs $9.5\%$, $$p \leq 0.03$$) [51], but in our study we could not confirm these findings.
## Strength and limitations
The main strength of this study is its population-based prospective study design. Detailed ophthalmological examinations were carried out by trained and certified operators using a strict protocol.
The main limitation includes response rate of $66.4\%$, limiting the generalizability of the study. Cataract surgery had already been performed on $8.02\%$ of those returning for 10-year follow-up, unfortunately there was no information on their PEX status and this might have impacted on our incidence rate [54].
## Conclusion
PEX prevalence in this Lithuanian cohort of well characterized patients increased from 9.8 to $34.2\%$ with no significant difference between sexes, but with significant increase with age. Our results showed that detailed characterization of the PEX and non-PEX eyes was beneficial in identifying trends in anterior segment changes. Conversely, there was no significant relationship with any AMD findings for these patients. An appropriate clinical pathway is required to enable timely diagnosis and treatment of these patients in order to keep them active in society.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 475 kb)
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|
---
title: 'Association between antihypertensive medication and the risk of urinary tract
infection (UTI) of outpatients: a retrospective cohort study'
authors:
- Niklas Gremke
- Karel Kostev
- Matthias Kalder
journal: Infection
year: 2022
pmcid: PMC10042971
doi: 10.1007/s15010-022-01895-8
license: CC BY 4.0
---
# Association between antihypertensive medication and the risk of urinary tract infection (UTI) of outpatients: a retrospective cohort study
## Abstract
### Purpose
The aim of this retrospective study was to investigate the impact of a broad spectrum of antihypertensive (AH) medications on urinary tract infections (UTI) of outpatients diagnosed in general practices in Germany.
### Methods
This study included a total of 367,960 patients aged ≥ 18 years newly a diagnosed with UTI in 1274 general practices in Germany between January 2010 and December 2019. The analysis was conducted for five groups representing five AH therapy classes (diuretics (DIU); beta blockers (BB); calcium channel blockers (CCB); ACE inhibitors (ACEi); angiotensin II receptor blockers (ARB)), each containing 73,592 patients. A Cox regression model was used to analyze the association between each antihypertensive drug class and UTI incidence as compared to all other antihypertensive drug classes (as a group).
### Results
The incidence of UTI diagnosis was slightly higher in patients treated with DIU ($8.6\%$), followed by ACEi ($8.1\%$), ARB ($7.9\%$), and CCB ($6.5\%$). Antibiotic therapy for UTI was given in $5.6\%$ of DIU and $4.3\%$ of CCB patients. The incidence of UTI and antibiotic therapy was much higher in women than in men across all therapy classes. No significant increase or decrease in UTI incidence or antibiotic therapy was observed in any of the AH therapy classes investigated.
### Conclusion
The present study did not identify a significant increase or decrease of UTI incidence or antibiotic therapy in patients treated with ACEi, ACB, CCB, beta blockers or diuretics. Across all AH classes studied, the incidence of UTI and antibiotic therapy was higher in women than in men, although not significantly.
## Introduction
Arterial hypertension (AH) is the most common preventable risk factor for cardiovascular disease (CVD) and chronic kidney disease (CKD) and is considered as the biggest single contributor to the global burden of disease and to global mortality with a total of 9.4 million deaths per year [1, 2]. The etiology of AH includes genetic predisposition, as well as a complex interplay of environmental and pathophysiological factors affecting multiple blood pressure (BP) regulation systems [3, 4]. One of the major BP regulation systems is the renin angiotensin aldosterone system (RAAS) which has a wide-ranging effect on BP control by regulating vascular tonicity as well as blood volume and, therefore, plays a pivotal role in AH pathogenesis [5, 6]. Based on this pathophysiological knowledge, a diverse subset of antihypertensive pharmacotherapeutics affecting the RAAS-System [e.g., ACE Inhibitors (ACEi) and Angiotensin II receptor blockers (ARB)] have been approved and have entered clinical practice in the past [7, 8]. However, clinicians are faced with many more classes of antihypertensive drugs, such as β-adrenoreceptor blockers (BB), calcium channel blockers (CCB), and different types of diuretics (DIU) [9, 10]. The World Health Organization (WHO) recently published a new guideline for the pharmacological treatment of AH in adults based on the data from 32 systematic reviews and pinpointed thiazide diuretics, ACEi/ARB’s, and long-acting dihydropyridine calcium channel blockers as first-line therapeutic agents. Especially in the treatment of patients with multimorbidity and frailty, these guidelines recommend clinical judgment because of potential risks arising from treatment side effects such as acute kidney injury, hyperkalemia, hypotension, and syncope which can result in hospital admission and reduced adherence of patients to antihypertensive medications [11, 12].
In view of these known side effects, the emergence of urinary tract infections (UTI) has become a subject of intense discussion in literature and has also repeatedly been identified as a possible side effect of antihypertensive therapy [13–15]. *In* general, UTIs are one of the most common diseases seen in general practitioner practices in Germany and are classified as either lower (confined to the bladder) or upper (pyelonephritis) and either uncomplicated (no relevant functional or anatomical anomalies in the urinary tract) or complicated (when those anomalies are present) [16, 17]. However, the evidence in the literature to date regarding the possible association between antihypertensive drugs and UTI is unsatisfying and contradictory. In this context, a population-based prescription sequence symmetry analysis conducted by Pouwels et al. showed a statistically significant increased risk of developing UTI upon ACEi initiation, whereas no association was found between beta blockers and UTI treatment.
Therefore, the aim of the present study is to analyze the incidence of UTI as a function of antihypertensive therapy and to reveal any potential association between a broad spectrum of antihypertensive medication and UTI incidence in outpatients with AH treated in general practices in Germany.
## Database
This study was based on data from the Disease Analyzer database (IQVIA), which contains drug prescriptions, diagnoses, and basic medical and demographic data obtained directly and in anonymous format from computer systems used in the practices of general practitioners and specialists [18]. The database covers approximately $3\%$ of all outpatient practices in Germany. Diagnoses (according to the International Classification of Diseases, 10th revision [ICD-10]), prescriptions (according to the Anatomical Therapeutic Chemical [ATC] classification system), and the quality of reported data are monitored by IQVIA. In Germany, the sampling methods used to select physicians’ practices are appropriate for obtaining a representative database of general and specialized practices. It has previously been shown that the panel of practices included in the Disease Analyzer database is representative of general and specialized practices in Germany [18]. Finally, this database has already been used in previous studies focusing on antihypertensive therapy [19, 20] as well as urinary tract infections [21, 22].
## Study population
This retrospective cohort study included adult patients (≥ 18 years) with an initial prescription of a single antihypertensive therapy (diuretics, ATC: C03A; beta blockers, ATC: C07A; calcium channel blockers, ATC: C08A; ACE inhibitors, ATC: C09A; angiotensin II receptor blockers, ATC: C09A) in 1,274 general practices in Germany between January 2010 and December 2019 (index date; Fig. 1). Patients diagnosed with urinary tract infections, site not specified (ICD-10: N39.0) or acute cystitis (ICD-10: N30.0) within 12 months prior to or on the index date were excluded. Fig. 1Selection of study patients Five groups of patients treated with different antihypertensive drug classes were matched 1:1:1:1:1 to controls by propensity scores based on sex, age, and diagnoses documented within 12 months prior to or on the index date including diabetes (ICD-10: E10–E11), hypertension (ICD-10: I10), renal failure (ICD-10: N18, N19), and cancer (ICD-10: C00–C97), as well as therapy duration in months.
## Study outcomes and covariates
The main outcome of the study was the incidence of UTI within 12 months after the index date as a function of antihypertensive therapy. Each patient was followed for up to 12 months from the index date until the first UTI diagnosis was documented or antihypertensive therapy ended (either because of a switch to a different antihypertensive therapy or the addition of another drug class to the initial single therapy).
## Statistical analyses
Differences in the sample characteristics between five antihypertensive drug class groups were tested using chi-squared tests for categorical variables and Kruskal–Wallis tests for age. Conditional Cox regression models were applied to study the association between each antihypertensive drug class and UTI incidence as compared to all other antihypertensive drug classes (as a group). These models were applied separately for four age groups and also for women and men. As a sensitivity analysis, the outcome was additionally defined as the diagnosis of UTI plus a prescription of an antibiotic drug (ATC: J01) within 7 days following UTI diagnosis. Both regression models were adjusted for the physicians’ practices to reflect the diagnosis behavior of treating physicians. To counteract the problem of multiple comparisons and also due to the large patient samples, p values < 0.01 were considered statistically significant. Analyses were carried out using SAS version 9.4 (SAS institute, Cary, USA).
## Basic characteristics of the study sample
The present study included five therapy class groups, each comprising 73,592 patients (367,960 patients in total). The basic characteristics of the study patients are displayed in Table 1. Due to the matched pair design of this study, all five cohorts had the same age, sex, and comorbidity structure. The mean age [SD] was 66.5 [SD: 13.7] years; $55.2\%$ of patients were women, prevalence of diabetes was $17.0\%$, hypertension $55.1\%$, renal failure $3.5\%$, cancer $5.9\%$. On average, patients were treated for approximately 250 days until therapy switch or break. Table 1Basic characteristics of the study sample after propensity score matchingVariableProportion affected among patients treated with ACE inhibitors (%)Proportion affected among patients treated with beta blockers (%)Proportion affected among patients treated with diuretics (%)Proportion affected among patients treated with CCB (%)Proportion affected among patients treated with ARB (%)P valueN73,59273,59273,59273,59273,592Age (mean, SD)66.5 (13.7)66.5 (13.7)66.5 (13.7)66.5 (13.7)66.5 (13.7)1.000Age ≤ 6032.532.532.532.532.51.000Age 61–7024.024.024.024.024.0Age 71–8028.128.128.128.128.1Age > 8015.515.515.515.515.5Female55.255.255.255.255.21.000Male43.843.843.843.843.8Diabetes17.017.017.017.017.01.000Hypertension55.155.155.155.155.11.000Renal failure3.53.53.53.53.51.000Cancer5.95.95.95.95.91.000Therapy duration in days (mean, SD)246 [153]248 [151]247 [150]247 [152]252 [147]0.689Proportions of patients given in % unless otherwise indicated SD standard deviation
## Cumulative incidence of UTI diagnosis
Figure 2 shows the incidence (cases per 1000 patient years) of UTI diagnosis. This incidence was slightly higher in patients treated with diuretics ($8.6\%$), followed by ACEi ($8.1\%$), and ARB ($7.9\%$). The lowest incidence was in patients treated with CCB ($6.5\%$). Antibiotic therapy for UTI was given in 5.6 DIU and $4.3\%$ CCB patients. The incidence of UTI and antibiotic therapy was much higher in women than in men across all therapy classes. Fig. 2Proportion of individuals with a diagnosis of UTI and antibiotic therapy within 12 months after initiation of AH therapy in patients treated with different antihypertensive drugs
## Association between antihypertensive therapy and incidence of UTI
Table 2 shows the results of the conditional regression analyses. No significant increase or decrease in UTI incidence or antibiotic therapy was observed in any of the therapy classes investigated. ARB was associated with an increased incidence of UTI (HR: 1.20, $95\%$ CI 1.03–1.39) among women only with a p value of 0.020, but this was not considered statistically significant in this study. Table 2Association between antihypertensive therapy and the UTI diagnosis in patients followed in general practices in Germany (Cox regression models)CohortBB vs. restCCB vs. restARB vs. restDIU vs. restACEI vs. rest Total Diagnosis0.94 (0.81–1.10)0.92 (0.79–1.06)1.13 (0.99–1.30)1.04 (0.90–1.19)0.97 (0.84–1.13) Diagnosis + antibiotic prescription1.00 (0.83–1.21)0.99 (0.83–1.18)0.97 (0.81–1.16)1.06 (0.89–1.26)0.98 (0.81–1.18)Women Diagnosis0.89 (0.75–1.07)0.95 (0.81–1.11)1.20 (1.03–1.39)1.04 (0.89–1.21)0.93 (0.79–1.09) Diagnosis + antibiotic prescription0.96 (0.80–1.18)0.99 (0.81–1.21)1.08 (0.88–1.31)1.07 (0.88–1.29)0.90 (0.73–1.12)Men Diagnosis1.18 (0.84–1.68)0.82 (0.57–1.17)0.94 (0.66–1.35)1.06 (0.76–1.48)1.05 (0.72–1.51) Diagnosis + antibiotic prescription1.18 (0.77–1.80)0.98 (0.65–1.48)0.64 (0.39–1.05)1.01 (0.67–1.54)1.27 (0.83–1.95)
## Discussion
The present retrospective cohort study included 73,592 patients for each class of antihypertensive drugs (367,960 patients in total) and found no significant increase or decrease in UTI incidence or antibiotic therapy in any of the therapy classes investigated. However, our results showed that the incidence of UTI and antibiotic therapy was much higher in women than in men across all antihypertensive therapy classes. These results are in line with the current literature, where UTI is considered the most common outpatient infection worldwide, having a prevalence of $23.3\%$ for women and $6.8\%$ for men, respectively [23]. A recently published study by Laupland et al. conducted population-based laboratory monitoring for all community-onset urinary tract infections among all residents of the Calgary Health region (~ 1.2 million residents) in Canada. During the 2 years of the study (2004–2005), there was a significant relationship between both age and sex and the incidence for outpatient onset UTI. The overall incidence in females was much higher than that in males (30.0 vs 5.0 per 1000, RR 5.98; $95\%$ CI 5.81–6.15; $p \leq 0.0001$) and a substantial increase was observed in association with advancing age, reaching up to 637.8 (m) and 925.7 (f) per 1000/year in the very old (90 years of age) [24]. In most cases, UTI is treated with antibiotics, although focusing on more differentiated antibiotic drug prescription for UTI is an important issue with respect to reducing antimicrobial resistance. In this context a population-based cohort study recently showed that women suffering from UTI received significantly more antimicrobial drug prescriptions than men ($45.2\%$ vs. $12.6\%$) and the proportion of prescriptions for UTIs among all prescriptions with an indication code increased from $5.2\%$ in 1996 to $14\%$ in 2014 in men and from $28\%$ in 1996 to $50\%$ in 2014 in women [25]. Bearing in mind the issue of demographic change, the ageing of the population may partly explain the phenomenon of increased antibiotic drug prescriptions for UTI because antibiotics were most commonly prescribed to patients in older age categories [25, 26]. The difference observed between men and women is certainly also due to the anatomically determined length of the urethra, which means that UTIs occur less frequently in men.
However, UTIs are not only considered the most common of all bacterial infections but also cause enormous societal costs (cost of general and specialized medical visits, prescription drugs, diagnostic tests, hospitalizations etc.) [ 27–29]. In view of this, there is an urgent need to clarify the development of UTIs as possible side effects of different pharmacotherapeutics. Notably, the results of previous pharmacoepidemiologic studies on this topic are contradictory and insufficient. A prescription sequence symmetry analysis performed using the Dutch InterAction pharmacy prescription database, revealed a statistically significant increased risk of starting UTI antibiotic therapy after starting ACEi treatment, whereas no association was found between beta blockers and UTI treatment. Notably, the excess of patients who received an antibiotic prescription was only significant for the first month after ACEi initiation [13, 14]. Based on this finding, the authors introduced the hypothesis that ACEi may occasionally lead to a decreased glomerular filtration rate (GFR) and the risk of developing UTI may increase as a consequence of lower urine output. They also found that the association between ACEi therapy and UTI antibiotic prescription was stronger among patients with diabetes then those without diabetes, possibly due to diabetes-related renal impairment [13, 14, 30–33]. Nevertheless, this study is also subject to a number of methodological limitations (e.g., using nitrofurantoin prescriptions as a proxy with insufficient sensitivity for UTI) [14, 34].
Another study was conducted as a post hoc analysis using data from the REnal and Vascular ENdstage Disease Intervention Trial (PREVEND IT), a randomized, double-blind, placebo-controlled trial in which participants received pravastatin, fosinopril or placebo on a randomized basis in a 2 × 2 factorial design over 4 years [15, 35, 36]. In particular, the ACEi fosinopril was associated with an increased occurrence of initial UTI antibiotic prescriptions (HR, 1.82; $95\%$ CI 1.16–2.88) [15]. However, this study was not designed and powered for a post hoc analysis and again used a proxy (antibiotic prescriptions) with limited sensitivity for the detection of UTI. In line with our observations, no studies are available that indicate that ACEi also increases the occurrence of UTI. Based on a reduced urine output as a result of ACEi treatment it could almost be assumed, that ACEi might increase the frequency of UTI [31, 32]. This hypothesis must be questioned, however, since a study has recently been published indicating that loop diuretics can deplete the renal cortico-medullary salt gradient as a major modulator of immune responses. As a result, renal transplant recipients suffer from a markedly increased rate of urinary tract infections (UTIs) upon treatment with diuresis-increasing loop diuretics [37].
Mansfield et al. analyzed the role of ACEi/ARBs or alternative AH therapeutics (β-blockers, calcium channel blockers, or thiazide diuretics) between April 1997 and March 2014 in a self-controlled case series (SCCS) in patients with acute kidney injury (AKI) following various infections (urinary tract infection, lower respiratory tract infection, and gastroenteritis). In this setting, acute infections (incl. UTI) are associated with a substantially increased risk of transient AKI among patients treated with AH drugs. However, the increase in relative risk was not higher among patients receiving ACEis/ARBs than in those treated with other AH drugs [38].
Unfortunately, there is still a lack of evidence in the literature for the incidence of UTI as a function of other AH therapeutics, e.g., CCB, ARBs, and diuretics. Particularly in view of this limited evidence, further research is needed concerning the role of UTI in patients receiving AH medications.
## Strengths and limitations
Our retrospective cohort study has several strengths: The German Disease Analyzer (DA) is a large European outpatient database containing data from 2898 practices with about 7.8 million patients in Germany and the representativeness of the diagnoses it contains has already been validated [18, 39]. Furthermore, DA provides continuously updated data generated directly from practice computers based on patient data (diagnoses, demographic data, prescriptions, several measurements, etc.) and has been successfully used for several pharmacoepidemiology studies in various disciplines [18, 40–42]. However, the DA does not contain information on external confounding factors (alcohol and tobacco consumption, socioeconomic status, etc.) and there is also a lack of hospital data and information on mortality that should be considered. Furthermore, diagnoses are based solely on ICD-10 codes documented by general practitioners and do not include diagnoses by gynecologists or urologists, which could influence the quality of the documented diagnoses. Moreover, using ICD-10 Code N39.0 (urinary tract infections, site not specified) is not sufficient to distinguish between upper or lower UTI. In addition, there is a lack of information regarding the specific UTI bacterial spectrum or the exact classification (complicated vs. uncomplicated).
Finally, it should be mentioned that the retrospective cohort design does not allow conclusions to be drawn concerning the causality of the present findings.
## Conclusions
The present study did not identify any significant increase or decrease in UTI incidence or antibiotic therapy in outpatients treated with ACEi, ACB, CCB, beta blockers or diuretics. The incidence of UTI and antibiotic therapy was higher in women than in men across all AH classes, although not significantly.
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|
---
title: Predicting and understanding human action decisions during skillful joint-action
using supervised machine learning and explainable-AI
authors:
- Fabrizia Auletta
- Rachel W. Kallen
- Mario di Bernardo
- Michael J. Richardson
journal: Scientific Reports
year: 2023
pmcid: PMC10042997
doi: 10.1038/s41598-023-31807-1
license: CC BY 4.0
---
# Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI
## Abstract
This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) networks were trained to predict the target selection decisions of expert and novice players completing a multiagent herding task. The results revealed that the trained LSTM models could not only accurately predict the target selection decisions of expert and novice players but that these predictions could be made at timescales that preceded a player’s conscious intent. Importantly, the models were also expertise specific, in that models trained to predict the target selection decisions of experts could not accurately predict the target selection decisions of novices (and vice versa). To understand what differentiated expert and novice target selection decisions, we employed the explainable-AI technique, SHapley Additive explanation (SHAP), to identify what informational features (variables) most influenced modelpredictions. The SHAP analysis revealed that experts were more reliant on information about target direction of heading and the location of coherders (i.e., other players) compared to novices. The implications and assumptions underlying the use of SML and explainable-AI techniques for investigating and understanding human decision-making are discussed.
## Introduction
Performing tasks with other individuals is an essential part of everyday human life. Such behavior requires that co-actors reciprocally coordinate their actions with respect to each other and changing task demands1–4. Pivotal to the structural organization of such action is the ability of co-actors to effectively decide how and when to act5, with robust decision-making often differentiating expert from non-expert performance6. This is true whether one considers the simple activity of family members loading a dishwasher together7–9, or the more complex activities performed by elite athletes during team sports10,11 or soldiers during high-stakes military operations12.
In contrast to practical reasoning or deliberative decision-making, where an actor extensively evaluates all possibilities to determine the optimal action, the decision-making that occurs during skillful action is typically fast-paced and highly context dependent13–16, with actors spontaneously adapting their actions to achieve task goals as “best as possible”17. Indeed, the effectiveness of action decisions during skillful behavior is a function of an actor’s level of situational awareness18,19, with task expertise reflecting the attunement of an actor to the information that specifies what action possibilities (optimal or sub-optimal) ensure task completion20–22. Developing a comprehensive understanding of decision-making during skillful behavior therefore rests on the ability of researchers and practitioners to identify what task information underlies effective decision-making performance. Key to achieving this, is developing decision-making models that can not only predict the action decisions of human actors during skillful action, but also help to identify what differentiates expert from non-expert performance. Motivated by these challenges, the current study proposes the use of state-of-the art Supervised Machine Learning (SML) and explainable AI (Artificial Intelligence) techniques to model, predict and explicate the action decisions of expert and novice pairs performing a fast-paced joint-action task. Specifically, for the first time in the literature, we show how using these computational techniques renders it possible to uncover the crucial information that co-actors exploit when making action decisions in joint-action (and individual) task contexts.
## Supervised machine learning
The application of Machine Learning (ML) techniques has rapidly increased over the last decade. For example, ML is now integral to modern image and speech recognition23–26, scientific analysis27,28, digital manufacturing and farms29,30, financial modeling31, and online product, movie and social interest recommendations32–34. In many contexts, ML models are trained via SML, whereby computational models learn to correctly classify input data, or predict future outcomes states from input data, by leveraging coded, realword training samples35,36. Training samples include representative task data (e.g. images, sounds, motion data) that have been labeled with the correct data class or outcome state. These training samples are then used to build an approximate model of how the input data (e.g., pixels from an image) map to the correct output label (e.g., cat or dog)37,38. Following training, the efficacy of the model is then tested against data not supplied during training, with effective models able to generalize the learned input–output associations to unseen data.
## Artificial neural and long short-term memory networks
SML models can be realized in numerous ways, for instance, using decision trees39,40, support vector machines41,42 or, of particular importance here, Artificial Neural Networks (ANNs). *In* general, ANNs are a composition of elementary units, nodes, that are grouped in interconnected layers, where nodes have different activation functions and the connections between nodes can have different weights. A typical ANN includes an input and an output layer, with 1 or more “hidden layers” in between (with deeper ANNs having more hidden layers). Training an ANN to model input–output associations via SML requires finding the combination of network parameters (weights and biases) that map input data to the correct output class or state prediction. This is achieved by iteratively evaluating the error between the correct and predicted output of the ANN (via a loss function) and adjusting the network parameters to minimize this error using a process called back-propagation (see e.g.43 for more details).
There are various types of ANNs. Of relevance here, is an ANN known as a Long Short-Term Memory (LSTM) network, which is a form of recurrent neural network that in addition to feed-forward connections among node layers also includes feedback connections. These feedback connections enable the ANN to process and retain information about sequences of consecutive inputs44,45. Accordingly, LSTMs are commonly used in time-series prediction tasks46–51, where the processing of both past and present input states is required to correctly predict future states. LSTMs are applicable to predicting human behavior52–54, as human action decisions are based on the assessment of dynamical (time varying) task information10,22 and, thus, the prediction of future state behavior or action decisions requires processing sequences of task relevant state input data.
## Explainable AI
Despite the increasing utility and effectiveness of ANNs in recent years24,25,33,55, the large number of connection weights and the non-linearity of the activation functions within ANNs, their generalization limits and complex decision boundaries, particularly in Deep-ANNs, makes it difficult to directly access how input features relate to output predictions. For this reason, ANNs are often referred to as “black-box” models. However, a desire to better understand and interpret the validity of ANNs and other black-box models, as well as the growing demand for more ethical and transparent AI systems56, has resulted in a renewed interest in the application and development of explainable-AI techniques50,57–60 such as LIME61, DeepLIFT62, LRP63,64 and, more recently, SHapley Additive exPlanation (SHAP)65,66, which we employ here.
These techniques make the internal processes of a black-box model understandable by deriving linear explanation functions of the effects that input features have on output states. For example, the SHAP algorithm pairs each input feature with a SHAP value. The higher the SHAP value, the greater the influence that feature has on an output state. Given that SHAP values are locally accurate, one can derive a measure of global feature importance by calculating the average importance of a feature over the test set used to assess model accuracy. The result is an average SHAP value for a given input to output association that captures the overall significance of a given input feature for a given output prediction.
## Current study
The current study had three primary aims: [1] investigate the use of SML trained LSTM-layered ANN models (hereafter referred to LSTMNN models) to predict human decision-making during skillful joint-action; [2] demonstrate how SHAP can be employed to uncover the task information that supports human decision-making during skillful joint-action by determining what input information (features) most influenced the output predictions of a trained LSTMNN model; and [3] apply these techniques to explicate differences between the decision-making process of novice and expert players while playing a simulated, fast paced herding game67–69.
Herding tasks involve the interaction of two sets of autonomous agents—one or more herder agents are required to corral and contain a set of heterogeneous target agents. Such activities are ubiquitous in daily life and provide a prototypical example of everyday skillful joint- or multiagent behavior. Indeed, while the most obvious examples involve farmers herding sheep or cattle, similar task dynamics define teachers corralling a group of young children through a museum or firefighters evacuating a crowd of people from a building70.
For the current study, we modeled data from71, in which pairs of players controlled virtual herder agents to corral a herd of four virtual cows (hereafter refereed to as targets), dispersed around a game field, into a red containment area positioned at the center of the game field. The task was presented on a large touch screen, with players using a touch-pen stylus to control their virtual herders (Fig. 1a). Targets were repelled away from the human-controlled herders when the herder came within a certain distance of the target. When not influenced by a herder, the movement of targets was governed by Brownian motion, with targets randomly (diffusely) wandering around the game field. Figure 1(a) The herding task and experimental setup. ( b) A screenshot of the data playback and coding application used to identify which target a herder was corralling at each time step. The blue and orange dots are the herders and the white dots are the targets. The text label above each target specifies the target number (i.e., 1 to 4). The transparent red area is the containment region, which for the data employed here was always in the center of the game field. The insert panel on the bottom right of (b) is the target coding panel, where UD = no target. See main text for more details.
To successfully corral the targets into the containment area, previous research has demonstrated that effective task performance requires that players coordinate their target selection decisions by dynamically dividing the task space into two regions of responsibility, with each player then corralling one target at a time in their corresponding regions of responsibility until all of the targets are corralled within the containment area68,71. To date, however, no previous research has explicitly explored or modeled the process by which players make their target selection decisions, nor what state information regulates this decision-making behavior. Assuming that a player’s (a) target selection decisions are specified in the movements72–74 of their herder and (b) that players used kinematic information of target and herder states (e.g., relative positions, velocities) to make target selection decisions20–22,68, we expected that an LSTMNN could be trained via SML to predict these target selection decisions using short sequences of herder and target state input features that preceded target selection. We expected that target selection decisions could be predicted prior to a player enacting a target selection decision and, given that experts perform significantly better than novices (71 and Supplementary Information, Sec. 1), we also expected that the trained LSTMNN,expert and LSTMNN,novice models would be expertise specific. Finally, we expected that SHAP could be employed to identify differences in the decision-making processes of the expert and novice herders, or more specifically, how accurate LSTMNN models of expert or novice target selection behavior deferentially weighted task information (feature inputs) when making predictions.
## Results
Given players could choose to corral no target, predicting the target selection decisions of human herders corresponded to a 5-label prediction problem, with ID = 1, 2, 3, 4 corresponding to the actual targets and ID = 0 corresponding to no target.
To train an LSTMNN using SML, we extracted time-series data from the data recordings of the expert-expert pairs and the successful novice-novice pairs that completed the dyadic herding task detailed in71. Only data from successful trials was employed. For each trial, state data was extracted from the time of task onset to when the players had corralled all four targets inside the containment area. At each time step, the target a herder was corralling was labeled manually by a research assistant, blind to the studies true purpose, using a custom coding software package (see Fig. 1b and “Methods” for more details). From the resulting labeled time-series data, training samples that included 48 input features were constructed of length ti to tf, where tf − ti = Tseq and the length of Tseq corresponded to 25 time steps (i.e., ti = tf − 25 time steps) or 1 s of system state evolution. The 48 state input features were the relative radial and angular distance between herders and between the herders and each of the 4 targets, each herder’s and target’s radial and angular distance from the containment area, and the radial velocity, radial acceleration and direction of motion of each herder and target.
## Predicting future target selection decisions
LSTMNN models were trained to predict the next target ID a herder would corral at tf+Thor given a feature input sequence of length Tseq, with Thor > 0 time steps. Note that Thor = 1 corresponded to predicting the target the herder would corral at the next time-step (equivalent to 40 ms in the future) and, thus, simply entailed predicting target selection decisions already made and/or currently being enacted by a herder. Here we present the results for models trained to predict target selection decisions at two longer prediction horizons, namely, Thor = 16 and 32, which corresponded to predicting the target a herder would corral 640 ms and 1280 ms in the future, respectively (for comparative purposes, we also trained models for Thor = 1 and 8, see Supplementary Information, Sec. 3).
Importantly, Thor = 16 and 32 involved predicting the target selection decisions before a player’s decision or behavioral intention was enacted or typically observable in the input data sequence Tseq. This was validated by calculating the average time it took players to move between targets when switching targets, with an average inter-target movement time of 556 ms for novice herders and 470 ms for expert herders (see Supplementary Information, Sec. 2).
Separate LSTMNN were trained to predict the target selection decisions of novice and expert herders for each prediction horizon. The confusion matrices of each LSTMNN evaluated on ten sets of test data samples (i.e., samples not employed during training) are reported in Fig. 2. Importantly, the values on the diagonal indicate that each target ID could be correctly predicted between 90 and $97\%$ of the time. Indeed, independent from prediction horizon and player expertise, each LSTMNN predicted which target a herder would corral with an average accuracy exceeding $94\%$ (see Table 1 for more prediction metrics, defined in “Methods”).Figure 2Confusion matrices for the trained prediction models tested on 10 sets of Ntest = 2000 samples for each expertise level and prediction horizon. Values on the diagonal indicate the portion of test samples correctly predicted. Table 1Average performance [%] of the expert and novel models as a function of prediction horizon, tested on 10 sets of Ntest = 2000 samples. AccuracyPrecisionRecallF1 scoreMIτhor = 16 (640 ms) prediction horizon Novice95.33 ± 0.295.18 ± 0.295.25 ± 0.295.2 ± 0.284.16 ± 0.5 Expert95.2 ± 0.494.17 ± 0.694.5 ± 0.594.3 ± 0.584.9 ± 0.5τhor = 32 (1280 ms) prediction horizon Novice95.75 ± 0.595.42 ± 0.595.5 ± 0.595.45 ± 0.583.24 ± 1.04 Expert94.66 ± 0.593.2 ± 0.793.34 ± 0.893.25 ± 0.781.32 ± 1.5MI Mutual Information.
## Predicting differing target selection behaviors
Recall that the data samples used to make a target selection prediction are vector time-series of the herding system’s state evolution for t ∈ [ti,tf], where tf − ti = Tseq, and the prediction outputs are chosen as the ID of the target that will be corralled at tf+Thor with Thor 6 = 0. It is important to appreciate that during the time interval Tseq, a human herder could either continuously corral the same target agent or transition between different targets. Here we classified these as non-transitioning and transitioning behavioral sequences, respectively. Furthermore, at Thor, a herder could be corralling the same target that was being corralled at the end of Tseq or could switch to a different target. Here we classified these two possibilities as non-switching and switching behaviors, respectively. Taken together, this defines four different sub-categories (subclasses) of target selection behavior (or data sample type), which are illustrated in Fig. 3.Figure 3Illustration of the different sub-categories of target selection behavior, which also reflects the four different types of data samples used for model training and testing. Non-transitioning (a, c) and transitioning (b, d) corresponded to whether a harder corralled the same target or different targets during the input sequenced Tseq = t ∈ [ti,tf]. Non-switching (a, b) and switching (c, d) corresponded to whether a herder was corralling the same or a different target, respectively, at Thor and tf.
The sample distribution of the different target selection behaviors observed within the expert and novice data set as a function of prediction horizon, Thor = 16 and 32, are displayed in Fig. 4a. Interestingly, the expert data contained a high proportion of non-transitioning-non-switching behavior ($69\%$), whereas the novice dataset contained a more even distribution of sample type compared to experts, particular for Thor = 32. This indicated that experts both transitioned and switched between different targets less often than novices and were more persistent in corralling a given target compared to novices. Of more significance with regard to target selection predictions was that differences in sample type distribution could skew model accuracy due to an uneven representation of each sample type during training. That is, models trained using randomly selected training sets would exhibit lower accuracy for the underrepresented behavior types; e.g., model accuracy would be lower for transitioning-switching behaviors compared to non-transitioning-non-switching behaviors for example. This is illustrated in Supplementary Information, Sec. 4, where we show how model accuracy is sub-class dependent when models are trained using representative distributions of sample type. Figure 4(a) Distribution of the different sub-categories of target selection (sample type) as a function of expertise level and prediction horizon. ( b) *The* general (mixed) and sample specific accuracy of models trained using a uniform distribution of training samples (i.e., training set contained $25\%$ of each sample type) as a function of expertise level and prediction horizon. Accuracy values ranged from 92.3 to $98.2\%$, with an overall mean accuracy of $95.23\%$ while Mutual Information ranged from 66.6 to $93.5\%$, with an overall MI score of $81.5\%$.
Accordingly, the LSTMNN models reported here were trained using training sets that contained a randomly selected, but uniform (balanced) distribution of sample type, such that each sub-class of target selection behavior was equally represented during training. Furthermore, in addition to examining overall model accuracy as a function of target ID (see Fig. 2 and Table 1), the LSTMNN models predicting expert and novice target selection decisions at Thor = 16 and 32 were also tested against N ≥ 1 novel test sets composed of either (i) non-transitioning-non-switching, (ii) non-transitioning-switching, (iii) transitioning-non-switching, (iv) transitioning-switching samples. Note that each test set contained 2000 test samples and thus the number of test sets N employed for model testing varied from 1 to 10 and was a function of how many samples were available post training (i.e., from the set of samples not employed during model training); see “Methods” for more details.
Average model accuracy for each sample type as a function of expertise level and prediction horizon is illustrated in Fig. 4; for a detailed list of model accuracy and mutual information values as a function of sample type see Supplementary Information, Sec. 4. The results revealed that the accuracy of the resultant models were essentially sample type independent. More specifically, for Thor = 16 the accuracy for each sample type ranged between $93.3\%$ and $98.2\%$ for LSTMnovice models and $93.6\%$ and $97.11\%$ for LSTMexpert models, with an average mixed sample type accuracy of $95.33\%$ (± 0.2) and $95.2\%$ (± 0.4) for LSTMnovice and LSTMexpert models, respectively. Similarly, for Thor = 32 the accuracy for each sample type ranged between $94.56\%$ and $96.51\%$ for LSTMnovice models and $92.32\%$ and $96.5\%$ for LSTMexpert models, with an average mixed sample type accuracy of $95.75\%$ (± 0.5) and $94.66\%$ (± 0.5) for LSTMnovice and LSTMexpert models, respectively.
## Specificity of expert and novice predictions
The latter results provided clear evidence that the LSTMNN models could accurately predict the future target selection decisions of expert and novice herders, independent of whether the future target to be corralled was the same or different from that being corralled at Thor ≤ 0. With regard to differentiating expert and novice performance, of equal importance was determining whether the corresponding LSTMNN models were expertise specific. This was tested by comparing the performance of the expert trained LSTMNN models attempting to predict novice target selection decisions and vice versa. As expected, when an LSTMNN trained on one expertise was used to predict test samples extracted from the opposite expertise model performance decreased to near or below chance levels (see Fig. 5), confirming that the models were indeed expertise specific. More specifically, for Thor = 16 the LSTMexpert models predicted novice samples with an average accuracy of only $40.68\%$. Similarly, the LSTMnovice models only predicted expert samples with a $57.7\%$ average accuracy. For Thor = 32, average accuracy dropped to $37.66\%$ for expert-to-novice and to $58.1\%$ for novice-to-expert predictions. Figure 5Average accuracy (%) of expert and novel trained models for 12 different tests sets of congruent (novice-novice, expert-expert) or in-congruent (novice-expert, expert-novice) Ntest = 2000 samples. ** *Indicates a* significant paired samples t-test difference of $p \leq 0.01.$ Note there were no significant differences between the accuracy of LSTMnovice and LSTMexpert models for both prediction horizon’s when tested on the same expertise level (i.e., novice-novice and expert-expert (all $p \leq 0.1$).
## Identifying differences in expert and novice target selection decisions
The significant difference in the performance of LSTMNN models trained and tested on the same level of expertise (see Table 1) compared to different levels of expertise (see Fig. 5) implied that the novice and expert LSTMNN models weighted input state variables differently. Recall that, of particular interest here was whether these differences could be uncovered using the explainableAI technique SHAP. Accordingly, for each model we computed SHAP values for each sample in a in a subset of the training set and then, using the average SHAP value, rank-ordered each input feature in terms of its predictive importance.
Before assessing what specific input features were weighted differently, we first computed the ordinal association of SHAP value rankings between the different LSTMNN models using the Kendall rank correlation coefficient (Kendall’s τ75) where τ = 0 corresponds to the absence of an association.
Note that although τ = 0 is the null-hypothesis and one cannot draw conclusions from non-significant results, it does provide a robust and intuitive assessment of rank order independence, τ = 1 corresponds to perfect association (matched rankings), and τ = − 1 corresponds to opposite ranking orders (negative association). More specifically, we computed Kendall’s τ on the SHAP rankings of the full input feature set and on the top 10 features ranked by SHAP, between the novice and expert LSTMNN models for each prediction horizon. Consistent with the expectation that novices and experts employed different state information when making target selection decisions, this analysis revealed little association between the novice and expert SHAP rankings for both Thor = 16 and 32, with an average τ = 0.08 ($p \leq 0.45$) for Thor = 16 and τ = − 0.09, ($p \leq 0.4$) for Thor = 32 (see Supplementary Information, Sec. 5 for a detailed summary of Kendall’s τ values).
To highlight what input features most influenced target selection predictions, Fig. 6 illustrates the average ranking of the different input features for both non-zero prediction outputs (i.e., ID = 1 to 4) and for ID = 0 prediction outputs, with the different input features defined as a function of input feature class (e.g., distance from herder, distance from co-herder, distance from containment area, velocity, etc.) and agent type. Note that the player that the target prediction corresponds to is referred to as the herder, a player’s partner is labeled as co-herder, the predicted target is the target that was predicted to be corralled by the herder at Thor and other-targets corresponds to the targets that were not predicted to be corralled at Thor (see Supplementary Information, Sec. 6 for a detailed summary of SHAP feature values).Figure 6SHAP results for target prediction (top row) ID 6 = 0 (i.e., ID = 1 to 4) and (bottom row) ID = 0, as a function of prediction horizon and expertise (novice = empty shapes, expert = filled shapes). Feature type is listed on the vertices. The radial axis represents the average rank position, such that “1st to 5th” represents a feature that was always or nearly always ranked as a top-five feature.
Note that we split the illustration and analysis between ID 6 = 0 and ID = 0, as there is a qualitative difference between deciding to pick a specific target to corral and choosing not to corral a target, with the latter corresponding to either a period of indecision, a period of inter-target switching, or a decision that no target needed to be collared at that time. It is also important to appreciate that there is a non-trivial difference in how one should interpret the SHAP results as a function of prediction horizon. Although accurately predicting the target selection decisions of herders further in the future provides compelling evidence of the predictive power of SML based LSTMNN modeling (and the potential stability or predictability of human behavior), predicting these decisions well in advance of the time that decision is made can provide less insight about what information the herder actually used. As noted above, the opposite is also true, for small prediction horizons the mapping between input features and model predictions might also provide less insight about what information a herder used to make a target selection decision, as the herder is likely already enacting a made decision.
For the present work, we continued to focus on Thor = 16 and 32 (i.e., 640 ms and 1280 ms, respectively), as these two prediction horizons appeared to provide a good approximation of the lower and upper bounds of the timescale of herder target selection decisions. This was again motivated by the analysis of the players inter-target movement time, which as detailed above was on average less than 600 ms for both experts and novices. More specifically, for experts, $75.16\%$ of their inter-target movement times were less than 640 ms, with $97.38\%$ below 1280 ms. Similarly, for novices, $69.54\%$ of their inter-target movement times were less than 640 ms, and $91.36\%$ below 1280 ms (see Supplementary Information, Sec. 3). Thus, given the fast-paced nature of the task and the assumption that herders typically decided which target to corral next just prior to action onset68,71, it seemed likely that the majority of herder target selection decisions were made within this 640 to 1280 ms window.
With regard to target ID 6 = 0 predictions, a close inspection of Fig. 6a revealed that the relative distance between the herder and the predicted target and the herder and the other (non-predicted) targets were consistently identified as the key input features for both expert and novice predictions. Indeed, these features nearly always ranked within the top 5 features on average, independent of prediction horizon. The distance of the predicted target from the containment region was also nearly always ranked as a top 10 feature independent of expertise and prediction horizon. Interestingly, these results are consistent with the heuristic target selection models previously developed by69,71, in which human herders completing the same herding task explored here were assumed to pick targets that were (a) furthest from the containment region, but (b) closer to themselves than to their co-herder. Thus, the current results provide empirical support for these previous assumptions.
Recall, that the results of the Kendall’s τ analysis found very little rankorder similarity between novices and experts. Given the latter SHAP results, this implied that although expert and novice decisions appeared to be founded on similar target distance information, the specific importance ordering of target distances as a function of feature class was different across experts and novices.
Indeed, a closer inspection of the top 10 feature rankings of experts and novices (see Supplementary Information, Sec. 6) revealed that for expert target selection predictions the distance of targets from the co-herder were always ranked as the most important feature, whereas for novices the distance of targets from themselves (i.e., the herder) were more often ranked as the most important feature. Although, this difference may seem subtle, it suggests that experts were more attuned to what targets better afforded corralling by their co-herder.
Further support for the latter conclusion was provided by the SHAP results for target ID = 0, where the distance of targets from a player’s co-herder were consistently ranked as a top 5 feature for experts, but not for novices; see Fig. 6b. That is, the decisions of experts to not corral a target appeared to be more evenly determined by the distance of targets from themselves (i.e., distance from herder) and their co-herder, whereas novices decisions were more heavily weight by the distance of targets from themselves. Again, this suggest that experts were more attuned to what target selection decisions were best actualized by their co-herder compared to novices.
Consistent with the recent results of76, the SHAP analysis revealed that information about the direction of motion of the predicted targets also played an important role in the target selection decisions of experts and novices. Another subtle difference between expert and novice predictions, however, was that this finding was more pronounced for experts compared to novices, with the direction of motion of the predicted target ranked as a top 10 feature on average for novices and as a top 20 feature on average for experts for both Thor = 16 and 32. Direction of motion was also more heavily weighted overall for expert target ID = 0 predictions in the shortest prediction horizon. This implied that expert target selection decisions were more heavily influenced by whether a target was moving towards or away from the containment region. Indeed, key to task success is ensuring that targets are moving towards the containment region, irrespective of the distance from the containment area (given that targets are constrained to move within a defined “fenced” boundary). Thus, it is often better to choose to corral a closer target that is moving away from the containment region, than to choose a target that is further away but moving towards the containment region.
Finally, the other difference between the SHAP results for expert and novice predictions related to the importance of herder acceleration and velocity. Indeed, these features were often ranked as a top 15 feature for novice predictions, particularly for no-target predictions when Thor = 16. Given that $29.54\%$ of novice inter-target movement times were greater than 640 ms, this result may be due to the novice LSTMNN models simply learning to map the movement information of novice herders to the periods of target ID = 0 that occur during inter-target movements. That is, rather than indicating that novices herders were influenced by their own velocity or acceleration, this may be a consequence of the slower action-decision timescales and inter-target movements of novices compared to experts. Finding that herder velocity were less important for novice predictions when Thor = 32 is consistent with this possibility. Thus, returning to the question of what prediction horizon best captured the decision timescale of herders, the latter result suggests that the SHAP results for the Thor = 32 prediction horizon may better reflect the information employed by novice herders when making target selection decisions.
## Discussion
The current study leveraged recent advances in SML based LSTM modeling and explainable AI methods to model and understand the decision-making activity of expert and non-expert human actors performing a complex, fast-paced multiagent herding task68,77. Results revealed that short (1 s) state information sequences (Tseq) could be used to train LSTMNN models to accurately predict the target selection decisions of both expert and novice players. Importantly, model predictions were made prospectively, with the majority of predictions for Thor ≥ 16 occurring before the target selection decision of herders were enacted or observable within the state input sequence. It is important to note that model effectiveness was not restricted to Tseq = 1 s or Thor ≥ 16. As detailed in the Supplementary Information, Sec. 3–4, LSTMNN models trained using sequence lengths of 0.5 to 2 s could accurately predict (above $95\%$) target selection decision at prediction horizons ranging from 20 ms to 2.56 s. Moreover, although correct predictions at Thor ≥ 16 does not provide definitive evidence that these predictions preceded a herders intent, this possibility seems likely as the action decisions made by human actors during skillful action are spontaneously tuned responses to the unfolding dynamics of a task13,18,20 and, for the type of perceptual-motor task investigated here, often only occur 150 to 300 ms prior to action onset78. A significant implication is that the current modeling approach could be employed for the anticipatory correction of human action decisions during task training and real-time task engagement, as well as to develop more ‘human-like’ artificial or robotic agents79 that are capable of robustly forecasting and reciprocally adjusting to the behavior of human co-actors during human–machine interaction contexts.
An interesting avenue for future research would be to explore the degree to which the current modeling approach could be employed to predict human decision-making events across a variable prediction horizon. For instance, for the current task context this would equate to predicting target switching decisions. Future research could also explore the functional relationship between prediction horizon length and accuracy as a function of the timescale of a task and its decision-making dynamics. Of interest would be whether the approach employed here can be adapted from the fast-paced decision timescales that were explored here to tasks that involve much slower decision timescales (e.g., tasks where the involved actions decisions are taken over tens of seconds or minutes).
A key finding of the current study was that the trained LSTMNN models were expertise specific, in that, when the expertise level of the training and test data was mismatched, prediction performance dropped to near chance levels. Consistent with decisions of skillful actors being a function of an actor’s trained attunement to the information that best specifies what action possibilities will ensure task completion20–22, this resulted from the expert and novice LSTMNN models weighting input features differently. These differences were identified using SHAP, with average SHAP feature rankings revealing that experts were more influenced by information about the state of their co-herders and were also more attuned to target direction of motion information compared to novices. Together with finding that experts transitioned between targets less often than novices, this suggests that experts were more attuned to information that better specified the collective state of the herding system, including when and what targets afforded corralling by themselves and their co-herder.
To our knowledge, no previous research has employed an explainable AI technique to try to understand and explain the decision-making behavior of human actors during skillful action, let alone identify the differences between expert and novice actors or experimental conditions within the context of coordinated joint-action. To date, research on explainable AI has focused on the ability of these techniques to make AI models more understandable to human users80,81 and to argument or enhance the decision making capabilities of human users82. And, while these work have often drawn connection to cognitive and psychologies models and theories of human decision making, the utility of explainable AI for specifically understanding the how, why and when of human decision making has not been considered (for an exception, see83). We openly acknowledge that employing explainable-AI and SML trained LSTMNN to understand human decision-making is based on two fundamental assumptions: (i) that the input features employed for model training includes the informational variables employed by human actors and (ii) that the mapping between input feature weights and model predictions is isomorphic with the actual information-decision mapping that underlies human action decisions; and that these assumptions need to be validated in future work.
We also acknowledge that task explored here provided players (herders) with access to full (global) state information. That is, at any given time, the herders could (more or less) always see the positions and movements of the other herder and all the target agents. Accordingly, another interesting avenue of future research is to explore whether the SML and explainable-AI approach proposed here can also be employed to model and understand human decision making when full access to the state of the task environment or system is inaccessible. This could be addressed, for example, by attempting to model the target selection decisions of human players completing a first-person herding game (e.g.,84), where each herder only has local (first-person field of view) information about the state of the task environment at any point in time.
Future research should also compare the effectiveness of the SHAP technique employed here with other explainable AI tools, such as LIME61, Deep-Lift62 or LRP63, as well as interpretable Transformer techniques85,86. Future work could also explore the possibility of using explainable-AI to understand decision making in contexts where indecision often occurs87, as well as whether an explainable-AI analysis of the input–output mappings that underlie miss-classification or incorrect decision predictions could be used to understand ineffective decision-making.
Despite the need for future work, the current study provides initial evidence that explainable-AI techniques could provide a powerful tool for understanding the decision-making processes of human actors, including what information best supports optimal task performance. Moreover, although we limited the focused of the current study on informational variables relevant to a visual-motor coordination task, the approach proposed here could be employed across a wide array of task and informational settings (i.e., visual, auditory, haptic, linguistic, etc.). Thus, the potential implications for both basic scientific research and the applied development of decision-making assessment tools could be limitless.
## Methods
All methods and procedures employed for the study were in accordance with Macquarie University human research regulations and were approved by the Macquarie University ethics board (protocol 6457). Informed consent was obtained from all subjects that participated in the original data collection study (71, with this previous study covered under the same ethics protocol listed above).
## Human herding task and data
Novice and expert human performance data from the joint-action herding experiments conducted in71 were employed for the current study. The herding task (game), developed with Unity-3D game engine (Unity Technologies LTD, CA), required pairs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{N}_{H}$$\end{document}N^$H = 2$) of human participants (players) to control virtual herding agents to corral and contain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{N}_{T}$$\end{document}N^$T = 4$ randomly moving target agents within a designated containment area positioned at the center of a game field. The task was performed on large 70″ touch screen monitors (see Fig. 1a), with the human participants using touch-pen stylus to control the location of motion of the herder agents. The targets’ movement dynamics were defined by Brownian motion when not being influenced by a herder agent, and when influenced by a herder agent would move in a direction away from the herder agent. During task performance, the position and velocity of all herders and targets (as well as other general game states) was recorded at 50 Hz. Pairs had a maximum of 2 min to corral the targets into the containment area, with task success achieved if pairs could keep the targets contained within the containment area for 20 s. Full details of the experimental set-up and data collection process can be found in71.
Novice data was extracted from 40 successful trials performed by 10 different novice pairs (4 successful trials from each pair). From each trial, we extracted state data from the time of task onset to when all four target agents were first contained within the specified containment area; that is, when the herders had corralled all the agents inside the containment area for the first time. The remaining trial data was disregarded as players treat the target herd as single entity after it is contained and individual target selection decisions no longer occur69,71. Note that a human herder was considered to be a “novice” if they were unfamiliar with the herding task prior to the data collection session. Novices repeated the task until they had completed the 4 successful trials included in the novice data-set (with an average of 8 unsuccessful trials per pair).
Expert Data was extracted from 48 successful trials performed by 3 pairs of human players with extensive experience (completed more than 100 successful trials) performing the simulated multiagent herding task (i.e., several authors from71). As with the novice data, we extracted state data from the time of task onset to when all four target agents were first contained within the specified containment area.
## State input features
From position and velocity data recorded in the original novice and expert data-sets we extracted and derived the following Nsv = 48 state variables:The radial and angular distance (∆, Ψ) between herders,The radial and angular distance (∆i,j, Ψi,j) of target i from herder j,The radial and angular distance of herder j or target agent i from the center of the containment region. The radial velocity and acceleration of herders (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{r}\left(t \right), \, \ddot{r}\left(t \right)$$\end{document}r˙t,r¨t) and target agents (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{\rho }\left(t \right), \, \ddot{\rho }\left(t \right)$$\end{document}ρ˙t,ρ¨t),The direction of motion of herder and target agents.
## Target coding
A paid research assistant, naive to the study’s purpose, coded (classified) what target (or not) a given herder was corralling at each point in time via an interactive data playback Unity3D (version 2018LTS) application that played back the original recorded data-set (see Fig. 1b). Data playback speed could be decreased to $\frac{1}{8}$ speed, as well as stepped frame by frame, with each target visually labeled with a fixed number (1 to 4). At each time step, the target agent a given human herder was corralling was coded by the research assistant with an integer number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{i} \in \left[{0,\hat{N}_{T} } \right]$$\end{document}i~∈0,N^T, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{i}$$\end{document}i~ = 0 meaning “no target agent being corralled” and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{i}$$\end{document}i~ 6 = 0 being the class ID of the target agent being corralled at that time step.
## Human inter-target movement time
To determine the time it took expert and novice herders to move from one target to the next we calculated the inter-target movement time when switching between targets ID = 1 to 4 (i.e., we ignored switch events from or to ID = 0). We calculated the time from when a human herder moved outside the region of repulsive influence of the current target and entered the region of repulsive influence of the next target. More specifically, inter-target movement time was the difference in milliseconds between the time instant at which a herder entered the repulsive radius (i.e., 0.12 m around each target agent) of the current target and their relative distance was decreasing, and the time instant at which the herder left the repulsive region around the previously corralled target and their relative distance increased. In addition to the mean results report above, see Supplementary Information, Sec. 2 for the distributions of inter-target movement times for expert and novice herders.
## Training and test set data
All successful trial data, per level of expertise, was stacked in a common feature processed novice or expert data-set along with the corresponding target codes (ID 0 to 4). From the resultant, feature processed, novice and expert data-sets we randomly extracted 2 sets of Ntrain = 21,000 training samples and 20 test sets of Ntest = 2000 samples each. The corresponding pseudo-code is reported in Algorithm 1.
The first training set and 10 test sets contained transitioning/switching samples in balanced proportion ($25\%$ each). This data set was used to train and test the models presented here. The second training set and the remaining 10 test sets contained transitioning/switching samples in the same proportion as in the entire data-set see Supplementary Information, Sec. 3–5 for information about the models trained using the latter unbalanced training sets.
Here, samples refer to pairs of state feature sequences and target label codes. Sequences are composed by Nseq = 25 consecutive instances of the above listed Nsv state variables, sampled at dt = 0.04 s, covering Tseq = 1 s system evolution, and labels are the ID of the agent being targeted, selected at Thor = 16dt and Thor = 32dt seconds from the end of the corresponding sequence.
In Supplementary Information, Sec. 3 we also consider values of Tseq = 0.5 s and Tseq = 2 s varying the sampling time to dt = 0.02 and dt = 0.08 s respectively. As in the default case presented in the Results, the novice and expert models trained with these different Tseq lengths also obtain accuracy values greater than $95\%$ when tested on data from the same expertise level (e.g., expert-expert) and closer to $50\%$ when tested on data from the different level of expertise (e.g., novice-expert).
## LSTM network and model training
For each combination of expertise and prediction horizon, we trained a LongShort Term Memory (LSTM) artificial neural network with Dropout layers44,45, using Adam optimization and stratified K-fold cross-validation with $K = 5$ (code available at https://github.com/FabLtt/ExplainedDecisions). We used Bayesian Optimization to tune the learning rate (α = 0.0018) of the Adam optimizer and the hyperparameters of the LSTMNN (i.e., the number of LSTM hidden layers, number of neurons in each layer, and dropout rates). The Input Layer and the output Dense layer of the optimized LSTMNN had dimensionality (Tseq, Nsv) and (Tseq, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{N}_{T}$$\end{document}N^T + 1), respectively. In the center, 3 hidden LSTM layers of 253, 45 and 8 neurons were alternated with Dropout layers of equal dimensionality. The dropout rate of each LSTM layers of the novice and expert models was 0.1145. For the dropout layers between each LSTM layer, the dropout rate was 0.0145. To avoid over-fitting, training was stopped when the logarithmic loss—that penalises false predictions—on the validation set stopped improving; the validation set being a randomly extracted $10\%$ of the training set. The LSTMNN was built and trained using Python 3.7.1 and Tensorflow library (https://www.tensorflow.org/, version 1.15). The corresponding pseudo-code is reported in Algorithm 2.
## Model performance
Performance of the LSTMNN were validated using the following measures: Accuracy—the fraction of correct predictions outputs among the samples tested; Precision—how valid the prediction was, that is the portion of relevant outputs among the predicted ones; Recall—how complete the prediction was, that is, the portion of relevant outputs that were predicted. Note that when Precision and Recall reach $100\%$, false positive outputs and false negative outputs are absent, respectively. Additionally, we also report the F1 score for model prediction’s, with higher values of F1 score, the harmonic mean of Precision and Recall, expressing how precise and robust the model prediction was.
## Shapley additive explanation
Given a sample, the SHAP algorithm assigns to each input feature an importance value (https://github.com/slundberg/shap, version 0.31). This is an estimate of the contribution of each feature to the difference between the actual prediction output and the mean prediction output. We randomly selected \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{N}_{train}^{S}$$\end{document}N^trainS = 200 samples from the training set as background set, that is, as a prior distribution of the input space used to approximate the conditional expectations of the SHAP values65. We applied SHAP DeepExplainer on Ntest = 6000 of the test samples used to evaluate performance65 and obtained the corresponding SHAP values for each state variable. To derive the corresponding approximate global feature importance measure (shown in Fig. 6) we averaged over the test set, for each class of prediction output (i.e., target ID). The corresponding pseudo-code is reported in Algorithm 3.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31807-1.
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|
---
title: Enhanced Ca2+-channeling complex formation at the ER-mitochondria interface
underlies the pathogenesis of alcohol-associated liver disease
authors:
- Themis Thoudam
- Dipanjan Chanda
- Jung Yi Lee
- Min-Kyo Jung
- Ibotombi Singh Sinam
- Byung-Gyu Kim
- Bo-Yoon Park
- Woong Hee Kwon
- Hyo-Jeong Kim
- Myeongjin Kim
- Chae Won Lim
- Hoyul Lee
- Yang Hoon Huh
- Caroline A. Miller
- Romil Saxena
- Nicholas J. Skill
- Nazmul Huda
- Praveen Kusumanchi
- Jing Ma
- Zhihong Yang
- Min-Ji Kim
- Ji Young Mun
- Robert A. Harris
- Jae-Han Jeon
- Suthat Liangpunsakul
- In-Kyu Lee
journal: Nature Communications
year: 2023
pmcid: PMC10042999
doi: 10.1038/s41467-023-37214-4
license: CC BY 4.0
---
# Enhanced Ca2+-channeling complex formation at the ER-mitochondria interface underlies the pathogenesis of alcohol-associated liver disease
## Abstract
Ca2+ overload-induced mitochondrial dysfunction is considered as a major contributing factor in the pathogenesis of alcohol-associated liver disease (ALD). However, the initiating factors that drive mitochondrial Ca2+ accumulation in ALD remain elusive. Here, we demonstrate that an aberrant increase in hepatic GRP75-mediated mitochondria-associated ER membrane (MAM) Ca2+-channeling (MCC) complex formation promotes mitochondrial dysfunction in vitro and in male mouse model of ALD. Unbiased transcriptomic analysis reveals PDK4 as a prominently inducible MAM kinase in ALD. Analysis of human ALD cohorts further corroborate these findings. Additional mass spectrometry analysis unveils GRP75 as a downstream phosphorylation target of PDK4. Conversely, non-phosphorylatable GRP75 mutation or genetic ablation of PDK4 prevents alcohol-induced MCC complex formation and subsequent mitochondrial Ca2+ accumulation and dysfunction. Finally, ectopic induction of MAM formation reverses the protective effect of PDK4 deficiency in alcohol-induced liver injury. Together, our study defines a mediatory role of PDK4 in promoting mitochondrial dysfunction in ALD.
Ca2+ overload-induced mitochondrial dysfunction is considered a contributing factor alcohol-associated liver disease pathogenesis. Here the authors report that PDK4 promotes Ca2 + -channelling complex formation at the endoplasmic reticulum-mitochondria contact sites, which contributes to the pathogenesis of alcohol-associated liver disease in studies with male mouse and hepatocyte models.
## Introduction
Mitochondrial dysfunction is one of the major factors involved in the pathogenesis of alcohol-associated liver disease (ALD)1,2. Altered morphology and defective mitochondria are observed throughout the spectrum of ALD, from steatosis, hepatitis, and cirrhosis3–5. Primarily, alcohol is metabolized in hepatocytes where mitochondria play a crucial role in converting the highly reactive alcohol intermediate, acetaldehyde to acetate via acetaldehyde dehydrogenase6. Mitochondria are an essential organelle involved in nutrient metabolism and adenosine triphosphate (ATP) synthesis to regulate cellular homeostasis7. However, deterioration of mitochondria function contributes to excessive intracellular reactive oxygen species (ROS) formation leading to the activation of stress signaling pathways in ALD1,8,9. Among the myriad pathways involved in ALD, mitochondrial Ca2+ accumulation has been linked to mitochondrial dysfunction10–12. However, the factors involved in promoting mitochondrial Ca2+ accumulation and dysfunction during the pathogenesis of ALD remain elusive.
Mitochondria-associated ER membrane (MAMs), an interface between mitochondria and endoplasmic reticulum (ER) with an approximate distance of 10~50 nm13, serves as a major hotspot for various signaling pathways to regulate mitochondrial dynamics, autophagy, and inflammasome formation14,15. One of its crucial functions is to regulate Ca2+ transport from the ER to mitochondria to support mitochondrial metabolism and respiration. In the MAM, Ca2+ transfer is regulated by the formation of the MAM Ca2+-channeling (MCC) complex, consisting of glucose-regulated protein 75 (GRP75), inositol-1-tri-phosphate receptor 1 (IP3R1), and voltage-dependent anion channel 1 (VDAC1)16. GRP75 is a functional tether which facilitates the ER-mitochondria interaction, and efficient Ca2+ transport to the mitochondria by physically interacting with the ER membrane Ca2+ efflux channel, IP3R1, and the outer mitochondrial membrane (OMM) protein, VDAC117. An aberrant increase in MAM formation promotes mitochondrial Ca2+ overload and dysfunction during hepatic insulin resistance18,19. However, the involvement of the MAM in alcohol-induced mitochondrial Ca2+ accumulation, mitochondrial dysfunction, and alcohol-induced liver injury has not been explored.
Pyruvate dehydrogenase kinase 4 (PDK4), one of the four PDK isoenzymes, regulates pyruvate dehydrogenase (PDH) complex activity to control glucose and fatty acid oxidation in the mitochondria20. Enhanced hepatic PDK4 expression in non-alcoholic steatohepatitis patients was shown to be correlated with disease aggravation21. On the other hand, genetic ablation of Pdk4 protects against obesity-induced hepatic insulin resistance and steatosis21–23. We reported previously that a sub-population of PDK4 localizes in the MAM and interacts with the MCC complex to regulate the MAM formation. In addition, we found that increase in PDK4 expression induced MAM formation and promoted fatty acid-induced mitochondrial dysfunction24. Conversely, PDK4 deficiency prevented fatty acid-induced mitochondrial dysfunction and insulin resistance via suppression of MAM20,24. However, the potential role of PDK4 and MAM formation on mitochondrial dysfunction in ALD has not been elucidated. Here, we aimed to determine the contribution of MAM in the pathogenesis of alcohol-induced mitochondrial dysfunction and investigate the upstream factors that may modulate MAM formation in ALD.
In this study, we found that ALD exhibits abnormal enhancement of MCC complex-dependent MAM formation alongside a notable increase in PDK4 expression. Henceforth, we identify that PDK4 phosphorylates a key MCC complex component, GRP75, at multiple sites to promote alcohol-induced mitochondrial Ca2+ accumulation and dysfunction. Conversely, genetic ablation of PDK4 ameliorates mitochondrial dysfunction and protects against alcohol-induced liver injury. Overall, we demonstrate a potential mediatory role of PDK4 in the pathogenesis of ALD.
## ER-Mitochondria contacts are augmented in alcohol-associated liver disease
To study the effect of alcohol on MAM dynamics, we utilized a well-established murine model of ALD25 (Fig. 1a). Alcohol largely affects the hepatocytes in the perivenous region26, therefore, we first analyzed liver sections obtained from mice fed with a control diet (CD) or ethanol (EtOH)-containing diet (ED) by transmission electron microscopy (TEM) in the perivenous region (Supplementary fig. 1a). Mitochondrial perimeter was significantly reduced in ED-fed mice compared to CD controls, while no difference was observed in the ER perimeter (Fig. 1b–d) between these two groups. Interestingly, the percentage of mitochondria in close proximity to the ER (within 50 nm distance) was significantly higher in ED-fed mice (Fig. 1b, e). Morphologically, most of the mitochondria in ED-fed mice were ER wrapped, resulting in increased MAM length compared to controls (Fig. 1f, g). Next, we asked whether alcohol also affects the MAM dynamics in the periportal region (Supplementary Fig. 1a). In contrast to the perivenous region, the mitochondrial perimeter was significantly increased in the periportal region, while the ER perimeter was marginally reduced in ED-fed mice when compared with CD-fed mice (Supplementary Fig. 1b–d). On the other hand, similar to the perivenous region, we observed a significant increase in the percentage of mitochondria adjacent to the ER in ED-fed mice compared to CD-fed mice (Supplementary Fig. 1b, e). However, no difference in MAM length was observed between CD and ED-fed mice (Supplementary Fig. 1f, g). These results revealed a differential effect of alcohol on mitochondria and ER ultrastructure in the perivenous and periportal hepatocytes. Notably, despite having a difference in the MAM length, the association between ER and mitochondria was enhanced in both regions, suggesting that alcohol promotes MAM formation but more prominently in the perivenous region. Fig. 1Alcohol augments hepatic ER-mitochondria interaction.a Graphical depiction of alcohol feeding model in mice. b Mitochondria and ER morphology in the perivenous region of control diet (CD) and EtOH diet (ED)-fed mice liver sections were visualized using TEM (Scale bars, 5 µm). The ER and mitochondria in the TEM images were reconstructed graphically to visualize MAM formation. ( <50 nm distance between ER and mitochondria were considered as MAM). Mito: Mitochondria (red), ER: Endoplasmic Reticulum (green), Nucleus (blue). c–e Quantification of average mitochondria perimeter (c) and ER perimeter (d), and percentage of mitochondria apposition to ER (e), $$n = 15$$ microscopic fields each (number of mitochondria/ER analyzed, CD: $\frac{260}{620}$ and ED: $\frac{390}{1040}$ from 3 mice/group). f Magnified TEM images (Scale bars, 1 µm). M: Mitochondria, ER: Endoplasmic Reticulum. g Quantification of MAM length to mitochondrial perimeter ratio, $$n = 15$$ microscopic fields each (number of mitochondria/ER analyzed, CD: $\frac{260}{620}$ and ED: $\frac{390}{1040}$ from 3 mice/group). h Confocal microscopy analysis of ER and mitochondria using PDI (Green) and TOM20 (Red) antibodies, respectively, in primary mouse hepatocytes cultured with or without 100 mM EtOH for 24 h (Scale bars, 20 µm). Colocalized portion of ER and *Mitochondria is* shown in white. i Quantification of co-localization between TOM20 and PDI (Ctrl, $$n = 29$$; EtOH, $$n = 30$$ microscopic fields with >200 cells from three independent experiments). All values are represented as mean ± SEM. *** $p \leq 0.001$; ****$p \leq 0.0001$ (Two-tailed unpaired t-test).
To reconfirm our in vivo observations, we treated isolated primary hepatocytes and murine hepatocyte cell line, AML12 cells with various doses of EtOH and confirmed that 100 mM EtOH is the optimal dose for inducing Cyp2E1, a sensitive indicator for alcohol exposure (Supplementary Fig. 2a, b). Next, we assessed the MAM formation by confocal microscopy after immunostaining the ER and mitochondria with protein disulfide isomerase (PDI) and translocase of outer membrane 20 (TOM20), respectively. We observed that EtOH treatment led to extensive co-localization of ER and mitochondria (Fig. 1h, i). Taken together, our results suggest that EtOH promotes the MAM formation in both in vitro and in vivo ALD models.
## Hepatic PDK4 expression and the MAM Ca2+-channeling complex formation is elevated in alcohol-associated liver disease
Next, to delineate the molecular mechanism underlying EtOH-induced MAM formation, we performed transcriptomic analysis of our previously reported RNA-Seq datasets (GEO155830) of CD and ED-fed mice liver27 to examine the differential expression of genes that are known to play a role in the MAM formation by encoding MAM resident proteins14–16,28. Among the 97 MAM-associated genes (Supplementary Table 1), four genes were significantly upregulated in ED-fed livers (pyruvate dehydrogenase kinase 4 [Pdk4], transglutaminase 2 [Tgm2], cell death inducing P53 target 1 [Cdip1], and activating transcription factor 6 [Atf6]), whereas one was downregulated (oxysterol binding protein like 8 [Osbpl8]) (Fig. 2a). The expression of Pdk4 was the highest among the 4 upregulated genes (Fig. 2a, b). To validate the RNA-Seq results, we performed qPCR and immunoblot analyses and confirmed the induction of hepatic PDK4 in ED-fed mice at the transcript and protein levels (Fig. 2c–e). To draw the clinical relevance, we analyzed PDK4 expression in liver sections of patients with ALD (Supplementary Table 2). We noted that both mRNA and protein expression of PDK4 was substantially higher in patients with ALD compared to healthy subjects (HS) (Fig. 2f–i). None of the other PDK isoforms showed any observable difference in expression in mice, except for hepatic PDK2 expression which was induced in ED-fed mice (Fig. 2c–e). Moreover, enhanced PDK4 expression correlated with an increase in PDH phosphorylation at the Ser300 (Fig. 2d, e). However, PDK2 gene and protein expression remained unchanged, whereas, PDK1 and PDK3 protein levels were reduced in patients with ALD but not in the mRNA levels compared to HS (Fig. 2f–h). This indicated that among the PDK isoforms, hepatic PDK4 was the only isoform consistently upregulated in both ED-fed mice and patients with ALD.Fig. 2PDK4 is highly induced in human and mouse model of ALD.a Gene expression of 97 known genes associated with MAM (Black dots) was analyzed from RNA-Seq (GEO155830) datasets of CD and ED-fed mice liver and represented as Volcano-plot. NS: Non-significant (Gray dots), UP: Significantly upregulated (Red dots) and DOWN: Significantly downregulated genes (Blue dots). b Heatmap showing the upregulated and downregulated MAM-associated genes in ED-fed mice compared to CD-fed mice. c–e mRNA ($$n = 5$$ mice/group) (c) and protein ($$n = 3$$ mice/group) (d-e) expression of PDK isoenzymes in CD and ED-fed mice liver. f–h protein (f, g) and mRNA (h) expression of PDKs in healthy subjects (HS) and alcohol-associated liver disease (ALD) human liver specimens ($$n = 5$$ each). i Representative image of immunohistochemical (IHC) analysis of PDK4 expression in the liver sections of HS and patients with ALD ($$n = 5$$ each) (Scale bars, 100 µm). j *Immunoblot analysis* of MCC complex proteins in the subcellular fractions isolated from CD and ED-fed mice liver. TL: tissue lysate, Mito: pure mitochondria, MAM: mitochondria-associated ER membrane and ER: endoplasmic reticulum. k MCC complex formation was evaluated by co-immunoprecipitation (co-IP) using GRP75 antibody and immunoblotting of the indicated proteins (left), quantifications relative to CD are provided below each blot and inputs (right). l Representative confocal microscope images (left) and quantification (right) of in situ PLA showing the IP3R1-VDAC1 interactions in primary mouse hepatocytes treated with or without 100 mM EtOH for 24 h (Scale bars, 20 µm) ($$n = 20$$ microscopic fields each with >500 cells from three independent experiments). m Representative confocal microscope images (left) and quantification (right) of in situ PLA showing the IP3R1-VDAC1 interactions in HS and ALD human liver tissue sections (Scale bars, 20 µm) (HS, $$n = 29$$; ALD, $$n = 40$$ microscopic fields with >500 nuclei from 5 HS and ALD patient liver specimens). All values are represented as mean ± SEM, *$p \leq 0.05$; **$p \leq 0.01$; ****$p \leq 0.0001$ (Two-tailed unpaired t-test).
We have previously reported that PDK4 regulates the MCC complex formation24. Therefore, we evaluated whether the upregulation of PDK4 expression promotes MCC complex formation in ALD. To that end, we performed subcellular fractionation on CD and ED-fed mice livers to determine the expression of MCC complex proteins, particularly in the MAM. We found an increase in the localization of PDK4 along with IP3R1, GRP75, and VDAC1 in the MAM fractions in ED-fed mice compared to CD-fed mice (Fig. 2j). Co-immunoprecipitation (co-IP) assays further revealed an increase in GRP75 interaction with PDK4, IP3R1, and VDAC1 but not with IP3R2 (the other IP3R isoform expressed in the liver) in ED-fed mice liver when compared to controls (Fig. 2k), confirming a direct physical association among the MCC complex proteins. Next, we evaluated the IP3R1-VDAC1 interaction by in situ proximity ligation assay (PLA), a method that detects the proximity between two proteins within a range of 40 nm, to evaluate MCC complex formation29,30. The specificity of IP3R1 and VDAC1 antibodies used for in situ PLA were validated by knocking down either IP3R1 or VDAC1 (Supplementary Fig. 2c–f). We found a significant increase in IP3R1-VDAC1 interaction upon EtOH treatment in primary mouse hepatocytes (Fig. 2l), AML12 cells (Supplementary Fig. 2g, h), and in the liver tissues of patients with ALD (Fig. 2m). Overall, these results demonstrate that MAM-associated PDK4 and MCC complex formation is elevated in ALD.
## PDK4 modulates MCC complex formation via GRP75 phosphorylation
As PDK4 is a serine/threonine kinase, we next examined whether PDK4 phosphorylates the MCC complex components. To that end, we performed immunoprecipitation using a phosphomotif antibody to detect the phosphorylation of MCC complex proteins. Interestingly, only GRP75 phosphorylation was induced upon EtOH treatment in primary mouse wildtype (WT) hepatocytes but not in the Pdk4 knockout (Pdk4−/−) hepatocytes (Fig. 3a). As depicted in Fig. 2j, PDK4 and GRP75 are predominantly expressed in the mitochondria in addition to the MAM. To determine the site of interaction between PDK4 and GRP75, we performed in situ PLA in AML12 cells co-expressing FLAG-tagged PDK4, mitochondria target-blue fluorescence protein (mito-BFP), and ER-targeted SEC61B (SEC61 Translocon Subunit Beta) tagged with GFP. In situ PLA blobs confirmed the interaction between GRP75 and PDK4 along with the individual MCC complex components at the ER-mitochondria interface (indicated by white arrows in the inset) (Fig. 3b, c).Fig. 3PDK4 phosphorylates GRP75 to regulate MCC complex formation.a Phosphorylation of MCC complex proteins in Pdk4+/+ or Pdk4−/− primary mouse hepatocytes treated with 100 mM EtOH for 24 h were analyzed by IP with phospho-serine/threonine (p-S/T) antibody. b PDK4-FLAG and GRP75 interaction (white arrows) was analyzed by in situ PLA in AML12 cells co-expressing pcDNA/PDK4-FLAG, mito-BFP (mitochondria) and Sec61B (ER) (Scale bars, 20 µm). c GRP75 and IP3R1/VDAC1 interaction (white arrows) was analyzed by in situ PLA in AML12 cells co-expressing mito-BFP and Sec61B (Scale bars, 20 µm). d In vitro kinase assay of recombinant GRP75 and PDK4 proteins in the presence of ATP and the phosphorylation of GRP75 was detected using p-S/T antibody by immunoblotting. e A model GRP75 protein structure highlighting the phosphorylation sites identified by mass-spectrometry. f Amino acid sequence comparison of GRP75 phosphorylation sites among the mammalian species. g GRP75 phosphorylation and its interaction with MCC complex proteins were analyzed by co-IP using HA antibody in AML12 cells co-overexpressing PDK4-FLAG and HA-tagged wild-type (WT) or phospho-mutant GRP75 constructs. h Inputs of (g). i IP3R1 and exogeneous GRP75 interaction was analyzed by in situ PLA in AML12 cells co-overexpressing PDK4-FLAG and HA-tagged WT or phospho-mutant GRP75 constructs. ( Scale bars, 20 µm). j Quantification of in situ PLA blobs of (i) (pcDNA+WT, $$n = 12$$; PDK4-FLAG + WT, $$n = 12$$; PDK4-FLAG + 120 A, $$n = 11$$; PDK4-FLAG + 266 A, $$n = 11$$; PDK4-FLAG + 267 A, $$n = 10$$ microscopic fields with >500 cells from 3 independent experiments). k Measurement of mitochondrial Ca2+ level in AML12 cells (stably expressing 4mitD3-CPV) transfected with PDK4 and WT or phospho-mutant GRP75 constructs. The YFP and CFP fluorescence was recorded every 10 s. 100 µM ATP was injected at 90 s. l, m Quantification of basal (l) and ATP-stimulated delta-peak from the basal (m) values of (k). ( $$n = 9$$ biological replicates from three independent experiments). All quantifications are represented as mean ± SEM, ***$p \leq 0.001$; ****$p \leq 0.0001$ (Ordinary one-way ANOVA, Dunnett’s multiple comparisons test). Quantifications relative to control are provided below each blot.
Next, we evaluated whether GRP75 is an immediate substrate of PDK4 by in vitro kinase assay using human GRP75 and PDK4 recombinant proteins. We found that PDK4 directly phosphorylates GRP75 (Fig. 3d). Further examination by mass spectrometry revealed 3 phosphorylation sites, corresponding to Thr120, Ser266, and Thr267 in the nucleotide binding domain (NBD) of human GRP75 (Fig. 3e and Supplementary Fig. 3a–c), which are highly conserved among the mammalian species (Fig. 3f). To gain further insight into the role of GRP75 phosphorylation in MCC complex formation, we generated non-phosphorylatable mutants of GRP75 by replacing the phosphorylation sites with alanine (120 A, 266 A, and 267 A). The cDNA variants of GRP75 were then co-transfected with FLAG-tagged PDK4 in AML12 cells and analyzed by co-IP. PDK4-induced phosphorylation of GRP75 was markedly suppressed in 120 A, 267 A mutant proteins, and to a lesser extent by 266 A mutation. In addition, overexpression of PDK4 stimulated WT GRP75 interaction with IP3R1 but not with the mutant proteins (Fig. 3g, h). Of interest, we noticed that the PDK4-induced GRP75 interaction with VDAC1 was reduced by the 266 A and 267 A mutations but not by the 120 A mutation (Fig. 3g, h), suggesting that the phosphorylation of GRP75 at T120 is dispensable for its interaction with VDAC1. Consistent with these results, in situ PLA showed that PDK4-stimulated IP3R1 interaction with WT GRP75 was markedly reduced with GRP75 mutants (Fig. 3i, j), confirming that PDK4 modulates MCC complex formation via GRP75 phosphorylation.
Next, we investigated the effect of PDK4-mediated MCC complex formation on mitochondrial Ca2+ uptake. An increase in MCC complex formation by over-expression of PDK4 (Fig. 3i, j) did not increase the basal mitochondria Ca2+ levels (Fig. 3k, l). However, stimulation of IP3R-mediated Ca2+ release by ATP significantly induced mitochondrial Ca2+ uptake in the cells co-expressing PDK4 and WT-GRP75, but not GRP75 mutants (Fig. 3k, m). Furthermore, GRP75 mutants did not affect the ATP-induced IP3R1-mediated ER Ca2+ release (Supplementary Fig. 4a), suggesting that reduction in mitochondrial Ca2+ uptake upon GRP75 mutation was not caused by decreased ER Ca2+ release. Taken together, our data indicate that PDK4-induced GRP75 phosphorylation promotes MCC complex formation and increases mitochondrial Ca2+ uptake.
## EtOH enhances MCC complex formation and mitochondrial Ca2+ accumulation via induction of GRP75 phosphorylation
To evaluate the effect of EtOH on PDK4-induced GRP75 phosphorylation and MCC complex formation, we performed co-IP assay in EtOH-treated AML12 cells overexpressing WT and mutant GRP75. EtOH markedly induced WT GRP75 phosphorylation, but not the mutants (Fig. 4a). Furthermore, EtOH treatment increased IP3R1 interaction with WT GRP75 but not with the GRP75 mutants (Fig. 4a). Consistent with the PDK4 overexpression (Fig. 3g), EtOH-induced VDAC1 interaction with WT GRP75 was compromised by 266 A and 267 A mutants but remained unaffected with the 120 A mutant (Fig. 4a). In situ PLA further revealed that EtOH-induced IP3R1-VDAC1 interaction was suppressed in GRP75 mutants overexpressing cells (Fig. 4b, c). Consequently, EtOH treatment increased the basal mitochondrial Ca2+ levels, which were further enhanced by ATP stimulation in WT GRP75 expressing cells but not in the mutants overexpressing cells (Fig. 4d–f). However, no difference in IP3R-mediated ER Ca2+ release was observed between the groups, except for a mild increase in 267 A mutant overexpressing cells (Supplementary Fig. 4b), suggesting that GRP75 mutation does not affect IP3R-mediated ER Ca2+ release. A sustained increase in mitochondrial Ca2+ level promotes excessive ROS generation causing mitochondrial dysfunction31. Furthermore, EtOH was shown to interfere with fatty acid oxidation32 and the accumulation of incomplete fatty acid oxidation-derived metabolites can trigger mitochondrial Ca2+ accumulation33. We reasoned that EtOH-induced mitochondrial dysfunction may lead to excessive ROS generation and interfere with lipid metabolism leading to intracellular lipid accumulation. Indeed, EtOH treatment led to an increased mitochondrial ROS formation and enhanced lipid accumulation in WT GRP75 expressing cells, but not in the mutant expressing cells (Fig. 4g–i). Taken together, these data indicate that EtOH-induced MCC complex formation, and subsequent mitochondrial Ca2+ accumulation lead to mitochondrial dysfunction and intracellular lipid accumulation. Fig. 4GRP75 phospho-mutants suppress EtOH-induced MCC complex formation and mitochondrial Ca2+ accumulation.a GRP75 phosphorylation and its interaction with IP3R1 and VDAC1 in AML12 cells overexpressing WT and phospho-mutant GRP75 treated with 100 mM EtOH for 24 h were analyzed by co-IP using HA antibody and immunoblotting. Quantifications relative to control are provided below each blot. b Analysis of IP3R1-VDAC1 interaction by in situ PLA in AML12 cells overexpressing WT and phospho-mutant GRP75 treated with EtOH (Scale bars, 20 µm). c Quantification of in situ PLA blobs of (b) (WT Ctrl, $$n = 18$$; WT/A120/A266/A267 + EtOH, $$n = 12$$ microscopic fields with >500 cells from 3 independent experiments). d Measurement of mitochondrial Ca2+ flux in AML12 cells (stably expressing 4mitD3-CPV) transfected with phospho-mutant GRP75 constructs and treated 100 mM EtOH for 24 h. The YFP and CFP fluorescence was recorded every 10 s and 90 s later 100 µM ATP was injected. e, f Quantification of basal (e) and ATP-stimulated delta-peak from the basal (f) values of (d) ($$n = 10$$ biological replicates from 3 independent experiments). g Analysis of Mitochondrial ROS level (mean fluorescence intensity; MFI) in AML12 cells overexpressing WT or phospho-mutant GRP75 and treated with 100 mM EtOH for 24 h using MitoSOX dye ($$n = 9$$ biological replicates from 3 independent experiments). h Intracellular neutral lipid accumulation was analyzed using the LipidTox dye in AML12 cells overexpressing WT or phospho-mutant GRP75 and treated with 100 mM EtOH for 24 h (Scale bars, 20 µm). i Quantification of fluorescence intensity of (h) ($$n = 10$$ microscopic fields with >500 cells from three independent experiments). All quantifications are represented as mean ± SEM, *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; ****$p \leq 0.0001$ (Ordinary one-way ANOVA, Dunnett’s multiple comparisons test).
## PDK4 deficiency prevents EtOH-induced mitochondrial Ca2+ accumulation and mitochondrial dysfunction
To examine the effect of PDK4 deficiency on EtOH-induced MAM formation and mitochondrial dysfunction in vitro, we first analyzed the MCC complex formation by in situ PLA. The proximity between the MCC complex proteins was significantly increased by EtOH in Pdk4+/+ hepatocytes but this was markedly suppressed in EtOH-treated Pdk4−/− hepatocytes (Fig. 5a–d). Furthermore, analysis of the ER-mitochondria contacts by confocal microscopy revealed that EtOH-induced MAM formation was reduced in Pdk4−/− hepatocytes (Fig. 5e, f). Similarly, knockdown of Pdk4 in AML12 cells suppressed EtOH-induced MAM formation (Supplementary Fig. 5a–c). As a consequence of reduced MAM formation in the absence of PDK4, EtOH-induced mitochondrial Ca2+ accumulation was significantly reduced in Pdk4−/− hepatocytes (Fig. 5g), along with other measures of mitochondrial dysfunction, ROS accumulation (Fig. 5h), and mitochondrial membrane potential (MMP) (Fig. 5i). Notably, even in the absence of EtOH, the ROS level in Pdk4−/− hepatocytes were considerably lower compared to Pdk4+/+ hepatocytes (Fig. 5h). Interestingly, evaluation of the oxygen consumption rate (OCR), a readout of mitochondrial function, revealed that the basal, ATP production-linked, and maximal OCR were significantly higher in Pdk4−/− hepatocytes (Pdk4−/−-Ctrl) when compared to those in Pdk4+/+ hepatocytes (Pdk4+/+-Ctrl), (Fig. 5j, k). This suggests that PDK4 deficiency potentiates mitochondrial function as evidenced by the reduction in mitochondria ROS (Fig. 5h) and increased resting state ATP production-linked respiration. Additionally, EtOH significantly reduced OCR in Pdk4+/+ but not in the Pdk4−/− hepatocytes (Fig. 5j, k). To confirm that these changes in mitochondrial function were not due to, alteration of mitochondrial OXPHOS or deregulation of antioxidant enzymes; Superoxide dismutase 2 (SOD2), catalase and glutathione peroxidases 4 (GPX4), we analyzed the expression of these proteins, but found no observable difference in OXPHOS complex or the antioxidant proteins expression in the absence or presence of EtOH between Pdk4+/+ and Pdk4−/− hepatocytes. However, GPX4 protein level, an enzyme that protects against cellular lipid peroxidation34 was reduced in EtOH-treated Pdk4+/+ hepatocytes but the expression was restored in Pdk4−/− hepatocytes (Supplementary Fig. 5d). Together, these results indicate that PDK4 deficiency protects against EtOH-induced mitochondrial dysfunction via suppression of MCC complex formation. Fig. 5PDK4 deficiency suppresses EtOH-induced MAM formation and mitochondrial dysfunction.a Analysis of interaction between MCC complex proteins in Pdk4+/+ and Pdk4−/− primary mouse hepatocytes treated with or without 100 mM EtOH for 24 h by in situ PLA (Scale bars, 20 µm). b–d Quantification of (a) [IP3R1-GRP75 (b), $$n = 14$$; VDAC1-GRP75 (c), $$n = 14$$; IP3R1-VDAC1 (d), $$n = 15$$ microscopic fields with >300 cells from 3 independent experiments]. e *Immunofluorescence analysis* of ER and mitochondria using PDI (Green) and TOM20 (Red) antibodies in Pdk4+/+ and Pdk4-/- primary mouse hepatocytes treated with or without EtOH for 24 h. Colocalized portion of ER and *Mitochondria is* shown in white (Scale bars, 20 µm). f Quantification of TOM20-PDI colocalization of (e) (Ctrl Pdk4+/+, $$n = 28$$; Ctrl Pdk4−/−, $$n = 24$$; EtOH Pdk4+/+ and EtOH Pdk4−/−, $$n = 30$$ microscopic fields with >300 cells from 3 independent experiments). g Measurement of mitochondrial Ca2+ level in Pdk4+/+ and Pdk4−/− primary mouse hepatocytes transduced with 4mitD3 adenovirus and treated with or without EtOH for 24 h ($$n = 8$$ biological replicates from three independent experiments). h, i Mitochondria ROS (mean fluorescence intensity; MFI) ($$n = 6$$ biological replicates) (h) and membrane potential ($$n = 18$$ biological replicates) (i) were measured using MitoSOX and the JC1 dye, respectively, in Pdk4+/+ and Pdk4−/− primary mouse hepatocytes treated with EtOH for 24 h (three independent experiments were performed). j Oxygen consumption rate (OCR) was measured by XF-analyzer. Rot: Rotenone, AA: Antimycin A. k Quantification of (j) ($$n = 13$$ biological replicates from three independent experiments). All quantifications are represented as mean ± SEM, *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; ****$p \leq 0.0001$ (Two-way ANOVA, Tukey’s multiple comparisons test).
## PDK4 deficiency suppresses MAM formation in ED-fed mice
Next, to verify our findings in vivo, we challenged wildtype (Pdk4+/+) and global Pdk4 knockout (Pdk4−/−) mice with either CD or ED, as depicted in Fig. 1a. No difference was observed in the body weight, daily food consumption and liver weight between Pdk4+/+ and Pdk4−/− mice fed with CD or ED (Supplementary Fig. 6a–c). We found that ED-induced PDK4 upregulation was accompanied by increased GRP75 phosphorylation but this was markedly reduced in ED-fed Pdk4−/− mice (Supplementary Fig. 6d, e and Fig. 6a). Unexpectedly, PDK4 deficiency did not prevent ED-induced PDH phosphorylation (s300), which might be explained via increased PDK2 expression observed in both ED-fed Pdk4+/+ and Pdk4−/− mice (Supplementary Fig. 6d, f, g). Next, we evaluated the alterations in MCC complex proteins expression in MAM fractions isolated from CD or ED-fed Pdk4+/+ and Pdk4-/- mice liver. PDK4 and other MCC complex proteins, as expected, were increased in the MAM fraction of ED-fed Pdk4+/+ mice compared to the CD-fed controls, but not in the ED-fed Pdk4−/− mice (Fig. 6b–e). However, this effect of PDK4 deficiency was not contributed by alterations in the total expression of MCC complex proteins (Supplementary Fig. 6h–k). Furthermore, TEM analysis of perivenous hepatocytes revealed the ED-induced increase in MAM length was significantly reduced in ED-fed Pdk4−/− mice (Fig. 6f, g). Lastly, we evaluated the effect of ED on mitochondrial DNA content, the protein levels of mitochondrial OXPHOS complex and antioxidant enzymes. No significant difference in mitochondrial DNA content (Supplementary fig. 7a) and mitochondrial OXPHOS protein expression was observed in EtOH-treated hepatocytes or ED-fed mice liver compared to controls, except for Complex IV [cytochrome c oxidase subunit I (MT-CO1)] which was increased in ED-fed Pdk4+/+ mice but not in ED-fed Pdk4−/− mice (Supplementary Fig. 7b, c). Previously, MT-CO1 overexpression was shown to promote ROS formation35. Interestingly, in line with the observation in vitro (Supplementary Fig. 5d), among the antioxidant enzymes, GPX4 protein expression was significantly reduced in ED-fed Pdk4+/+ mice compared to CD-fed Pdk4+/+ mice but not in ED-fed Pdk4−/− mice (Supplementary fig. 7b, d–f), indicating that PDK4 deficiency may prevent alcohol-induced GPX4 depletion. Together, these results demonstrate that PDK4 deficiency suppresses alcohol-induced MAM formation in vivo. Fig. 6Genetic ablation of PDK4 prevents alcohol-induced MAM formation in vivo.a GRP75 phosphorylation in CD/ED-fed mice liver isolated from Pdk4+/+ and Pdk4−/− mice was evaluated by IP with p-S/T antibody (representative blot of $$n = 3$$ mice/group). Quantifications relative to control are provided below each blot. b–e *Immunoblot analysis* of the MCC complex proteins in MAM fractions isolated from CD/ED-fed Pdk4+/+ and Pdk4−/− mice livers (b), and quantifications (c–e) (CD Pdk4+/+/Pdk4−/−, $$n = 2$$; ED Pdk4+/+/Pdk4−/−, $$n = 3$$ mice/group). f MAM formation in the perivenous region of CD/ED-fed Pdk4+/+ and Pdk4−/− mice liver sections were analyzed by TEM imaging [Scale bars, 2 µm (top panel) and 1 µm (magnified)]. The ER and mitochondria in the TEM images were reconstructed graphically to visualize MAM formation. Mito: Mitochondria (red), ER: Endoplasmic Reticulum (green), Nucleus (blue). g Quantification of MAM length to mitochondrial perimeter ratio, CD Pdk4+/+, $$n = 8$$ (with 250 mitochondria); CD Pdk4−/−, $$n = 14$$ (with 670 mitochondria); ED Pdk4+/+, $$n = 22$$ (with 990 mitochondria); EtOH Pdk4−/−, $$n = 25$$ (with 800 mitochondria) microscopic fields from 3 mice/group. All quantifications are represented as mean ± SEM, *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$ (Two-way ANOVA, Tukey’s multiple comparisons test).
## PDK4 deficiency ameliorates alcohol-induced liver injury by suppressing MAM formation
Next, we sought to examine whether PDK4 deficiency could ameliorate alcohol-induced liver injury. To this end, we examine the serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, indicators of hepatic injury. AST and ALT levels were markedly elevated in ED-fed Pdk4+/+ mice when compared to pair-fed Pdk4+/+ controls but significantly reduced in ED-fed Pdk4−/− mice (Fig. 7a, b). Furthermore, ED-induced hepatic lipid accumulation (Fig. 7c) and triglyceride content were significantly decreased in PDK4 deficient mice (Fig. 7d), indicating that genetic ablation of PDK4 protects against the development of alcohol-induced liver steatosis. Fig. 7Whole-body or hepatocyte-specific deletion of PDK4 alleviates alcohol-induced liver injury and is reversed by induction of MAM formation using Linker.a, b Serum AST (a) and ALT (b) levels of CD/ED-fed Pdk4+/+ and Pdk4−/− mice (CD Pdk4+/+/Pdk4−/−, $$n = 5$$; ED Pdk4+/+/Pdk4-/-, $$n = 10$$ mice/group). c, d Examination of lipid accumulation and histology by Oil-Red-O (top) and H&E (bottom) staining, respectively (c) (Scale bars, 50 µm), and triglyceride (TG) content (d) in CD/ED-fed Pdk4+/+ and Pdk4−/− mice liver (CD Pdk4+/+/Pdk4−/−, $$n = 5$$; ED Pdk4+/+/Pdk4−/−, $$n = 10$$ mice/group). e, f Serum AST (e) and ALT (f) levels of CD/ED-fed Pdk4f/f and Pdk4Δhep mice (CD Pdk4f/f/Pdk4Δhep, $$n = 5$$; ED Pdk4f/f/Pdk4Δhep, $$n = 6$$ mice/group). g, h Oil-Red-O (top) and H&E (bottom) staining (g) (Scale bars, 50 µm), and TG content in CD/ED-fed Pdk4f/f and Pdk4Δhep mice liver (h) (CD Pdk4f/f/Pdk4Δhep, $$n = 5$$; ED Pdk4f/f/Pdk4Δhep, $$n = 6$$ mice/group). i Depiction of forced induction of MAM formation using Linker. j Targeting of Linker-RFP at MAM was analyzed by confocal microscopy in AML12 cells expressing mito-BFP and Sec61B with empty pcDNA/Linker-RFP (Scale bars, 20 µm). k Graphical demonstration of Mock/*Linker adenovirus* (AdV) delivery to hepatic tissue via intravenous (IV) injection in Pdk4−/− mice during ED-diet feeding period. l TEM images of AdV-Mock/AdV-Linker injected ED-fed Pdk4−/− mice liver sections ($$n = 3$$ mice/group) (Scale bars, 1 µm). m, n Serum AST (m) and ALT (n) levels of AdV-Mock/AdV-Linker injected ED-fed Pdk4−/− mice (AdV-Mock, $$n = 3$$; AdV-Linker, $$n = 5$$ mice/group). o–q Oil-Red-O staining (o) (Scale bars, 50 µm), H&E staining (p) (Scale bars, 50 µm), and TG content (q) in AdV-Mock/AdV-Linker injected ED-fed Pdk4−/− mice liver (AdV-Mock, $$n = 3$$; AdV-Linker, $$n = 5$$ mice/group). All quantifications are represented as mean ± SEM, *$p \leq 0.05$; **$p \leq 0.01$; ****$p \leq 0.0001$ (Two-way ANOVA, Dunnett’s multiple comparisons test and two-tailed unpaired t-test).
To gain a better understanding of the cell-type-specific function of PDK4, we thus generated hepatocyte-specific Pdk4 knockout mice (Pdk4Δhep) by crossing Pdk4 floxed (Pdk4f/f) with albumin-cre mice (Supplementary Fig. 8a) and fed them with CD and ED. No significant difference was observed in body weight, average food intake, and liver weight between the groups (Supplementary Fig. 8b–d). As expected, hepatic expression of PDK4 was significantly increased in ED-fed Pdk4f/f mice (Supplementary Fig. 8e, f). In congruence with global Pdk4 deletion, ED-fed Pdk4Δhep mice showed a reduction in serum AST and ALT levels, accompanied by lowered hepatic lipid accumulation and triglyceride content when compared to ED-fed control mice (Fig. 7e–h). Importantly, in line with our prior observations in Pdk4−/− mice (Supplementary Fig. 6d, f, g), ED-induced PDK2 expression and PDH phosphorylation levels remained unaltered in ED-fed Pdk4Δhep mice (Supplementary Fig. 8e, g, h), suggesting that the effect of Pdk4 deficiency in hepatic tissue produced by the whole body and hepatocyte-specific Pdk4−/− mice are indistinguishable. These findings confirm that PDK4 plays a significant role in the pathogenesis of ALD and that loss of PDK4 abrogates alcohol-induced hepatic steatosis and injury. Based on these findings, we tested the effect of PDK4 inhibition on EtOH-induced MAM formation and mitochondrial dysfunction using dichloroacetic acid (DCA). DCA treatment led to a significant reduction in EtOH-induced MCC complex formation, accompanied by decreased PDK4 expression and GRP75 phosphorylation (Supplementary Fig. 9a–c). Moreover, EtOH-induced elevation of mitochondrial Ca2+ level, ROS generation and lipid accumulation were significantly decreased in DCA-treated cells (Supplementary Fig. 9d–g). These results suggest that inhibition of PDK4 might be a viable therapeutic route to treat ALD.
Finally, to understand the critical role of MAM formation in the pathogenesis of ALD, we overexpressed an ER-mitochondrial linker to enhance MAM formation and examine whether forced MAM formation could reverse the protective effect of PDK4 deficiency in ALD. Linker specificity was validated by confirming the localization of RFP at the MAM interface in AML12 cells labeled with mito-BFP and SEC61B-tagged GFP (Fig. 7i, j). Adenovirus constructs for mock control (AdV-Mock) and linker (AdV-Linker) were injected on the 6th day of ED feeding in Pdk4−/− mice by tail vein injection (Fig. 7k). Using this approach, we observed an increase in MAM formation in ED-fed Pdk4−/−, confirmed by TEM imaging (Fig. 7l). No difference was observed in body weight and liver weight between the groups (Supplementary Fig. 10a, b). Linker introduction led to a significant increase in serum transaminase, notably ALT (Fig. 7m, n) and worsened hepatic steatosis in ED-fed Pdk4−/− mice (Fig. 7o–q), indicating a reversal of the phenotype upon induction of MAM formation. However, in line with a previous report18, the administration of linker in absence of ED, despite having enhanced MAM formation failed to induce hepatic steatosis, gross morphological alteration, and TG accumulation (Supplementary Fig. 10c–f). This indicates that ectopic enhancement of MAM formation, at least in this experimental setting, is not sufficient to promote hepatic steatosis but requires a pathological trigger (in this case, ED) to promote hepatic steatosis. Taken together, our data indicate that PDK4-mediated MAM formation contributes to the pathogenesis of ALD (Fig. 8).Fig. 8Graphical illustration of PDK4-mediated Ca2+-channeling complex formation at the ER-mitochondria contact site promotes mitochondrial dysfunction in alcohol-associated liver disease. Ca2+-channeling complex formation in MAM is augmented in alcohol-associated liver disease (ALD). An increase in PDK4 expression enhances the MAM Ca2+-channeling complex formation via the phosphorylation of GRP75 to promote mitochondrial Ca2+ accumulation and dysfunction in ALD. ROS: Reactive Oxygen Species, OCR: Oxygen Consumption Rate, MMP: Mitochondrial Membrane Potential.
## Discussion
In the liver, almost all mitochondria are found to be in close contact with the ER within a distance of 100 nm also known as wrappER-associated mitochondria36, whereas <50 nm distance between ER and mitochondria is the threshold for this contact to be considered as MAM13. MAM is a specialized contact site which plays an essential role in lipid synthesis, metabolite exchange, and Ca2+ transport to regulate mitochondrial function13,14, and it is dynamically regulated by the change in nutritional status of the cells36. An aberrant increase in Ca2+ transport via MAM disturbs mitochondrial homeostasis and promotes mitochondrial dysfunction18,28. Dysregulation of mitochondrial Ca2+ homeostasis causing mitochondrial dysfunction has been reported in ALD10,11,37, however, the detailed mechanism underlying these findings remained unclear. GRP75 plays a critical role in the formation of MAM and also facilitates Ca2+ transport by binding with IP3R1 and VDAC116,17. The importance of GRP75 in regulating Ca2+ transport between ER and mitochondria via the MCC complex is well established16. However, upstream regulation of the GRP75-mediated MCC complex formation has not been fully elucidated. We have previously demonstrated that PDK4 resides in the MAM and participates in the regulation of MCC complex formation24. This study provides evidence that PDK4 induces mitochondrial dysfunction in ALD by modulating MCC complex formation.
We demonstrated here that alcohol promotes MAM formation both in vivo and in vitro. In addition, we observed that an increase in the MAM formation is accompanied by enhanced MCC complex formation in both human and murine models of ALD. In depth transcriptomic analysis of MAM-associated genes revealed that PDK4 is highly elevated in the hepatic tissues of ED-fed mice and patients with ALD. Interestingly, transcriptional regulators of PDK4, namely, CCAAT enhancer binding protein beta (C/EBPβ)38, and estrogen-related receptor γ (ERRγ)39 were also shown to be upregulated in ALD40,41, suggesting a potential involvement of these transcription factors in EtOH-mediated induction of PDK4 transcript level. Further investigation on the regulatory role of PDK4 on MCC complex formation identified GRP75 as the substrate for the modulation of the MCC complex. We found that PDK4 phosphorylates GRP75 at multiple sites, T120, S266, and T267. Using non-phosphorylatable mutants of these phosphorylation sites, we uncovered the mediatory role of PDK4 on alcohol-induced MCC complex formation and mitochondrial Ca2+ accumulation. Of note, we observed that, unlike EtOH treatment, PDK4 overexpression alone failed to affect basal mitochondrial Ca2+ levels in WT GRP75-expressing cells. However, upon stimulation of IP3R-mediated Ca2+ release by ATP, both PDK4 overexpression and EtOH treatment strongly induced mitochondrial Ca2+ uptake in WT GRP75-expressing cells, suggesting that PDK4 primes MCC complex formation but does not modulate ER Ca2+ release at the basal state. Ca2+ homeostasis plays a crucial role in maintaining cellular physiology; an excess level of Ca2+ can activate ROS-generating enzymes leading to a deterioration of mitochondrial function31. We found that EtOH-induced mitochondrial Ca2+ accumulation is accompanied by enhanced ROS formation. However, suppression of MCC complex formation by genetic ablation of PDK4 or the non-phosphorylatable mutants of GRP75 (120 A, 266 A, and 267 A) significantly reduced mitochondrial Ca2+ levels and ROS formation. These data are in line with the previous findings that alcohol-induced mitochondrial Ca2+ accumulation causes mitochondrial oxidative stress10,12. Our findings indicate that PDK4-mediated MCC complex formation initiates alcohol-induced mitochondrial Ca2+ accumulation.
Mitochondria undergo rapid remodeling in response to stress as a part of the adaptive process; a prolonged mitochondrial stress with a compromised antioxidant system contributes to liver disease7. In congruence with the previous reports37, we observed a decrease in mitochondrial respiration, MMP, and increased ROS formation which was accompanied by depletion of GPX4 protein level in EtOH-treated hepatocytes. Similarly, a significant reduction in GPX4 in ED-fed was observed compared to CD-fed control. However, this effect of EtOH was reversed upon genetic ablation of PDK4. These observations suggest that PDK4 may play a key role in driving the mitochondrial adaptive phase towards the maladaptive phase in ALD. Additionally, disruption in several cellular defense mechanisms, including autophagy and the redox system, have been implicated in the pathogenesis of ALD42. Mitochondrial quality control (MQC) is closely interlinked with these processes and plays a critical role in maintaining cellular homeostasis7. MAM plays a significant role in MQC by regulating mitochondrial fission43. Additionally, alcohol induces mitochondrial fission in hepatocytes;37 in line with our observation, we found that ED reduced mitochondrial perimeter in the hepatocytes of the perivenous region, the primary site affected by alcohol26. We have recently demonstrated that PDK4 promotes stress-induced mitochondrial fission44. Together, these evidences suggest that PDK4 may play a role in mitochondrial fission in coordination with its role in MAM formation in ALD. Of note, in contrast to perivenous hepatocytes, the mitochondrial perimeter was increased in the periportal hepatocytes of ED-fed mice which correlates with an earlier report suggesting that alcohol promotes the formation of megamitochondria45. These findings suggest that the effect of alcohol on mitochondrial shape and sizes may vary in different zones of the liver but how hepatocytes in different zones react differently in response to alcohol needs further investigation. In this study, we have addressed how PDK4-mediated enhanced MCC-complex formation can play a part in promoting mitochondrial dysfunction in ALD. However, how PDK4 can affect autophagy or cellular redox system is currently unclear. Future studies are warranted to explore the role of PDK4 and MAM formation in these processes during the early adaptive phase of EtOH challenge as well as in chronic alcohol-induced liver injury.
Alcohol-induced mitochondrial dysfunction increases intra-hepatic lipid contents, via the inhibition of mitochondrial fatty acid oxidation32, and the improvement in the mitochondrial function alleviates lipid accumulation37,46. PDK4 deficiency has been shown to suppress hepatic lipogenesis in non-alcoholic steatohepatitis21,23. Furthermore, overexpression of GRP75 promotes mitochondrial stress and lipid accumulation in hepatocytes47. We observed that alcohol-induced MCC complex formation and mitochondrial dysfunction were associated with intra-hepatic lipid accumulation in WT GRP75 but significantly less in GRP75 mutants expressing cells, suggesting a crucial role of PDK4 in the hepatocytes in the pathogenesis of ALD via regulating GRP75-mediated MCC complex formation. To determine the functional implications of PDK4 in ALD pathogenesis, we employed two different PDK4 knockout models, a whole-body KO (Pdk4−/−) and a hepatocyte-specific KO (Pdk4Δhep). We found that both Pdk4−/− and Pdk4Δhep prevented alcohol-induced liver injury via the suppression of MAM. Additionally, a causal role of PDK4 on MAM-mediated hepatic steatosis in ALD is supported by the evidence that the forced induction of MAM formation abrogated the protective effect of PDK4 deficiency in alcohol-induced hepatic steatosis. Recent reports revealed that multiple proteins involved in lipid droplet biosynthesis were found to be localized in MAM48–50. Therefore, the contribution of MAM formation in the context of lipogenesis in ALD warrants further investigation. Together, these findings indicate a critical role of the MAM in orchestrating mitochondrial dysfunction that induces liver injury and lipid accumulation during ALD pathogenesis. Of note, induction of PDK4 promotes fatty acid oxidation51 and glycolysis52 via inhibitory phosphorylation of PDH. Surprisingly, we found that PDK4 deficiency failed to suppress ED-induced PDH phosphorylation. This may be due to an increase in PDK2 expression observed in both ED-fed Pdk4+/+ and ED-fed Pdk4−/− mice, as PDH is a common substrate of all PDK isoforms. Our results suggest that the protective effect of PDK4 deficiency on ED-induced mitochondria dysfunction, to a significant extent, is independent of the canonical function of PDK4. The direct involvement of MAM in regulating insulin signaling, mitochondrial dynamics, autophagy, inflammation, apoptosis and senescence pathways are well evident15,53,54. Interestingly, all these pathways were shown to play a role in the progression of ALD37,55,56. Collectively, these findings suggest that alcohol-induced MAM formation may not be limited to induction of mitochondrial dysfunction but it may also play a role in dictating other stress signaling pathways in ALD which requires further investigation.
There is, however, a major limitation in this study. We have predominantly verified our current hypothesis in male mouse model of ALD. Although this does not rule out the applicability of the mechanistic conclusions in female models, however, generalization outside the male models needs to be investigated in-depth to establish that the effect of PDK4 in alcohol-induced liver injury shows no prominent sexual dimorphism. In summary, we conclude that PDK4 is a key mediator of alcohol-induced liver injury, based on the following evidence. First, PDK4 deficiency prevents MAM formation in ED-fed mice. Second, the suppression of MAM formation in the absence of PDK4 prevents alcohol-induced mitochondrial Ca2+ accumulation, mitochondrial dysfunction, and intracellular lipid accumulation. Third, the induction of ER-mitochondrial interaction in Pdk4−/− mice with a linker abrogated the protective effect on alcohol-induced liver injury. Together, our present study defines a causal role of PDK4 in the pathogenesis of ALD by mediating the MAM formation and that PDK4 is a potential therapeutic target for ALD.
## Animal experiment
All experiments were conducted according to the guidelines established and approved by the Institutional Animal Care and Use Committee of Kyungpook National University and Indiana University. Wild-type (Pdk4+/+), Pdk4 knockout (Pdk4−/−) and hepatocyte-specific Pdk4 knockout (Pdk4HepΔ) mice (generated by crossing Pdk4 flox mice with Albumin-Cre mice provided by Dr. Chul-Ho Lee, Korea research institute of bioscience and biotechnology, Daejeon, South Korea) of C57BL/6 J background were housed in a pathogens-free facility and maintained in standard housing conditions on a light/dark 12-h cycle at 22 ± 2 °C.
Previous studies have reported that alcohol consumption causes hypothermia in human57 and rodents maintained at 20~22 °C temperature58,59. In addition, alcohol feeding was shown to affect brown adipose tissue mass60 and inter-organ cross talk influencing liver function61,62. Mice housed and maintained at thermoneutral conditions show greater non-alcoholic fatty liver disease and provide the best way to investigate the cellular and molecular mechanisms underpinning alcohol liver disease pathogenesis63. Therefore, to rule out the influence of body temperature difference between control and alcohol-fed mice on liver function and its possible confounding effects on mitochondrial morphology and function, in this study, we induced ALD in mice under thermo-neutral conditions, i.e. 30 oC to investigate the potential role of MAM in alcohol-induced mitochondrial dysfunction.
All the experiments were performed in 10–12 weeks old male mice. ALD was induced in mice as described earlier25 During the experiment, the mice were maintained in a 12 h light/dark cycle at 30 °C, and food consumption, body weight was monitored daily. Mice of the same age were acclimatized for 5 days with a control Lieber-DeCarli liquid diet (F1259SP, Bio-Serv) ad libitum. Then, the mice were fed with an ethanol diet (F1258SP, Bio-Serv) containing $5\%$ (v/v) ethanol for 10 days. On the 11th day, each mouse received a single dose of Maltose Dextrin (3585; Bio-Serv) or $31.5\%$ (v/v) ethanol (2701; Decon Labs) via oral gavage and was euthanized 8 hours (h) later. Collected serum and liver tissue samples were stored at −80 °C for analysis. For adenoviral delivery of ER-mitochondria linker in the liver, 1 × 109 plaque-forming units (PFU) mock/*Linker adenovirus* per mouse were injected intravenously. The ER–mitochondria linker tagged with red fluorescence protein (Linker-RFP) which would provide a 15~20 nm space between ER and mitochondrial outer membrane was provided by Dr. Hyun-Woo Rhee (Seoul National University, Seoul, South Korea).
## Human tissue sample
The use of human liver tissues of healthy controls and patients with ALD were approved by the Indiana University Institutional Review board under the study protocol 1712511018 and 1011004278. Written informed consent was obtained from all study participants.
## Healthy controls
We obtained the liver tissues samples from the IRB-approved protocol at the Indiana University, Department of Surgery at Johns Hopkins Hospital (supported by the NIAAA, R24AA025017, Clinical resources for alcoholic hepatitis investigators), and Department of Pathology and Laboratory Medicine, Indiana University School of Medicine.
## Alcohol-associated liver disease samples
Liver tissue samples from patients with alcohol-associated liver disease (alcoholic hepatitis) were provided by the Department of Surgery at Johns Hopkins Hospital. Baseline clinical and demographic data of alcohol-associated liver disease patients are provided in the Supplementary Table 2.
## Cell culture
Murine hepatocyte cell line, AML12 (CRL-2254; ATCC) was cultured in DMEM/F12 medium (11330-032; Gibco) supplemented with insulin-transferrin-selenium (41400-045; Gibco), 40 ng/mL dexamethasone (D1756; Sigma), $10\%$ FBS (16000-044; Gibco) and 1X antibiotics (15140-122; Gibco) and all the experiments were performed between 4-20 passage number. Primary mouse hepatocytes were isolated from 8–10-week-old Pdk4+/+ and Pdk4−/− mice by the collagenase perfusion method described previously64 and cultured in M199 media (M4530; Sigma) supplemented with 23 mM HEPES (15630-080; Gibco), 10 nM dexamethasone (D1756; Sigma), $10\%$ FBS (16000-044; Gibco) and 1X antibiotics. 24 h after plating, cells were treated with 100 mM Ethanol (459836; Sigma-Aldrich) in $0.5\%$ FBS containing media for 24 h (ethanol-containing media was refreshed every 12 h). 2 mM Dichloroacetic acid (DCA) (347795; Sigma) was co-treated with EtOH for 24 h.
## Quantitative real-time PCR (qPCR)
Total RNA was isolated from tissues using QIAzol lysis reagent (79306; Qiagen) as described in the manufacturer’s protocol. cDNA was synthesized from 2 µg of total RNA using a cDNA synthesis kit (K1622; Thermofisher Scientific). qPCR was carried out using qPCR Master Mix (M3003L; New England Biolabs) in ViiA 7 Real-Time PCR system (Applied Biosystems). 36B4 or GAPDH was used for normalization. The primer sequences used in the study are listed in the Supplementary Table 3.
## Plasmid vector constructs and siRNA transfection
siRNAs targeting mouse Pdk4 (Cat no. 1406883) [Sense 5'-CUCUACUCUAUGUCAGGUU(dTdT)−3'; Antisense 5'-AACCUGACAUAGAGUAGAG(dTdT)−3'], Itpr1 (Cat no. 1374716) [Sense 5'-CUGUAUGCGGAGGGAUCUA(dTdT)−3'; Antisense 5'-UAGAUCCCUCCGCAUACAG(dTdT)−3'], and Vdac1 (Cat no. 22333-1) [Sense 5'-GACGGAUGAAUUCCAGCUU = tt(1-AS)−3'; Antisense 5'-AAGCUGGAAUUCAUCCGUC = tt(1-AA)−3'] and siControl (#SN-1003) were purchased from Bioneer (Daejeon, South Korea). Mitochondria-targeted blue fluorescence protein (mito-BFP) and ER-targeted Sec61B-GFP were provided by Dr. Hyun-Woo Rhee (Seoul National University, Seoul, South Korea). PDK4 with c-term FLAG tagged in pcDNA3 vector was provided by Dr. Robert A. Harris (Indiana University, IN, US). Human GRP75 with c-term hemagglutinin (HA) tagged plasmid construct (HG16926-CY; Sino Biological) was used for generating non-phosphorylatable GRP75 mutants (T120-A, S266-A, and T267-A) using the QuickChange lightning site-directed mutagenesis kit (210518-5; Agilent technologies) and the mutations were confirmed by DNA sequencing. For transfection, cells were transfected with 100 nmol/L siRNA and plasmid DNA using RNAi Max reagent (13778150; Thermofisher Scientific) and Lipofectamine 2000 (11668019; Thermo Fisher Scientific), respectively, for 48 h and treated with 100 mM EtOH for 24 h.
## Co-immunoprecipitation (co-IP) and immunoblotting
Cells/tissues were lysed in RIPA lysis buffer with protease (04693132001; Roche) and phosphatase inhibitors (P0044; Sigma-Aldrich). Protein concentration was determined using BCA protein assay kit (23225; Thermofisher Scientific). For co-IP, cells/tissues were lysed in lysis buffer (50 mM Tris, 150 mM NaCl, $1\%$ Triton X-100, 1 mM EDTA, and $10\%$ glycerol) and 500 ug protein/sample was incubated with the indicated primary antibody for 3 h. A/G plus agarose beads (2003; Santa Cruz Biotechnology) were added and incubated overnight at 4 °C. The protein-bound beads were washed and boiled with SDS-PAGE sample buffer. Protein samples were then loaded, separated by SDS-PAGE, and transferred to a PVDF membrane (IPVH00010; Millipore). Membranes were incubated with indicated primary antibodies followed by incubation with horseradish peroxidase-linked secondary antibodies (anti-rabbit; Cell signaling, 7074 S or anti-mouse; R&D systems, HAF007; 1:2000 dilution) and detected using the iBright CL1500 Imaging System (Thermofisher Scientific). Primary antibodies used in the study are listed in the Supplementary Table 4.
## Subcellular fractionation
Subcellular fractions were performed in the freshly isolated liver tissue as previously described65. Briefly, minced tissue was homogenized with a Dounce homogenizer in Buffer 1 (225-mM mannitol, 75-mM sucrose, $0.5\%$ BSA, 0.5-mM EGTA and 30-mM Tris–HCl pH 7.4) and the homogenate was centrifuged at 740 g for 5 min. The supernatant was separated into crude mitochondrial pellet and ER containing supernatant by centrifugation at 9000 g for 10 min. The supernatant was collected for ER isolation, whereas, the crude mitochondrial pellet was resuspended in buffer 2 (225-mM mannitol, 75-mM sucrose, $0.5\%$ BSA and 30-mM Tris–HCl pH 7.4), and centrifuged at 10,000 g for 10 min. The pellet was resuspended in Buffer 3 (225-mM mannitol, 75-mM sucrose and 30-mM Tris–HCl pH 7.4), centrifuged at 10,000 g for 10 min, and the crude mitochondrial pellet was resuspended in the mitochondrial resuspension buffer (MRB) (250-mM mannitol, 5-mM HEPES; pH 7.4 and 0.5-mM EGTA). Mitochondria and MAM were separated with a percoll medium (225-mM mannitol, 25-mM HEPES; pH 7.4, 1-mM EGTA and $30\%$ Percoll; vol/vol) from the crude mitochondria by ultra-centrifugation at 95,000 g for 30 min. MAM layer and mitochondrial pellet was collected and resuspended in MRB buffer. MAM and mitochondrial suspensions were centrifuged at 6300 g for 10 min. Pure mitochondrial pellet was collected, and MAM fraction was collected after pelleting by ultra-centrifugation at 100,000 g for 1 h. For ER isolation, the supernatant was centrifuge at 20,000 g for 30 min followed by ultracentrifugation at 100,000 g for 1 h. Isolated subcellular fraction was resuspended with lysis buffer containing protease and phosphatase inhibitors. Protein samples were resolved by Tris-glycine SDS PAGE and analyzed by immunoblot.
## Transmission electron microscopy (TEM)
Liver tissue sections were fixed with the fixative solution containing $2\%$ paraformaldehyde and $2.5\%$ glutaraldehyde, and then post fixed with $1\%$ osmium tetroxide (OsO4) in ice for 2 h. The tissues were then washed with PBS and dehydrated in ethanol and propylene oxide series, embedded in Epon 812 mixture, and polymerized in an oven at 70 °C for 24 h. The sections acquired from polymerized blocks were collected on grids, counterstained with uranyl acetate and lead citrate, and examined with Bio-HVEM system (JEM-1000BEF at 1000 kV, JEOL, JAPAN) and Bio-TEM system (JEM-1400Plus at 120 kV, JEOL, JAPAN and Tecnai G2 at 120 kV, Thermo Fisher Scientific, Waltham, MA, USA). To map the perivenous and periportal region in the liver sections, a combination of low- and high-magnification EM imaging ranging from 38X to 5000X was applied to precisely locate the hepatocyte population in the perivenous and periportal region (within 50 µm from the central vein or portal vein border) as demonstrated in the Supplementary Fig. 1a. The architecture of mitochondria and ER in the EM images were reconstructed graphically using GIMP software (GNU image manipulation program, Gimp 2.10), and analyzed using ImageJ (NIH, Bethesda, MD, USA).
## Immunofluorescence
Cells were seeded on 12 mm glass coverslips in 24 well plates. At the end of the experiment, cells were washed with PBS, fixed with $4\%$ paraformaldehyde, permeabilized ($0.2\%$ Triton X, 0.1 M glycine in PBS), and incubated with primary antibodies overnight at 4 °C. Next, the cells were washed, incubated with the secondary antibodies conjugated with fluorophores Alexa Fluor 488 (Invitrogen, A11001; 1:100 dilution)/Alexa Fluor 568 (Invitrogen, A11011; 1:100 dilution), and mounted using VECTASHIELD mounting medium containing DAPI (H1200). Images were captured using Olympus FluoView FV1000 confocal microscope (Olympus Imaging, PA, USA). Pearson co-efficient colocalization of ER and mitochondria was analyzed by ImageJ software with Fiji plugin (NIH, Bethesda, MD, USA). Primary antibodies used in immunofluorescence experiments were anti-rabbit TOM20 (Santa Cruz, sc-11415; 1:100 dilution) and anti-mouse monoclonal PDI (Abcam, ab2792; Clone RL90; 1:100 dilution).
## In situ Proximity ligation assay (PLA)
Protein-protein interactions were visualized using Duolink In Situ detection kit (DUO$\frac{92002}{4}$/7; Sigma-Aldrich) by following the manufacturer’s protocol. Briefly, cells were cultured on 12 mm glass coverslips in 24 well plates. At the end of the experiment, cells were washed with PBS, fixed with $4\%$ paraformaldehyde, and permeabilized ($0.2\%$ Triton X, 0.1 M glycine in PBS). Next, the cells were incubated with blocking solution, followed by overnight incubation with primary antibodies. The cells were then washed, probed with the secondary antibodies conjugated with oligonucleotide, ligated, and amplified. Preparations are then mounted using Duolink II mounting medium containing DAPI (DUO82040; Sigma-Aldrich), and the fluorescence signals were analyzed by ImageXpress Micro confocal and MetaXpress software (Molecular devices). Primary antibodies used in in situ PLA experiments were anti-rabbit VDAC1 (Abcam, ab15895; 1:100 dilution), anti-mouse monoclonal IP3R1 (Santa Cruz; sc-271197; Clone E8; 1:50 dilution), anti-mouse monoclonal GRP75 (Santa Cruz, sc133137; Clone D9; 1:50 dilution), anti-rabbit IP3R1 (Invitrogen, PA1-901; 1:100 dilution), anti-rabbit FLAG (Cell signaling, #2368; 1:100 dilution) and anti-mouse HA (Santa Cruz, sc7392; Clone F-7; 1:50 dilution).
## In vitro kinase assay
In vitro kinase assay to detect protein phosphorylation was analyzed as previously described66. Briefly, PDK4 (provided by Dr. Nam-Ho Jeoung, Catholic University, Daegu, South Korea) and GRP75 (NBC1-18380; Novus biologicals) human recombinant proteins were mixed in 1:50 ratio in 1X kinase buffer (9802 S; Cell signaling) and incubated for 30 min at 30 oC in presence of 500uM ATP (A2383; Sigma). The reaction was terminated by the addition of 1X sample buffer and boiled. The samples are then analyzed by SDS-PAGE and immunoblotting.
## LC-MS/MS analysis
LC-MS/MS analysis was performed after de-stainning and excision of the target protein band from the Coomassie stained SDS-PAGE gel as follows. In short, the gel was sectioned into 10 mm sections, and in-gel digested with trypsin. The tryptic digest was separated through online reversed-phase chromatography using a reversed-phase peptide trap occlusion PepMap™100 (internal diameter, 2 cm length) and a reversed-phase analysis column PepMap™RSLCC,1875 (75 μm inner diameter, 15 cm length, 3 μm particle size), both from Thermo Scientific, followed by electrospray ionization at a flow rate of 300 nl min-1. Samples were eluted with a split gradient of 3-$50\%$ solution B ($80\%$ ACN with $0.1\%$ FA) for 60 min and 50-$80\%$ solution B for 10 min, and the columns were washed in $100\%$ solution B for 10 min. The chromatography system coupled in line with an Orbitrap Fusion Lumos mass spectrometer was operated in a data dependent mode with the 120,000 resolution MS1 scan (375-1500 m/z), an AGC target of 5e5, and a maximum injection time of 50 ms. Peptides above the threshold of 5e3 and a charge of 2-7 were selected for fragmentation through dynamic exclusion after 15 second and 10 ppm tolerance.
DATABASE SEARCHING- Charge state deconvolution and deisotoping were not performed. All MS/MS samples were analyzed using Sequest (Thermo Fisher Scientific, San Jose, CA, USA; version IseNode in Proteome Discoverer 2.4.1.15) and X! Tandem (The GPM, thegpm.org; version X! Tandem Alanine (2017.2.1.4)). Sequest was set up to search Uniprot-human.fasta (unknown version, 42230 entries) assuming the digestion enzyme trypsin. X! Tandem was set up to search a reverse concatenated subset of the Uniprot-human database (unknown version, 54442 entries) (only “Mudpit_GRP75_1: GRP75_1”) also assuming trypsin. Sequest and X! Tandem were searched with a fragment ion mass tolerance of 0.60 Da and a parent ion tolerance of 10.0 PPM. Carbamidomethyl of cysteine was specified in Sequest and X! Tandem as a fixed modification. Met-loss of methionine, glu->pyro-Glu of the n-terminus, ammonia-loss of the n-terminus, gln->pyro-Glu of the n-terminus, deamidated of asparagine, oxidation of methionine, acetyl of lysine and the n-terminus and phospho of serine were specified in X! Tandem as variable modifications. Met-loss of methionine, met-loss+Acetyl of methionine, deamidated of asparagine, oxidation of methionine, acetyl of lysine and the n-terminus, phospho of serine and GG of lysine were specified in Sequest as variable modifications.
CRITERIA FOR PROTEIN IDENTIFICATION- Scaffold (version Scaffold_4.11.0, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at >$95.0\%$ probability. Peptide Probabilities from X! Tandem were assigned by the Scaffold Local FDR algorithm. Peptide Probabilities from Sequest were assigned by the Peptide Prophet algorithm67 with Scaffold delta-mass correction. Protein identifications were accepted if they could be established at >$99.0\%$ probability and contained at least two identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm68. Proteins that contained similar peptides, and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.
## Subcellular Ca2+ measurements
AML12 cells stably expressing mitochondrial targeted ratiometric calcium sensor, 4mitD3-CPV69 or primary hepatocyte were plated in a black, clear bottom 96 well plate (3603; Costar), and transduced with 4mitD3-CPV adenovirus for 24 h. The cells are then washed, and replaced the medium with Ca2+-free Krebs-Ringer bicarbonate (KRB) buffer. Fluorescence signals were monitored using two emission filters, YFP (540 nm) and CFP (490 nm) by ImageXpress Micro confocal (Molecular Devices). Fluorescence images was recorded every 10 s, and 90 s later, 100 µM (final concentration) ATP was injected. YFP and CFP fluorescence ratio were quantified using MetaXpress software (Molecular devices). 4mitD3-CPV plasmid used for stable cell line preparation, and adenoviral construct was kindly provided by Dr. Kyu-Sang Park (Yonsei University Wonju College of Medicine, Wonju, South Korea). Cytosolic Ca2+ levels were measured using the Fura-2 QBT™ Calcium Kit (#R6139; Molecular Devices) as described in the manufacturer’s protocol. Briefly, cells were loaded with Fura-2 for 1 h and excited at 340 nm and 380 nm, with emission at 510 nm using FlexStation3 (Molecular Devices). Fluorescence signals were measured every 10 s and 60 s later, 100 µM ATP was injected.
## Mitochondrial oxygen consumption rate (OCR) measurement
Cells were seeded in Seahorse XF96 plate overnight, and treated with/without 100 mM EtOH for 24 h. Before OCR measurement, cells were washed and equilibrated for 1 h at 37 °C in XF base medium (102353-100; Agilent technologies) supplemented with 1X GlutaMAX (35050; Gibco), 1 mM sodium pyruvate (S8636; Sigma-Aldrich), and 25 mM glucose (G7528; Sigma-Aldrich) (pH 7.4). Pre-hydrated sensor cartridge was loaded with mitochondrial inhibitors to deliver a final concentration of 1 μM oligomycin (75351; Sigma-Aldrich), 1 μM FCCP (C2920; Sigma-Aldrich), and 0.5 μM rotenone (R8875; Sigma-Aldrich) with 0.5 μM Antimycin A (A8674; Sigma-Aldrich), and measured using Seahorse XFe96 analyzer (Agilent technologies). OCR values were normalized with protein concentration.
## Lipid-TOX staining
Cells were seeded in a black, clear bottom 96 well plate (3603; Costar). At the end of the experiment, cells were pre-stained with NucBlue (R37605; Thermofisher Scientific), washed (PBS), fixed ($4\%$ paraformaldehyde), and stained with LipidTOX neutral lipid stain (H34475; Life technologies corporation). Images were captured, and analyzed using ImageXpress Micro confocal and MetaXpress software (Molecular devices), respectively.
## Mitochondrial ROS and membrane potential measurement
Cells were plated in a black, clear bottom 96 well plate (3603; Costar). At the end of the experiment, cells were loaded with MitoSOX (M36008; Invitrogen) fluorescent dye to detect mitochondria superoxide or JC-1 (T3168; Invitrogen) fluorescent dye to detect mitochondrial membrane potential (MMP) for 10 min and 30 min, respectively, as described in the manufacturer’s instruction. The fluorescence intensity was measured using FlexStation3 (Molecular Devices) and the mean fluorescence intensity (MFI) was normalized with the total nuclei (NucBlue stained) count per well.
## Mitochondrial DNA content
Genomic DNA (gDNA) and mitochondrial DNA (mtDNA) were extracted from the liver tissues using DNeasy Kit (69504; Qiagen) as previously described70. Briefly, 2.5 ng DNA were used to quantify the mtDNA and normalized with the gDNA using mitochondrial ND1 primers (Forward: CCTATCACCCTTGCCATCAT; Reverse: GAGGCTGTTGCTTGTGTGAC) and nuclear Pecam1 primers (Forward: ATGGAAAGCCTGCCATCATG; Reverse: TCCTTGTTGTTCAGCATCAC) by qPCR.
## Biochemical analysis
Serum aspartate transaminase (AST) and alanine transaminase (ALT) levels were measured with an automatic biochemistry analyzer 7020 (Hitachi). Triglyceride (TG) content in the liver homogenate was measured using the TG Quantification Colorimetric Kit (K622-100; BioVision) by following the manufacturer’s instruction, and measured using micro-well spectrophotometer (Molecular Devices).
## Histological analysis
For immunohistochemistry, liver tissues were fixed with $4\%$ PFA, embedded in paraffin, and immunohistochemistry analysis was performed. In short, after heat-induced epitope retrieval, paraffin-embedded sections were incubated in $3\%$ H2O2, and blocked in $3\%$ normal serum buffer. Sections were incubated with PDK4 antibody (NBP1-54723; Novus) overnight at 4 °C. Vectastain Elite ABC Staining Kit and DAB Peroxidase Substrate Kit (Vector Laboratories, Burlingame, CA) were used to visualize the staining. Hematoxylin and eosin (H&E) staining were performed according to the standard procedures. For Oil Red O staining, cryosections of OCT-embedded liver tissue samples were fixed in $10\%$ formalin, stained with Oil Red O, washed with $60\%$ isopropanol, and analyzed using Olympus Slideview VS200.
## Statistics and reproducibility
Graphs were plotted, and statistical significance was analyzed using GraphPad Prism 8 (GraphPad Software, La Jolla, CA). Images presented in the figures are the representatives of three or more independent experiments/samples with similar results. Statistical significance analysis was performed by two-tailed unpaired t-test for comparisons between two groups and one-way or two-way analysis of variance (ANOVA) to compare more than two groups applied with Dunnett’s or Tukey’s multiple comparison test from three or more independent experiments as specified in each figure legend. Statistical analysis was considered significant at p-value < 0.05 and it is indicated by the following annotations: *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, and ****$p \leq 0.0001.$ The error bars are presented as mean ± standard mean error (SEM).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37214-4.
## Source data
Source Data
## Peer review information
Nature Communications thanks Béatrice Morio, Derick Han and Dorian Ziegler for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers
and potential targets
authors:
- Hind Alamro
- Maha A. Thafar
- Somayah Albaradei
- Takashi Gojobori
- Magbubah Essack
- Xin Gao
journal: Scientific Reports
year: 2023
pmcid: PMC10043000
doi: 10.1038/s41598-023-30904-5
license: CC BY 4.0
---
# Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets
## Abstract
We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that $70\%$ of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.
## Introduction
Alzheimer’s disease (AD) is the most common cause of dementia, and its prevalence increases with age1,2. Unfortunately, no approved drugs can prevent or delay AD progression despite the array of potential targets identified for AD treatment. Also, many potential therapies in clinical trials have failed, including the most promising Aβ-directed therapies3,4. The problem may be that although our knowledge of AD progression has grown, and we know the development of amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the brain are hallmarks of AD, the real cause of AD is still unclear5,6. Nonetheless, early detection of AD is key to its treatment. Thus, researcher efforts are also directed at detecting AD using artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and incorporating different types of data including but not limited to: neuroimaging data7, non-coding RNAs8,9, transcriptomic data10, miRNAs biomarker11, or other genome data12. Another direction that researchers paid more attention to is to repurpose approved drugs to treat AD13,14.
Despite the caveats in our understanding of AD pathogenesis, current knowledge has allowed us to identify potential biomarkers and several proteins that may serve as effective targets in counteracting AD. Many of these biomarkers and targets are now also first determined through in silico means to minimize the considerable investments in developing novel drugs. For example, Madar et al.15 used 26 differentially expressed genes (DEGs), shortlisted based on p-value and analyses using the online annotation tool DAVID, to build six different classifiers that differentiate healthy and diseased samples. Then, they performed additional co-expression analyses to identify 13 of the genes as potential AD biomarkers. Perera et al.16, on the other hand, determined the AD DEGs and then shortlisted them using PCA, Random Forest, and Extra Tree Classifier as feature selection methods. They used the shortlisted features to build six different classifiers that differentiate healthy and diseased samples, and the feature importance scores from Random Forest and Extra Tree Classifier and correlation matrix were used to identify 14 new candidate biomarker genes for AD. Zhao et al.17 started by identifying the key modules associated with AD, then performed functional enrichment analysis to reveal the hub genes, which they validated by machine learning algorithms. Yu et al.18 determined the AD DEGs, from which hub genes were shortlisted and genes based on the LASSO feature selection method. They identified a 16 hub gene set and a 35 biomarker set based on LASSO and proposed the overlapping genes as potential AD targets through this process. These studies obtained significantly different results based on the methodologies used, but they each contribute to revealing more of the complex AD progression.
Several studies have analyzed the gene expression in different brain regions and compared the gene expression in specific brain regions to unravel the underlying biology. Here, instead, we used machine learning to pinpoint genes that feature in AD’s progression despite the differences in sample type, such as laser-captured neurons versus bulk tissue or differences in brain regions. First, we used AD DEGs to determine hub genes through six ranking algorithms (and the overlapping genes) and two feature selection methods (and the overlapping genes). We then used this to determine the gene set/s that best contribute to an AD versus healthy controls prediction task using random forests (RF) and support vector machine (SVM) classifiers for the ML models, and convolution neural networks (CNN) and deep neural networks (DNN) for DL models. The feature selection methods achieved better prediction performances than the hub gene sets, and the five overlapping genes from the feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We also explored which set of overlapping genes is more aligned with the underlying biology of AD based using DisGeNET in Enrichr and found that 7 of the top-10 enriched diseases were neurological diseases/disorders for the upregulated genes from the overlapping hub gene set. Furthermore, we conducted a literature review and found a substantial portion of the pinpointed upregulated hub genes ($70\%$) are known AD targets.
## Gene expression data
We searched for gene expression datasets of brain tissue in the Gene Expression Omnibus (GEO) database19 using the query: "Alzheimer* AND Homo sapiens" filtered by "Expression profiling by array" on the 2nd March 2022. We retrieved 188 entries which we sifted through. We found three datasets (GSE528120, GSE4835021, and GSE129722) generated using the same platform, in this case, Affymetrix Human Genome U133 Plus 2.0 Array, that provides gene expression data of the brain tissue of AD patients and healthy controls within the same age range. There are 253 samples (80 ADs and 173 controls) in GSE48350, 161 samples (87 ADs and 74 controls) in GSE5281, and 31 samples in GSE1297 (22 ADs and 9 controls). In total, we had 455 samples (189 ADs and 256 controls) which we used to develop our ML and DL models. We also used the GSE10988723 and GSE13826024 datasets for independent testing. GSE109887 is a brain tissue dataset from the medial temporal gyrus, containing 46 AD samples and 32 controls, and GSE138260 is a brain tissue dataset containing 17 AD samples and 19 controls. Table 1 describes the selected datasets. Table 1Description of the datasets selected for training and independent testing. Dataset IDsNo. of SamplesHealthy controls/ADBrain regionFemale/ MaleUseGSE$\frac{528116174}{87}$HippocampusEntorhinal cortexMedial temporal gyrusPosterior cingulateSuperior frontal gyrusPrimary visual cortex$\frac{58}{103}$TrainingGSE$\frac{48350253173}{80}$HippocampusEntorhinal cortexSuperior frontal cortexPost-central gyrus$\frac{129}{124}$TrainingGSE$\frac{1297319}{22}$Hippocampus, Entorhinal cortex$\frac{18}{13}$TrainingGSE$\frac{1098877832}{46}$Medial temporal gyrus$\frac{40}{38}$Independent testingGSE$\frac{1382603619}{17}$Brain Tissue$\frac{19}{16}$ (one sample is NA)Independent testing Note, for dataset selection, we considered the datasets with more than 30 samples. However, several datasets have been excluded for different cases, some datasets were excluded by the ImaGEO quality control tool (elaborated on in the next section). Other datasets that we checked containing duplicates for the same individual, in the same brain region, with highly varying expression levels, were also excluded, as this reflects poorly on the quality of the provided data.
## Meta-analysis of the gene expression data
We used ImaGEO25, a web-based platform that integrates and performs meta-analyses of multiple GEO datasets. We aimed to combine the same experimental condition across different studies to increase the sample size and statistical power. Thus, we established the ImaGEO meta-analysis as an effect size method. Then, we selected the fixed-effect model parameter for the effect size estimation to identify the genes with the most potent effect in the selected datasets with an adjusted p-value < 0.05, with only $10\%$ missing values allowed. Next, we used ImaGEO to integrate all three GEO datasets (GSE5281, GSE48350, and GSE1297), perform background correction, normalization, batch effect correction, and apply initial differential expression analysis. Through this process, ImaGEO generated an integrated matrix with 924 genes as the potential DEGs, which we used in subsequent analyses. To define our features for the ML models, we downloaded the matrix file provided by ImaGeo for each of the three datasets. We then used R26, an open-source statistical and scientific programming language for data analysis, to integrate the matrices, creating the full matrix containing gene expression for all the samples. After that, we selected the DEGs from this full matrix to build the final matrix with 445 samples and 924 features (DEGs).
## Identifying hub genes using the PPI network
*Hub* genes are regularly used to zoom in on the subset of DEGs that would best discern the diseased samples from the healthy control. Thus, we used STRING27, a biological database and web resource of known and predicted PPI (http://string-db.org), to explore the interactions between the DEGs. Next, we used the Cytoscape software28 (version 3.9.1) (https://cytoscape.org/) to visualize the network, and we utilized the cytoHubba plugin in Cytoscape to identify the hub genes in the PPI network using several ranking methods.
CytoHubba29 provides different algorithms for node ranking, including local and global methods. The local rank method considers the relationship between the node and its direct neighbors, while the global method examines the relationship between the node and the entire network. We used six ranking algorithms to determine the hub genes, including three local ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC)), and three global ranking algorithms (Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality). The degree of a node v is the number of its adjacent nodes. MNC is the size of the maximum connected component of N(v), where the neighborhood N(v) is the set of nodes adjacent to v and does not contain node v. Stress centrality measures the absolute number of shortest paths, while betweenness centrality measures the fraction of the shortest paths passing through a node. Finally, closeness centrality indicates how close a node is to all other nodes in the network, calculated as the average of the shortest path length from the node to every other node in the network. Finally, we used the top-100 genes from each ranking method to develop ML/DL models.
## Identifying the subset of DEGs that best discern the diseased samples from the healthy control using feature selection models
To identify the subset of DEGs that best differentiate the diseased samples from the healthy controls, we applied feature selections algorithms, including LASSO regression (Least Absolute Selection and Shrinkage Operator)30 and Ridge regression31 that select the best features in high-dimensional data (i.e., provide a principled way to reduce the number of features in a model). These algorithms assign an importance score to each feature based on the feature's ability to predict the correct label. Briefly, LASSO regression eliminates many features and reduces overfitting in the linear model, while Ridge regression minimizes the impact of features that are not important in predicting the sample’s label. LASSO involves a penalty factor that determines how many features are retained; using cross-validation (CV) to choose the factor helps assure that the model will generalize well to future data samples. We implemented LASSO and Ridge algorithms using Python programming language and the Sikitlearn library. To apply the LASSO regression feature selection, we first need to tune the alpha (α) hyperparameter to make a suitable regression model and obtain the best performance. Thus, we utilized grid search for an α parameter by applying GridSearchCV using fivefold CV and repeated the process five times. Next, we fed the sample matrix with all the features (924 DEGs) into LASSO logistic regression. The model then allows us to select the best subset of features based on a threshold important score. We evaluated the ability of each subset chosen to differentiate between AD and healthy samples by calculating the AUC scores using our ML/DL models and then selected the subset with the highest AUC. We applied similar steps to the Ridge algorithm. At the end of this process, the α values selected were 0.01 and 0.99 for LASSO and Ridge, respectively.
## Developing ML and DL models
We developed several ML and DL models to distinguish between the AD samples and the healthy controls. We implemented RF and SVM classifiers for the ML models and CNN and DNN for the DL models.
For the ML models, we created a search space for each model for parameter optimization to find the best combination of parameters. Therefore, we used a randomized search followed by a grid search algorithm for hyperparameter optimization in RF and SVM. We created a grid of hyperparameters for the randomized search and then trained/tested our models using random hyperparameter combinations. Next, the best parameters identified through the randomized search are subjected to a grid search to find the optimal parameter combinations.
For the DL models, we only applied the grid search in our hyperparameter search space. We implemented DNN, a neural network with four hidden layers with unit sizes [64, 64, 32, 32], followed by a dense layer. We set the batch size to 128 and trained 500 epochs. In the same way, we implemented CNN, a convolutional neural network using two 1D-CNN layers with filter sizes 64, followed by two dense layers with unit sizes 32. We used max-pooling and flatten layers between the CNN and the dense layer. We trained the model with batch size = 128 and 500 epochs. Table 2 summarizes the tested values and bold font indicates the selected values. Table 2Parameter search space for optimizing RF, SVM, DNN and CNN models. ModelHyperparametersSearch spaceRFMax_depth[10,20,30,40,50,60,70,80,90,100]N_estimators[100,200,300,400,500,600]Min_samples_leaf[1, 4, 6, 8, 12]SVMGamma[‘scale’, ‘auto’]Kernel[‘linear’, ‘poly’, ‘rbf’]C[0.05, 0.25, 0.5, 1.0, 1.5, 2.0]DNNActivation function['softmax', 'relu', 'tanh', 'sigmoid', 'linear']Optimizers['SGD', 'RMSprop','Adam', 'Nadam']Node size in each layer[32, 64, 128, 256]Batch size[8, 16, 32, 64, 128, 256]Epochs[50, 100, 200, 500, 1000]CNNActivation function['softmax', 'relu', 'tanh', 'sigmoid', 'linear']Optimizers['SGD', 'RMSprop','Adam', 'Nadam']Filters[32, 64, 128, 256]Batch size[8, 16, 32, 64, 128, 256]Epochs[50, 100, 200, 500, 1000]The optimal parameter values are in [bold].
To evaluate these models, we used five-fold-cross-validation (CV), where the data is divided into five subsets using the StratifiedKFold method, which ensures that all subsets include the same percentage of positive and negative samples (i.e., AD and controls). The process is implemented (repeated) five times by keeping one subset for testing and using all the remaining sets for training. The AUC score of each fold is calculated, which we used to report the average AUCs.
## Bioinformatics analyses
For the bioinformatics analyses, we used DisGeNET housed in the comprehensive gene set analyses tool, Enrichr32. We ranked the results based on the Odds Ratio. We also used miRNet33 with a network degree filter cutoff of 5.0 for all network nodes to determine the key set of microRNA and transcription factors associated with the upregulated genes among the 28 overlapping hub genes.
## The study design
The workflow of our study incorporates six main steps, as depicted in Fig. 1. First, we downloaded three brain tissue-based datasets from the National Center of Biotechnology Information-GEO (NCBI-GEO) Datasets34 accessed by March 2022. The details and statistics of these datasets are given in Table 1. Thus we obtained a total of 445 samples consisting of 189 ADs and 256 controls. Second, after combining all data samples, we used them in an integrated meta-analysis. Therefore, the analysis identified 2915 DEGs in GSE5281, 163 DEGs in GSE48350, and 4 DEGs in GSE1297. Third, we utilized the ImaGEO tool to integrate and identify the DEGs of the three integrated datasets (i.e., the overlapped DEGs that are found in the three datasets). Fourth, we identified the hub genes using six graph ranking algorithms. Fifth, we identified the most significant DEGs using other feature selection methods. The sixth step is to build and evaluate ML/DL models using different sets of features (i.e., DEGs) generated from the latter two steps. Each step is explained later in more detail in the corresponding subsection. Finally, we tested our best models using independent datasets. Figure 1The study workflow consists of two key paths, via ranking algorithms and feature selection methods.
## Identifying DEGs between brain samples from AD patients and healthy aged controls
The ImaGEO tool's quality control test shows the data used in this study is of good quality. For the integrated datasets, the ImaGEO tool’s meta-analysis further identified 924 DEGs, including 512 upregulated DEGs and 403 down-regulated DEGs. Supplementary Table S1 provides the complete list of DEGs. A visual representation of the top-100 DEGs in a heatmap shows that the expression of more of the genes in the AD group is consistently upregulated in all the samples compared to the healthy aged controls (see Fig. 2, purple represents the AD samples, and the green represents the control samples from the healthy aged individuals, annotated at the top of each plot). Also, about $50\%$ of these clearly down-regulated genes in the AD group are consistently upregulated in the control samples. On the other hand, most of the genes upregulated in the so-called ‘healthy aged controls’ were not consistently upregulated in all the samples, and expression levels varied dramatically across samples. This observation is not too surprising as Berchtold et al.21, amongst others, reported age-dependent changes in gene expression in the brain. Specifically, the aged brain in both sexes increases immune activity, but it is proportionally in the female brain, and the male brain suffers a global decrease in catabolic and anabolic capacity. These sex and age-dependent changes in gene expression in the brain are thought to set the balance between neurodegeneration and compensatory mechanisms in the brain. Figure 2An ImaGEO generated heatmap of the top-100 DEGs (Red represents the relative upregulated gene expression; green represents the relative downregulated gene expression; black represents no significant change in gene expression).
## Determining the subset of DEGs that can serve as features to build the ML/DL models
We determined the features needed to build the ML/DL models using: 1/ various ranking algorithms to identify the hub genes and 2/ feature selection methods to determine the gene sets that best contribute to the prediction task.
Briefly, we used the 924 DEGs to construct a PPI network using STRING. To remove all nodes with less than five connected edges, we filtered the PPI network using a threshold of five degrees, which generated a network consisting of 863 nodes (i.e., genes) and 4720 edges (i.e., direct physical PPI). This network was then fed into Cytoscape software28 to visualize and determine the hub genes using the cytoHubba plugin. As a result, we obtained the top-100 hub genes for six topological ranking algorithms, including Degree, Betweenness Centrality (BC), Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Closeness Centrality, and Stress Centrality (see Supplementary Table S2). Surprisingly, we found that all six ranking algorithms commonly identified only 28 of the hub genes (ATP5B, ATP5C1, CYCS, NDUFA4', ACO2, NHP2, RPS7, FAU, EIF2S1, CCT7, UBC, PTEN, PSMD4, PSMD7, GOT2, SNAP25, MAPT, BDNF, NTRK2, SNCA, APP, JUN, IGF1, MAP2K1, RAF1, CDC42, ENO2, JAK2), and if we exclude the MCC list, the genes commonly identified by the ranking algorithms increase to 53 hub genes. Finally, we used these hub gene lists to develop computational models that could differentiate between AD samples and the healthy control samples.
We also applied the LASSO and Ridge logistic regression feature selection methods using the 924 DEGs. We tested several threshold values to select the best subset of features for both methods. Utilizing LASSO logistic regression, we obtained gene sets of 71, 27, and 8 by specifying the important scores higher than 0.01, 0.02, and 0.03, respectively. Similarly, for the Ridge algorithm, we obtained sets of 80, 41, and 26 genes by specifying the important scores higher than 0.05, 0.06, and 0.07, respectively.
## Evaluating the prediction performance of the ML and DL models
We evaluated the changes in the prediction performances of the two ML (RF and SVM) and the two DL (DNN and CNN) models when fed different sets of the features determined by the six ranking algorithms (hub genes) and the two feature selection methods separately (see Fig. 1).
## Prediction performances using hub genes determined by diverse ranking algorithms
We constructed RF, SVM, DNN, and CNN models using genes identified through the six ranking algorithms separately. We iteratively added ten of the top-100 ranked genes for each ranking algorithm to evaluate the models. Briefly, the evaluation process is as follows: first, we obtained the featured hub genes list based on the MNC algorithm, sorted using the MNC score. We evaluated the performance of the top genes by iteratively adding ten by ten genes each time. That is, for training and testing, we added the 10 top-ranked genes, used the cross-validation method, and reported the averaged AUC score as described in section "Identifying hub genes using the PPI network". Then, we added the next set of 10 genes, making it the top-20 genes, and applied the same evaluation process to obtain the averaged AUC. We repeated this process until the complete list of the top-100 genes was fed into the RF classifier. Note, we performed the same procedure for all ranking lists separately.
Table 3 provides the results for each experiment of the evaluation process. For each model, we underline the best prediction performance for each ranking method, then the bold and italic indicate the best and second-best of these results. The results show that the BC ranking algorithm consistently appeared among the best and second-best of these results for all the models, followed by Stress Centrality and Degree. On the other hand, the MCC list generally produced the worst performances for all the models, except DNN.Table 3Based on six topological ranking algorithms, the prediction performances for RF, SVM, CNN, and DNN in terms of AUC for top-ranked DEGs (increased by 10).RFRankingtop10top20top30top40top50top60top70top80top90top100BC0.7730.7960.82860.84540.85990.86040.86960.87350.86590.8642MNC0.75760.81760.82720.8470.8490.85230.86070.85920.86660.8651MCC0.7460.82770.85130.84690.84930.8560.85070.84270.85090.8555Stress0.76030.80280.82840.84280.86130.8550.86410.86280.86380.8658Close0.76840.78890.81070.83260.84030.85020.84690.8590.86450.8661Degree0.75760.81760.83440.84360.85410.8480.85560.85830.86710.8669SVMRankingtop10top20top30top40top50top60top70top80top90top100BC0.70640.74970.76360.7990.79780.81010.81140.82460.82470.8183MNC0.70610.75180.76210.78430.7870.7810.79550.80320.81190.8104MCC0.70290.7390.74670.74760.75190.76920.77190.78630.78150.7841Stress0.69950.74060.76070.7860.810.79850.80090.81230.80810.817Close0.68450.73390.73260.78590.7810.79220.80020.79790.80490.8062Degree0.70610.75180.76150.7820.79290.79220.78710.79210.81290.8105CNNRankingtop10top20top30top40top50top60top70top80top90top100BC0.74310.83240.85110.83530.78550.8430.82810.84890.85240.8465MNC0.74650.82450.8360.78780.83970.84490.83690.80110.83360.8277MCC0.72250.78530.79540.79040.81930.8090.81530.7960.79220.8123Stress0.73370.76520.83810.80120.83520.84430.84530.8530.85020.853Close0.75070.75290.78890.81090.79250.81310.82540.83760.83010.8422Degree0.74650.82450.83390.8080.84940.8240.83730.81210.84830.8234DNNRankingtop10top20top30top40top50top60top70top80top90top100BC0.79080.82620.87930.89550.87210.86410.87270.86410.86220.8725MNC0.78190.86310.84130.8530.86720.86430.85290.84250.85640.8546MCC0.7560.81010.86290.84720.87560.87660.84910.84980.86220.84Stress0.79580.82230.85460.8630.87360.86210.8610.87490.86650.8764Close0.79370.8120.85520.83720.84290.85050.84120.85980.86130.8556Degree0.78190.86310.86980.86530.86130.84950.83970.84490.84280.8585The underline indicates the highest AUC achieved for each ranking algorithm. The Bold and Italic values suggest the best and second-best performing ranking algorithms.
Focussing on the models, we find that the RF and DNN models clearly outperformed other models. Specifically, the DNN achieved the best prediction performance with an AUC of 0.8955 using the top-40 genes of the BC ranking list. On the other hand, the best prediction performance for RF is an AUC of 0.8735, achieved with the top-80 genes of the BC ranking list.
## Prediction performances of the models using genes determined by feature selection algorithms
We evaluated the RF, SVM, DNN, and CNN models separately based on two feature selection algorithms, LASSO and Ridge. We evaluated the prediction performances at multiple thresholds for the importance scores. Figure 3 provides the prediction performances achieved using the features selection algorithms. First, all the AUC results are better when applying Ridge or LASSO feature selection methods than when feeding all 924 DEGs to ML and DL models, indicating that filtering the features by removing some of them with low importance scores improves the results. Second, the Ridge algorithms achieved the best results when using the important score > 0.06 (Fig. 3 a). While the best results were achieved for the LASSO algorithms when using the important score > 0.02 (Fig. 3 b). Another observation is that all ML and DL prediction performances were significantly higher when applying Ridge and LASSO features than when the hub genes were applied. The high AUC demonstrates that both the LASSO and Ridge algorithms captured the significant DEGs that allow the classifiers to distinguish the AD samples from the samples belonging to the healthy aged controls. Both the LASSO and Ridge algorithms achieved high AUCs of 0.9896 and 0.9841, respectively, with the DNN model, outperforming all the other models. The lists of genes are provided in Supplementary Table S3.Figure 3The prediction performance of the list of DEGs selected by (a) LASSO (L) and (b) Ridge (R) regression algorithms at multiple thresholds for the importance scores.
## Prediction performances using overlapping hub genes determined by ranking and feature selection algorithms
As mentioned above, 28 hub genes were commonly identified through the six ranking algorithms, and when we exclude the genes identified by MCC, we found this list of commonly identified genes almost doubles to 53 hub genes. Thus, we also evaluated the prediction performances of these overlapping genes. We found that of the genes common to all the ranking algorithms (the 28 and 53 hub genes), the 53 hub genes achieved higher AUC for all the models than the 28 genes (Fig. 4). Moreover, with the 53 hub genes, the DNN and RF models achieved better prediction performances. Specifically, the DNN model achieved the best prediction performance with an AUC = 0.8473. Beyond this, the feature selection algorithms LASSO and Ridge commonly identified five genes, including FCER1G, PDE6H, SEMA6A, SLC25A46, and SST. When evaluating the prediction performances of the models with these five genes, we similarly found that deep learning models achieved better prediction performances than the machine learning models. Specifically, the CNN model achieved the best prediction performance with an AUC = 0.979. Moreover, the prediction performances of the models with these five genes achieved AUC’s ranging from 0.9589 to 0.979, which is surprising as the five hub genes commonly identified by the feature selection algorithms are not a subset of the 28 or 53 hub genes identified through the ranking algorithms. Figure 4Comparing the AUC results of different genes lists. The blue bar is for the whole DEGs list (924 genes), the red bar is the overlapping list of LASSO and Ridge (5 genes). The yellow bar is the overlapping list of the six ranking algorithms (28 genes), and the green is the overlapping list of the ranking algorithms excluding MCC (53 genes).
These results suggest that even though several research groups built classifiers that achieved good prediction performances using hub genes35,36, we can likely improve the classifier’s prediction performances by using feature selection algorithms such as LASSO and Ridge instead. Also, the AUC = 0.979 achieved with the five genes (commonly identified by the LASSO and Ridge algorithms) is very close to the highest AUCs achieved with the 27 (AUC = 0.9896) and 41 (AUC = 0.9841) genes identified by the LASSO and Ridge algorithms, respectively. This finding is crucial as even though we have moved to a time where high throughput expression data is more easily attainable, medical facilities are still largely implementing the practice of only testing a small set of genes to determine the diagnosis. Thus, incorporating the five genes into current medical practice should be more readily accepted than long lists of genes. Nonetheless, more experiments are needed before this idea will be feasible.
## Evaluating the performance of the classification models on independent sets
To further assess the robustness of our best models. We tested these models on two independent datasets (GSE109887 and GSE138260, see Table 1). We performed the external testing using two of the best-constructed models, the DNN model built by the 27 key genes selected by LASSO and the DNN model built using 41 key genes selected by Ridge.
In this experiment, we used all the samples of the combined dataset to train the DNN model and then tested the model using the external datasets. For the GSE109887 brain tissue dataset from the medial temporal gyrus, our best model successfully classifies the samples using the 41 genes, and achieves AUC = 0.8546, F1 = 0.7524, Recall = 0.7564, and Precision = 0.7549. Also, for the GSE138260 brain tissue dataset, our best model classified the samples with AUC = 0.7523, F1 = 0.7205, Recall = 0.7222, and Precision = 0.737. The results show that even though the cross-validation shows several feature sets give high accuracy, performing the external testing, we found that the best feature set is the 41 genes since it performed well in both the training and the independent testing sets.
## Bioinformatics analyses of the overlapping gene sets
To determine which set of overlapping genes is more aligned with the underlying biology of AD based on different bioinformatics tools, we used the comprehensive gene set analyses tool, Enrichr32. First, we determined which gene set is linked to the most neurological disorders using DisGeNET in Enrichr. For this process, we only used the upregulated genes that in essence contributors to the features that characterize AD. Figure 5 provides the top-10 enriched diseases retrieved from DisGeNET for all the overexpressed genes in the LASSO, Ridge, and DEGs gene set, as well as the overlapping gene sets. We then determined all the neurological disorders in each gene set (highlighted in pink). All the upregulated DEGs and all the overlapping gene sets, only had two or less neurological diseases/disorders featuring in the enriched top-10 diseases, except the 28 overlapping hub gene set. For the upregulated genes from the 28 overlapping hub genes, 7 of the top-10 enriched diseases were neurological diseases/disorders, suggesting that this list likely captures more of the underlying AD pathophysiology. Figure 5The top-10 enriched diseases retrieved from DisGeNET for upregulated genes in the different gene sets, including the DEGs, LASSO, Ridge, and the overlapped/common hub and feature selection genes.
For the 5 overlapping genes identified through the feature selection methods (LASSO and Ridge), two of the potentially identified biomarkers SST and SEMA6A are also identified in Perera et al.16, Yu et al.18, and Madar et al.15. MT1M is also one of the genes selected by LASSO, the subset that achieved high accuracy in distinguishing AD and control samples, and was also identified as a potential biomarker in Perera et al.16. For the 28 overlapping genes identified through the ranking algorithms, one of the potentially identified biomarkers BDNF is also identified in Yu et al.18. This shows the overlap between our study and other studies15–18 specifically is very small.
We subsequently conducted a literature review to determine which of the ten upregulated hub genes (among the 28 overlapping hub genes) have already been recognized as AD therapeutic targets. There are currently many research efforts to provide disease-modifying therapies for AD treatment37. The key targets are Tau (the MAPT gene product) and amyloid precursor protein (APP), as they are the significant components of neurofibrillary tangles and amyloid plaques, respectively38,39. Brain-derived neurotrophic factor (BDNF) functions as a ligand for neurotrophic tyrosine kinase receptor type 2 (NTRK2). BDNF stimulates NTRK2 to phosphorylate APP, causing its accumulation in the TGN (Trans-Golgi Network), diminishing its amyloidogenic cleavage. However, BDNF is reduced in AD, while levels of APP increase. Moreover, NTRK2 and APP are cleaved by δ-secretase in AD brains, and blocking TrkB cleavage in 5xFAD mice attenuated AD pathologies40. Also, Aβ triggers PDZ-dependent recruitment of PTEN into the postsynaptic compartment to induce synaptic toxicity and cognitive dysfunction, which offers a new mechanism-based therapeutic target to counteract downstream Aβ signaling41,42. Thus, APP, NTRK2, PTEN, and the MAPT gene product (Tau) are all aggregate protein-related therapeutic targets.
Moreover, the JAK2 inhibitor, TG101209, attenuated the IFNγ-induced changes in cultured microglia and microglia from APP/PS1 mice43. Also, Raf inhibitor, sorafenib, reversed memory impairment and reduced the expression of APP, Cox-2, and iNOS in the brain of an AD transgenic mouse model, which also suggests targeting RAF144. These works suggest JAK2 or RAF1 as AD targets and potential strategies for reducing AD’s neuroinflammation. George et al.45 also recently demonstrated that long-term suppression of (insulin-like growth factor 1 receptor) IGF1R signaling alleviates AD progression and promotes neuroprotection in animal models.
This shows that a substantial portion of the pinpointed upregulated hub genes ($70\%$) are AD targets and suggests that JUN, CYCS, and PSMD4 should also be explored. This is in line with recent reviews46–48 that suggest inhibitors of the mitogen-activated protein kinases (MAPK) pathways, such as the c-Jun N-terminal kinase (JNK) pathway, be tested for AD treatment as JNK3 enhances Aβ production and plays a key role in the maturation and development of neurofibrillary tangles.
To further determine the key set of microRNA and transcription factors associated with the upregulated genes among the 28 overlapping hub genes, we used miRNet33 with a network degree filter cutoff of 5.0. Figure 6 provides the network generated with miRNet that shows six important miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and highlights JUN as the critical transcription factor participating in this process. Since 2020 four of the six microRNA were shown to be potential AD targets. Specifically, miR-16-5p49 and miR-34a-50 were shown to relieve amyloid β-induced injury by decreasing apoptosis and oxidative stress via targeting BACE1, and overexpression of miR-26a-5p suppresses Tau phosphorylation and Aβ accumulation51, indicating these microRNAs downregulated in AD are protective agents, and their increase can be targeted as a treatment. Moreover, another study show inhibitor of NF-κB kinase β (IKKβ) knockdown and miR-155-5p inhibition ameliorated cognitive impairment, improved neuron regeneration, and attenuated Aβ deposition in APP/PS1 mice, suggesting miR-155-5p as a target for AD treatment52.Figure 6Network generated by miRNet. It shows six important miRNAs, represented by blue squares, and highlights JUN as the critical transcription factor.
Together these results suggest that even though the feature selection methods are better for the classification of samples, in this case, the 28 overlapping hub genes may be better for analyzing the underlying pathophysiology of AD and for pinpointing the potential targets, compared to the 5 overlapping genes identified through the feature selection methods. Additionally, the obtained results and follow-up investigation can deliver new insights into AD treatment.
## Concluding remarks
Decades of research have been poured into understanding the underlying pathophysiology of AD, yet we still do not have a cure for AD. And more recently, these efforts have been focused on identifying more potential targets for drug development as past drug development failures were found to be a result of inefficient targets. In parallel, advances associated with high throughput technology made more data available to build classifiers that can serve as decision support tools for clinicians. Thus, several research groups have built classifiers using hub genes and feature selection methods, and further used these gene sets to unveil more of the diseases’ underlying pathophysiology Thus, we here used both hub and feature selection genes to build ML/DL classifiers that can best distinguish AD samples from healthy aged controls. We also used the overlapping genes which allow zooming in on genes commonly pinpointed by more than one approach which increases confidence in their reliability. In our case, the five genes (commonly identified by the LASSO and Ridge algorithms) that produce a prediction performance (AUC = 0.979) similar to the best-performing method with 27 to 41 genes, can be incorporated into current medical practice more easily than a larger number of genes. Second, using only the overlapping hub genes and specifically, the upregulated ones makes the data less convoluted to show its link to the disease and the literature review further suggests that the majority of the upregulated overlapping hub genes may be targets, as well as the microRNAs that target multiple of the genes in this specific set.
## Data availability
In this study, we used publicly available gene expression datasets. These datasets can be found on Gene Expression Omnibus, (accessed by April 2022), https://www.ncbi.nlm.nih.gov/geo/. The source code of the ML\DL models in this work is available on: https://github.com/HindAlamro/AD_biomarker.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30904-5.
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|
---
title: Genome-wide association study of population-standardised cognitive performance
phenotypes in a rural South African community
authors:
- Cassandra C. Soo
- Jean-Tristan Brandenburg
- Almut Nebel
- Stephen Tollman
- Lisa Berkman
- Michèle Ramsay
- Ananyo Choudhury
journal: Communications Biology
year: 2023
pmcid: PMC10043003
doi: 10.1038/s42003-023-04636-1
license: CC BY 4.0
---
# Genome-wide association study of population-standardised cognitive performance phenotypes in a rural South African community
## Abstract
Cognitive function is an indicator for global physical and mental health, and cognitive impairment has been associated with poorer life outcomes and earlier mortality. A standard cognition test, adapted to a rural-dwelling African community, and the Oxford Cognition Screen-Plus were used to capture cognitive performance as five continuous traits (total cognition score, verbal episodic memory, executive function, language, and visuospatial ability) for 2,246 adults in this population of South Africans. A novel common variant, rs73485231, reached genome-wide significance for association with episodic memory using data for ~14 million markers imputed from the H3Africa genotyping array data. Window-based replication of previously implicated variants and regions of interest support the discovery of African-specific associated variants despite the small population size and low allele frequency. This African genome-wide association study identifies suggestive associations with general cognition and domain-specific cognitive pathways and lays the groundwork for further genomic studies on cognition in Africa.
A genome-wide association study for five cognitive phenotypes in a rural South African community suggests several variants associated with general cognition and domain-specific cognitive pathways in this cohort.
## Introduction
Normal cognitive function is an essential determinant for health and quality of life indicators. Evolutionary evidence suggests that along with increased cranial complexity, humans developed complex communication, abstract thought, and reasoning through their increased capacity for social learning1. Genome-wide association studies (GWAS) for cognitive function have been challenging despite twin studies suggesting heritability scores up to ~$80\%$ for various cognitive phenotypes1–10. The question of heritability is further complicated by evidence that it varies across the lifespan and has different trajectories throughout the life course, with relative stability observed from middle to old age4,9–13. Despite the complex, polygenic, and pleiotropic nature of neurocognitive phenotypes, meta-analyses with larger sample sizes (>50,000) were able to detect associations with single nucleotide polymorphisms (SNPs) and have successfully replicated findings with genome-wide significance ($p \leq 5$ × 10−8)3,6,14–17. In order to perform these meta-analyses, general cognitive ability (or Spearman’s g) was derived from diverse positively but not perfectly correlated cognitive performance tests (capturing ~$40\%$ of phenotypic variance), or proxy phenotypes such as educational attainment3,6,9,14–18. Studies have used different metrics, measures, and tests to describe traits such as intelligence (fluid or crystallised), general cognitive function, and domain-specific cognitive outcomes hence the adoption of g to account for testing heterogeneity2–4,9,18–20. Functional studies have shown that each of the cognitive domains has an impact on gene expression in different regions of the brain, making latent cognitive ability an amalgamation of activity within the brain acting through different biological pathways4,9,21–29. A further limitation of these studies is that they suffer from sample heterogeneity in terms of the age of participants, socio-economic status (SES), and participant’s access to education3,12,15,20. As noted, cognitive trajectory changes throughout lifespan require participants to be within similar age ranges to accurately capture cognitive ability for comparative studies4,11–13. Education is also a major moderating factor for assessing cognitive ability, with evidence suggesting that genes associated with educational attainment are an artefact of positive selection1,9. Cognitive performance tests typically rely on literacy and numeracy, which is a source of bias in many low-income populations2,4,9,13,18,20,30,31. In some settings, SES is a major determinant influencing access to education, so cognitive batteries may be measuring educational exposure rather than innate cognitive function2,4,13,18,20,30,31.
There is little research on the genetics of cognitive function in African populations, or in those of African ancestry4,5,32–34. The lack of diverse ethnic representation in studies to date limits the discovery of associated variants as differences in linkage disequilibrium (LD) (with generally smaller LD blocks in Africans compared to Europeans), could enhance the discovery of causal variants in African populations35. The Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) collected baseline cognition data for over 5000 older adults in Bushbuckridge, rural Mpumalanga, South Africa (SA)36. A sub-set of 2246 participants from this study were also recruited as part of the Africa Wits-INDEPTH Partnership for Genomic Studies (AWI-Gen) for whom genotype data were available from the Illumina Human Heredity and Health in Africa (H3Africa) array37,38. The combined dataset with phenotype and genotype data was used to explore genetic associations with latent cognitive ability based on multiple quantitative traits (total cognition score, verbal episodic memory, executive function, language, and visuospatial ability) for ~2000 individuals in five independent GWAS using LD structure specific to those of African ancestry in SA. To the best of our knowledge, this is the first large study in Southern Africa to explore genetic contributions to non-pathological cognitive performance.
## Genome-wide association study results
We performed GWAS for five cognitive traits (Table 1). This sample had more women (~$58\%$) than men. The participants had little access to education, where ~$77\%$ of the sample population had not progressed beyond primary school. After rank normalisation, cognitive domain data were available for 1887 genotyped participants. The ranges displayed in Table 1 are for the population-standardised z-scores and show a particularly wide range of performance for visuospatial cognition. Total cognition score data were available for all 2211 genotyped participants. The imputed dataset included 13,972,012 SNPs. Table 1Descriptive statistics showing cognitive trait and covariate distribution. MeasurenRange (Median[IQR])Mean or percentageSDAge (years)221140 to 80 (57 [49–67])57.6910.93Sex2211Female$58.53\%$ [1294]Male$41.47\%$ [917]Level of education2211No formal education$39.85\%$ [881]Primary$37.63\%$ [832]Secondary$18.72\%$ [414]Tertiary$3.80\%$ [84]Cognitive measuresTotal cognition score22110–24 (12 [9–14])11.684.38Cognitive domainsa1887Executive function−4.69 to 4.33 (−0.14 [−1.32–1.30])Episodic memory−3.91 to 1.46 (0.29 [−0.54–0.79])Language−2.53 to 1.28 (0.13 [−0.41–0.51])Visuospatial cognition−10.63 to 4.77 (0.49 [−1.75–2.33])aCognitive domain data given as z-scores prior to rank normalisation with median and interquartile ranges rounded up to two decimal places.
The GWAS for verbal episodic memory, identified a genome-wide significant signal for rs73485231 ($$p \leq 7.70$$ × 10−9, β = 0.24, SE = 0.04) on chromosome 13 (Fig. 1a shows the Manhattan plot, b the QQ-plot with λ = 0.99, c and the Locus zoom plot). The mean episodic memory score was significantly lower in G homozygotes ($$p \leq 5.4$$ × 10−4) (Supplementary Fig. 1a). This intergenic SNP between GNG5P5 and HTR2A had a notably higher minor allele frequency (MAF) in Africans (AFR) (MAF = 0.13), compared to Europeans (EUR), Americans (AMR) and Asians (EAS and SAS) 1000 G Project super population groups (Table 2). Although no previous associations with cognition had been reported, GWAS Catalog reported this SNP to be associated with adolescent idiopathic scoliosis (Fig. 1c). A suggestive signal, rs140372794 ($$p \leq 1.04$$ × 10−7, β = 0.33, SE = 0.06), was observed on chromosome 8 (Supplementary Data 1). This SNP, along with a second suggestive variant (rs62529410, $$p \leq 7.02$$ × 10−7) within 27 kb of it, falls within 100 kb of LINC02055, a long intergenic non-protein coding RNA gene harbouring several SNPs previously associated with mathematical ability and general cognitive function. Gene-based association (Supplementary Table 1) yielded two suggestive gene signals; one for TRPM6 on chromosome 9 (minimum $$p \leq 2.31$$ × 10−6), which encodes a magnesium channel protein39,40, and another for BACE2 on chromosome 21 (minimum $$p \leq 3.08$$ × 10−6) which codes for an essential enzyme for the cleavage of β-Amyloid and the development of Alzheimer’s disease (AD)41–43.Fig. 1Genome-wide and suggestive associations with verbal episodic memory.a Manhattan plot. Genome-wide significance cut-off 5 × 10−8 is shown by red line and suggestive cut-off 5 × 10−6 is shown by blue line. b QQ-plot (λ = 0.99) for association summary statistics c Locus zoom plot for rs73485231. LD is based on a South African LD panel. Table 2Minor allele frequency distribution of reported association signals in our sample compared to 1000 genomes super populations. TraitVariant IDMinor alleleMAF this studyMAF AFRMAF AMRMAF EASMAF EURMAF SASMemoryrs73485231A0.140.130.020.020.020.07Memoryrs140372794A0.050.040.01000Languagers140578927C0.010.010000Executive functionrs3845674T0.230.230.300.400.250.20Visuospatial abilityrs191611493T0.030.030.00000Total cognition scorers138832740C0.030.0400001000 Genomes Project Super Populations groups AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), and SAS (South Asian).
Our GWAS for language detected a near genome-wide significant association on chromosome 6 (rs140578927, $$p \leq 6.99$$ × 10−8, β = 0.65) (Fig. 2a, b). The MAF (C allele) for rs140578927 was 0.01 in our cohort and had not been reported in population groups other than the African supergroup in the 1000 G Project (Table 2). Despite its rarity, heterozygous individuals had higher mean language performance scores than homozygous individuals (Supplementary Fig. 1b). FUMA output indicated the nearest gene to be PLEKGH1 which has been associated with blood pressure, white matter intensity, and cortical volume44–46. Regional lookup places it downstream of MTHFD1L, which had been associated with late-onset Alzheimer’s disease and coronary artery disease47,48. A series of suggestive signals associated with language in this cohort are listed in Supplementary Table 1. Gene-based output (Supplementary Table 1) suggested two genes encoding mitochondrial proteins on chromosome 15; MRPL46 associated with depressive disorders (minimum $$p \leq 6.24$$ × 10−5)49,50 and MRPS11 (minimum $$p \leq 3.16$$ × 10−6) linked to body-mass index (BMI)51.Fig. 2Genome-wide and suggestive associations with language.a Manhattan plot. Genome-wide significance cut-off 5 × 10−8 is shown by a red line, and suggestive cut-off 5 × 10−6 is shown by a blue line. b QQ-plot (λ = 1.00) for association summary statistics. c Locus zoom plot for rs140578927. LD values are based on a South African LD panel.
Genome-wide analysis results for executive function yielded only suggestive signals (Fig. 3a and Supplementary Data 1); however, rs3845674 is of particular interest due to its proximity to BIN1 (Fig. 3c). *This* gene has been reported in multiple AD studies52–54. The effect allele of rs3845674 (G) has an allele frequency of ~$77\%$ in our sample, and homozygous carriers of this allele had significantly reduced executive function compared to heterozygous and homozygous T individuals (Supplementary Fig. 1c).Fig. 3Genome-wide and suggestive associations with executive function.a Manhattan plot. No genome-wide or near genome-wide significant signals. Suggestive cut-off 5 × 10−6 shown by a blue line. b QQ-plot (λ = 1.00) for association summary statistics. c Locus zoom plot for rs3845674. LD values are based on a South African LD panel.
No genome-wide associations were observed for visuospatial ability (Fig. 4a, b), but a series of suggestive signals in LD falling within the gene LMBRD2 are shown in Fig. 4c represented by rs191611493 which had the lowest p value ($$p \leq 1.23$$ × 10−6, β = 0.39, SE = 0.08) (Fig. 4a, b). The frequency of the effect allele was very low (Supplementary Data 1) and it did not have a significant effect on performance in this cohort (Supplementary Fig. 1d). Along with LMBRD2, gene-based analysis results implicated DHX15, TRPC7, DTX2, UPK3B and POMZP3 (Supplementary Table 1).Fig. 4Genome-wide and suggestive associations with visuospatial ability.a Manhattan plot. No genome-wide or near genome-wide significant signals. Suggestive cut-off 5 × 10−6 shown by a blue line. b QQ-plot (λ = 1.00) for association summary statistics. c Locus zoom plot for rs191611493. LD values are based on a South African LD panel.
Although no SNPs reached genome-wide significance for association with the total cognition score (Fig. 5a, QQ-plot 5b, and Supplementary Data 1), the lead SNP rs138832740 ($$p \leq 1.61$$ × 10−7, β = −2.01, SE = 0.38) was African ancestry-specific according to the 1000 G Project dataset (Table 2). No previous associations had been reported for rs138832740, likely due to its low frequency and apparent continental specificity. The closest gene to this SNP is RN7SL831P which has been reported in behavioural traits and BMI. Only one participant was homozygous for the C allele, but a significant difference ($$p \leq 2.4$$ × 10−3) between performance was observed between heterozygous individuals and those who were homozygous for the major allele (Supplementary Fig. 1e). *Two* genes (RBFOX3 and MACROD2), although they did not meet gene-wide significance, code for proteins which are highly expressed in the central nervous system and integral to neuron development (Supplementary Table 1).Fig. 5Genome-wide and suggestive associations for total cognition score.a Manhattan plot. No genome-wide or near genome-wide significant signals. Suggestive cut-off 5 × 10−6 shown by a blue line. b QQ-plot (λ = 1.00) for association summary statistics. c Locus zoom plot for rs138832740. LD values are based on a South African LD panel.
## GWAS replication
Exact replication of previously reported genome-wide significant variants associated with various cognitive function phenotypes was not achieved; however, using window-based methods proved to be sufficient to represent replication of our signals in other studies. The top observed association signals for each cognitive trait (Supplementary Data 1) were used for our window-based replication analysis. We reported replication of previously reported genome-wide significant SNPs (marked with an asterisk in Supplementary Data 2) and suggestive signals for memory and total cognition score.
The lowest p value observed for episodic memory window-based replication was for rs10773290 ($$p \leq 3.68$$ × 10−4), which was previously reported by ref. 55 as a suggestive signal for working memory along with two other markers. For rs8067235 (our study $$p \leq 4.55$$ × 10−4), a near genome-wide significant signal ($$p \leq 6.00$$ × 10−8) was observed by ref. 56 for association with memory performance.
For the total cognition score, we reported all window-based replication signals with $p \leq 5$ × 10−4 in Supplementary Data 2. Using the cut-off of 5 × 10−3, we managed to exactly replicate two suggestive signals: one for cognitive performance (rs2616984, $$p \leq 1.86$$ × 10−3), which also fell below our window-based replication threshold ($$p \leq 1.44$$ × 10−4), and one for general cognitive ability (rs1512144, $$p \leq 1.11$$ × 10−3 and window $$p \leq 2.81$$ × 10−4). Through widow-based replication, we further replicated 14 signals that had reached genome-wide significance in their respective studies for the traits of general cognitive ability and cognitive function. A further 40 SNPs were replicated for previously reported suggestive signals for the traits, cognitive performance, and generalised correlation coefficient along with the other traits mentioned above.
For the rest of the remaining cognitive traits; language, executive function, and visuospatial cognition, we failed to replicate previously reported suggestive signals within our cut-off threshold. These are presented in Supplementary Data 2.
## Discussion
*Few* genetic association studies for cognitive traits have been performed in continental Africans and meta-analyses suffer from the limitations of grouping different cognitive phenotypes together, of which data was collected using different screening tools4,57. Although a number of recent epidemiological studies assessing cognitive function and various associated phenotypes have been published, there is still a dearth of genomic data available from Africa.
Traditional cognition batteries are often ill-adapted to screening populations with lower literacy and numeracy levels, confounding comparative analyses4,57. This is especially evident in settings where educational attainment is strongly influenced by SES1,4,9,57. Adaptations of the standard mini-mental state examination (MMSE) to screen for cognitive impairment linked to ageing, and neurological and psychiatric conditions have been used since its inception as a simple way to assess cognitive traits such as orientation, comprehension, language, memory, and tasks for reading, writing, and drawing58. The main limitation of the MMSE is that it cannot be administered to individuals who are illiterate, making it unsuitable for capturing cognitive function data in communities with low literacy levels58. Spearman’s g (derived from the Wechsler Adult Intelligence Scale (WAIS) and general cognitive ability, used in large meta-analyses, are also problematic because the first is administered as an Intelligence Quotient (IQ) test assessing verbal comprehension, perceptual reasoning, working memory, and processing speed is said to account for only up to half of the variation of cognitive function, and the latter is composed of a number of imperfectly correlated traits representing a single cognitive metric4,7,59–62.
This pioneer African GWAS used baseline cognitive function data from a well-characterised rural South African cohort36, genetic data enriched for common African variants and imputed using an African-variant-enriched reference panel, and the OCS-Plus cognitive assessment tool specifically developed for low-income settings where access to formal education is limited, and language may present barriers, to search for genetic associations with population-standardised cognitive domain scores and total cognition. Although of modest size, compared to many recent meta-analyses of cognitive traits, several genome-wide signals associated with related traits were replicated.
The genome-wide significant variant observed for association with verbal episodic memory, rs73485231, is localised to an intergenic region between G protein subunit gamma 5 (GNG5P5) and 5-hydroxytryptamine receptor 2A (HTR2A). Although this common variant was significantly associated with better memory performance in this sample, due to the low minor allele frequency of this SNP in other population groups, this signal was not replicated. Multiple SNPs within the same region corresponding to GNG5P5 have been associated (although not at genome-wide significance) with gateway drug initiation in families63. Although the suggestive signal rs6252910 was located near Long intergenic non-protein coding RNA 2055 (LINC02055) from which independent variants have been associated with self-reported mathematical ability64, educational attainment65, and the relationship between schizophrenia and cognitive function25 in large meta-analyses, this is insufficient to provide evidence of association. Variants mapped to the suggestively associated gene, Beta-secretase 2 (BACE2), were associated with both educational attainment and mathematical ability by Lee, et al. [ 2018] and Okbay, et al. [ 2022]. BACE2, although originally thought to be a β-amyloid precursor protein (APP)-cleaving enzyme, cleaves APP at three sites, thereby inhibiting β-amyloid production as well as actively degrading it41–43. Its overexpression in cultured cells was found to significantly lower the concentration of intracellular β-amyloid, and it has been hypothesised that it may influence susceptibility to AD41–43. The second suggestively associated gene, transient receptor potential cation channel subfamily M member 7 (TRPM7), encodes a protein that has both ion channel and kinase domains that may play a role in magnesium homoeostasis39,40. It plays an essential role in embryogenesis and complete knockout is lethal in murine models39,66. Studies in Xenopus have shown that it is involved in neural tube closure and deficits result in a range of neural tube defects39,66. We replicated four reported suggestive signals previously associated with memory phenotypes; working memory, and memory performance. The replicated signal with the lowest reported p value, rs8067235, was the focus of a study combining computational modelling, GWAS data, and neuroimaging to validate the association of brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2) with verbal memory tasks56. Utilising functional MRI, they observed differences in mRNA expression between the anterior and posterior of the medial temporal lobe (the part of the brain responsible for encoding, memory storage, and recall)67, specifically when comparing recall of negative versus neutral memory tasks56. The remaining replicated signals were reported by Donati, et al. [ 2019] in a study looking at the overlap between measures of latent cognitive function and education in adolescents55.
Our suggestive signal associated with language is located within an intron of Pleckstrin homology and RhoGEF domain (PLEKHG1). Although, previous associations for this African-specific variant had not been reported for language or any other cognitive performance phenotypes, other variants within PLEKHG1 have been associated with cerebral white matter intensities (an indication of susceptibility to vascular dementia) in Europeans and systolic blood pressure in sickle cell populations44–46. Suggestive signals associated with language ability were replicated, with two SNPs (in Supplementary Data 2) reported in a Danish family study assuming that receptive language in children is subject to a parent-of-origin effect68. *The* genes FUMA suggested were associated with language code for large and small mammalian mitochondrial ribosomal subunits, respectively. The association of MRPL46 with depressive disorders was observed by Howard, et al. [ 2018] and Yao, et al. [ 2021] in their studies assessing multiple neuropsychiatric phenotypes and the possible genetic overlap between them49,50.
The GWAS results for executive function yielded a genome-wide significant replication of rs139493 associated with a trail-making test in 78,547 UK Biobank donors62. Our suggestive signal tagged Bridging Integrator 1 (BIN1) has been repeatedly reported as a significant AD locus52–54. Although the exact mechanism is unclear, there is evidence that there are numerous ways in which BIN1 expression may alter brain pathology54. BIN1 binds Tau proteins and its overexpression is correlated with AD pathology, possibly through increasing Tau production by stimulating its release from microglial cells52–54. In a study using transgenic mice, deposits of insoluble BIN1 were reported to accumulate alongside β-amyloid plaques in the brains of AD mice53. Furthermore, in knockout experiments, deficits appeared to cause impairment in spatial recognition and memory69.
We replicated a suggestive signal previously reported for association with visuospatial tasks in a Chinese population70. The most interesting gene-based result was for limb development membrane protein 1 domain containing 2 (LMBRD2). Malhotra, et al. [ 2020] reported novel missense variants at this locus in ten individuals, each exhibiting traits which are indicative of neurodevelopmental abnormalities71. These included motor and intellectual delay, as well as structural abnormalities71.
By using window-based replication, we replicated several genome-wide significant signals reported by Davies, et al. [ 2018] in a study of over 300,000 individuals assessed for general cognitive function6. This includes signals mapped to RNA Binding Fox-1 Homologue 1 (RBFOX1), a homologue to one of the suggestive gene-based association outputs from FUMA, and loci associated with various neurological disorders6. The SNP with the lowest p value for replication was rs11210871, which along with rs11577684, corresponds to loci on chromosome 1, which have been previously associated with intellectual disability and AD6. Loss of function variants and CNV in proximal gene GATA zinc finger domain-containing 2B (GATAD2B) have been associated with cases of intellectual disability72,73. Our lead SNP is a rare African-specific variant to which RNA 7SL cytoplasmic 831 pseudogene (RN7SL831P) is the closest gene. Aside from appearing in studies for educational attainment65 and mathematical ability64, single SNPs in the intergenic regions have been listed as associated with genome-wide significance to sleep-related phenotypes74–76 and neuropsychiatric traits like attention deficit hyperactivity disorder (ADHD)77,78, bipolar disorder79, eating disorders, and substance use77,80,81. Gene-based analysis suggested that RNA Binding Fox-1 Homologue 3 (RBFOX3) and mono-ADP ribosylhydrolase 2 (MACROD2) were associated with total cognition score. RBFOX3 is an alternative splicing regulator expressed in neurons and is a biomarker for neuron maturity82–84. Studies in mice and rats have elucidated its involvement in neuronal differentiation, neuro and synaptogenesis, and neurological disorders characteristic of hippocampal dysfunction82–84. Rare microdeletions in this gene have been found in patients suffering from childhood idiopathic epilepsy presenting with or without seizures85. Alterations in RBFOX3 have been associated with specific cases of developmental delay in humans86 and impaired visual learning in knockout mice82. RBFOX3 is expressed in neurons through all developmental stages and has been shown to interact with binding sites outside of the other RBFOX proteins83. Thus, it has also been suggested to play a role in miRNA biogenesis [94]. Immunohistochemistry of MACROD2 expression suggests that it may be involved in different stages of cortical neuron development and affect synaptic function87. Rare and de novo CNV within this gene have been observed in ADHD patients88. Knockout mice exhibited hyperactivity which increased with age despite slower observed movement and unusual sleep patterns similar to that seen in ADHD89. The most reported SNP for this locus, rs4141463, reached genome-wide significance for association with autism spectrum disorder (ASD) in a European study but was neither replicated in a later European study, nor in a study of Han Chinese90–92.
We observed overlapping suggestive signals for the highly correlated traits of language, executive function, and visuospatial ability on chromosomes 6 and 3. This was expected as in early childhood, executive function and language are intertwined as children with higher executive function tend to have better language skills93. In children with language impairments, lower executive function and attention reduced the ease at which visuospatial tasks were completed94. In the elderly, higher levels of education improved performance on verbal and non-verbal tasks requiring complex executive function95.
The adaptation of the US HRS cognition battery96 proved adequate in our study as a robust assessment of total cognition based on memory and orientation. Although this test originally included questions on numeracy, these were excluded as they were shown to be biased toward participants with higher levels of education96. The widespread use of cognitive screening tests derived from MMSEs provided a number of study phenotypes which were similar to the total cognition score as we calculated it. Using the highest level of education attained as a covariate allowed us to observe similar signals to those in large meta-analyses where educational attainment was used as a proxy for intelligence. On their own, our reported signals and the ones we replicated do not contribute to the overall heritability estimates for these phenotypes in a significant way, but there are some highly conserved loci which appear to contribute to the polygenicity of cognitive function. The OCS-Plus was a valuable tool in our study community which is known to have long-standing poor access to and quality of education, further limited by low employment rates97. We captured intra-population domain-specific cognition, rather than exploring the genetic basis of educational attainment as a proxy for cognitive function, as many other studies have done. Educational attainment is known to be a biased and inadequate metric in communities such as the one targeted in our research, where low levels of education observed likely correspond to extreme educational inequality in rural communities in South Africa during the apartheid era, when these individuals were young20,31,96,97. Having a set of well-defined traits that are population-standardised provides more accurate phenotype distributions for isolating variants associated with cognitive traits, as well as mitigating stigma attached to traits labelled inappropriately as intelligence. The use of traits like g fails to capture the variation observed in the actual trait vs that for g itself62. The age of the sample population was a strength as the literature states that the heritability of cognitive function changes across the lifespan and that trends between domains differ progressively with age, but stabilise at older ages4,11. Despite being limited by sample size, this study replicated previous genome-wide significant signals using sliding windows mostly based on studies that were performed in populations with European ancestry, informing the need for larger African cohorts where genomic and cognitive data have been collected.
The AWI-Gen/HAALSI collaboration is a trailblazer for genetic studies on neurocognitive traits in South and sub-Saharan Africa with evidence of novel associations and replication of previous associations. Larger continental African cohorts with genomic and cognitive screening data would increase the power to detect and replicate findings in other population studies, as well as provide an African cohort to use for replication of our work. Additionally, functional magnetic resonance imaging (MRI) results from this same cohort could be used to find signals linked to specific biological pathways or regions of the brain. Incorporating the OCS-Plus in future African studies may serve to establish usable datasets for monitoring cognitive health in Africa at this stage of rapid health and social transition. *The* generation of genomic data alongside such data will contribute to a greater understanding of how variation in African populations influences cognitive function.
## Participants
Participants were enrolled in both the AWI-Gen and HAALSI studies. Ethical approval was granted through the University of the Witwatersrand, Johannesburg, Human Research Ethics Committee under the following certificate numbers: AWI-Gen M121029 and M170880; HAALSI M141159; and the current study M170916. Socio-demographic data, infection history, and cognitive performance data were collected from 5059 consented participants (male ($$n = 2345$$) and female ($$n = 2714$$)) aged 40 years and older recruited from Bushbuckridge, Mpumalanga (November 2014 to November 2015) and a sub-set of 2246 of these participants (male ($$n = 935$$) and female ($$n = 1311$$)) had genotype data. All participants provided written informed consent. Descriptive statistics was performed using R (R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. Austria. https://www.R-project.org/).
## Questionnaire-based cognitive assessment
The United States Health and Retirement Study (US HRS) cognition screening tool was culturally adapted and translated into the local vernacular Shangaan (also referred to as Xitsonga). This tool consisted of questions representing the domains of memory and orientation, and was scored from 0–2431,36,96.
## Tablet-based cognitive assessment
The Oxford Cognition Screen Plus (OCS-Plus) is an electronic cognitive assessment administered using a tablet and was validated for use in this cohort20. It consists of nine domain-specific cognitive tests which assess language, episodic memory, executive function, attention, and pattern recognition20. A factor score was derived for each cognitive domain (episodic memory, executive function, language, and visuospatial ability)31. This method is based on Seidlecki, Honig, and Stern [2008], and produces population-standardised domain z-scores for each participant31.
## Genotyping and imputation
Genotyping of the full AWI-Gen dataset (10,900 participants) was performed using the H3Africa array by Illumina (San Diego, CA, USA). This custom array of ~2.3 million SNPs was developed to be enriched for common African variants (http://chipinfo.h3abionet.org)98. Data from AWI-Gen were processed through the H3A GWAS pipeline (https://github.com/h3abionet/h3agwas), where individuals with SNP missingness greater than 0.05 were removed from the dataset99,100. SNPs were removed if they had genotype missingness above 0.05, minor allele frequency (MAF) below 0.01 and were not in Hardy–*Weinberg equilibrium* (HWE) $p \leq 1$ × 10−6. SNPs were matched to Genome Reference Consortium Human Genome build 37 (GRCh37) and ambiguous SNPs were removed99,100. The 1.71 million SNP dataset was then imputed using the African Genome Resources reference panel at the Sanger Imputation Server98. EAGLE2 was selected for the pre-phasing and positional Burrows–Wheeler transformation (PBWT) algorithm for imputation. Poorly imputed SNPs with info scores (generated by the Sanger Imputation Service: https://www.sanger.ac.uk/tool/sanger-imputation-service/) of less than 0.6, with MAF below 0.01 and HWE p value cut-off <10−6 were excluded, and the final dataset included ~14 million SNPs. The info score is an indicator of the certainty of imputation and is a score between 0 and 1, with scores closer to 1 being more accurately imputed. The AWI-Gen HAALSI samples were extracted from this dataset.
## Population structure and affinities
Principal component analysis (PCA) using EIGENSTRAT101 was performed to assess population stratification within the samples as well as to find the genetic affinities of our cohort to other African ancestry populations from the 1000 Genomes Project (1000 G Project) dataset102. A cut-off of ±6 standard deviations (SD) was applied to the first five PCs resulting in the removal of 35 population outliers. The sample size for further analysis was then 2211 individuals. In Fig. 6a, little evidence of population heterogeneity was shown and the PCA with other African Ancestry populations from the 1000 G Project102 datasets showed a distinct clustering from East, West and Central-West African populations, and African Americans (Fig. 6b).Fig. 6Population structure and affinities of the HAALSI/AWI-Gen participants. Principal component analysis (PCA) of individuals from the HAALSI/AWI-Gen showing PC1 and 2. a shows the absence of any major population structure after the removal of individuals outside of the 6 SD cut-off at five PCs. b shows a PCA comparison of our study participants prior to removal of outliers with African population datasets (African Caribbeans in Barbados (ACB) and Americans of African Ancestry in SW USA (ASW)), East (Luhya in Webuye, Kenya (LWK)), and West Africans (Yoruba in Ibadan, Nigeria (YRI), Gambian in Western Divisions in the Gambia (GWD), and Mende in Sierra Leone (MSL)) from the 1000 Genomes Project.
## Statistics and reproducibility
A GWAS was performed for each of the five cognitive phenotypes. The total cognition score was captured for the entire cohort, whereas the OCS-Plus was administered to a subset of individuals. Only individuals with accompanying genomic data were included in our study sample. Total cognition was used as a continuous trait with scores ranging from 0 to 24 ($$n = 2211$$). Cognitive domain scores for 1887 individuals from the OCS-Plus were rank normalised using R (https://www.R-project.org/) as standardised z-scores were not normally distributed. The association was performed on the full imputed dataset using Genome-wide Efficient Mixed-Model Association (GEMMA)103 (https://github.com/genetics-statistics/GEMMA#gemma-genome-wide-efficient-mixed-model-association), adjusting for five PCs, age as a continuous covariate, sex, and highest level of education attained (primary, secondary, tertiary) as a categorical covariate. GEMMA was developed to perform quick association tests through univariate linear mixed models in order to correct for population substructure as well as cryptic relatedness103. LD scores from the 1000 G Project African reference panel and a reference panel specific to AWI-Gen’s SA data were used to adjust for LD structure99,100. Analyses were run on an automated H3Africa workflow for GWAS (http://github.com/h3abionet/h3agwas/)99,100.
## Visualisation and post-GWAS analysis
Association output files from GEMMA were analysed using Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) (https://fuma.ctglab.nl/) for partitioning of signals based on LD, visualisation and functional annotation104. Genome-wide significance (5 × 10−8) was input for analysis and the cut-off used for suggestive signals was 5 × 10−6. Manhattan plots and QQ plots for both SNP and gene-based association were generated using FUMA and R packages. Genomic inflation factors were calculated using a local R script. Locus zoom plots105 were created for selected association signals based on the summary statistics from GEMMA and SA-specific LD panel99,100. Kruskal–Wallis plots were constructed for comparison of cognitive function between individuals by genotype at each SNP99,100. GWAS Catalogue (http://ebi.ac.uk/gwas/) and Phenoscanner v2 (http://www.phenoscanner.medschl.cam.ac.uk/) were used to infer previous associations of the lead SNPs. We also studied previous associations in 100 kb genomic regions on either side of each lead SNP [accessed 10 October 2022]. Ensembl106 and literature mining were used to assess the functional interpretation.
## Replication
Considering the low likelihood of being able to replicate the individual genome-wide and suggestive association signals observed in our study, due to limited power and differences in LD between our study sample and European population-based cohorts, we employed a window-based approach similar to a study by Kuchenbaekar et al.107. Window-based replication was performed utilising add-ons from the H3A GWAS pipeline with a P value cut off of $p \leq 1$ × 10−399,100. This cut-off was decided on the basis of empirical estimates from another study on South African populations by Mathebula, et al.108. Loci reported, either reaching genome-wide significance or those reported as suggestive, in previous studies of traits determined either by the similarity of methods of data collection, domain-specific tasks, and educational attainment as a proxy were prioritised for this method of replication.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04636-1.
## Peer review information
Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Kaoru Ito and George Inglis.
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|
---
title: Reshaping healthcare with wearable biosensors
authors:
- Aaron Asael Smith
- Rui Li
- Zion Tsz Ho Tse
journal: Scientific Reports
year: 2023
pmcid: PMC10043012
doi: 10.1038/s41598-022-26951-z
license: CC BY 4.0
---
# Reshaping healthcare with wearable biosensors
## Abstract
Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit, which analyzes the data and provides feedback of the health value to the user through the computer. This is the working principle of wearable sensors for health. This article focuses on wearable biosensors used for healthcare monitoring in different situations, as well as the development, technology, business, ethics, and future of wearable sensors for health monitoring.
## Introduction
As the worldwide aged population has grown in recent years, the medical sector has been confronted with a new challenge: how to assist the patients in achieving health monitoring. Biosensors that enable real-time health monitoring, prevention, and treatment have been the emphasis in recent years. As smart wearable devices have become more popular, they can now be used to predict disease, and people improving their health beyond exercise through wearable devices will become a new trend1. Wearables are a burgeoning market, and their capabilities have evolved from tracking steps to monitoring medical issues and enhancing general health2.
Figure 1 shows a conceptual demonstration of a remote monitoring system, from which wearable sensors are used to collect data on the wearer's physiological indicators to monitor the patient's own physical condition in real-time. The patient's data is passed on to the mobile device through wireless communication, and then the data is passed to the remote center via the Internet. When the system detects a patient's sudden condition, such as a fall, it sends an alert message to the emergency service center to provide timely assistance to the patient. In an emergency, the data-linked family members and caregivers are also alerted to each other to let the relevant personnel know the status of the current patient. Figure 1Schematic diagram of a remote health monitoring system based on wearable sensors3. Data on physical health is collected through the sensors worn and transmitted to healthcare workers through networked devices. Healthcare workers can use this data to take an action.
The major purpose of these sensors is to assist users in monitoring their own health and to provide timely warnings to customers who are ill. Wearable sensors have the main advantage of allowing consumers to conduct scientific health monitoring anywhere and at any time. Because the user visits the clinic for a diagnostic and the doctor utilizes the external sensor to make a diagnosis in a specific location, the user can save a lot of time if the portable sensor is worn. The advantages of wearable sensors for users are more prevalent, especially for users who use a range of portable sensors. For patients with chronic diseases, wearable devices can reduce the number of hospitalizations, because they can adjust their daily routines and diets in time according to the display of device data, which can save expensive treatment costs. Wearables can replace medical gear such as ECG gadgets and defibrillators by precisely measuring body temperature and heart health, which can help hospitals save money on equipment3. In addition, wearable devices can help patients better manage their lives and health, so that patients can understand their current physical condition in real-time through the device so that they can be hospitalized in a timely manner. People are paying more attention to their own health as their living standards improve, thus people’s attention to the heart, blood oxygen monitoring, sports and fitness, and related equipment has expanded dramatically4.
Sensor detection for a variety of chronic and acute disorders has been studied since the nineteenth century. The pacemaker is the first sensor for monitoring human health by sending electrical impulses to the heart to allow it to beat more consistently5. Humans began to utilize wearable sensors for health purposes as early as 19606. According to research by Jeffrey and Parsonnet, the first pacemaker was implanted in a 40-year-old patient in 1958 by thoracic surgeon Erik Senning of Karolinska Hospital in Stockholm, who also implanted a pulse generator with rechargeable NiCd batteries6. The pacemaker's progress much like other devices has been amazing and swift.
People are becoming more enthusiastic about the future of health-related sensors. Different wearable health sensors7–9 and implantable sensors10–12 as well have been created and utilized since then. When the sensor is functional, it is important to know how to acquire the patient's health information and new technology with phone apps are helping make that possible.
In recent years, the face of wearables has shifted dramatically, with researchers shifting their attention from tracking people's regular exercise routines to addressing important difficulties in healthcare applications such as diabetic patient management and geriatric remote monitoring. To meet these big obstacles, scientists have focused their efforts on developing wearable biosensors, which are defined as sensing devices that incorporate biometric aspects into sensor operations, such as enzymes, antibodies, cell receptors, or organelles.
The sensor unit, first and foremost, is a required feature of all modern smart gadgets and comprises of many circuit boards that assemble sensors. The data collected by the sensors is sent to a central processing module, such as a personal digital assistant (PDA). The acceleration sensor is mounted on a pair of pants, and the circuit board is connected to a laptop in Laerhoven and Cakmakci's experiment. The circuit board will send data from the acceleration sensor on the user's pants to the laptop when the user is exercising13. For a long time, Lee and Mase have used sensor units to detect user behavior; they use a gyroscope, an angular velocity sensor, and an acceleration sensor to detect user behavior; these sensors are connected to a Linux PDA14, which is a sensor unit; the hardware for data processing is smaller than that of a notebook, and meets the standard of portability of the monitoring system and does not interfere with normal life which places a crucial role in sensor device design.
Sensors can be physically attached to the body, via avenues of standard clothing, watches, mobile phones, and other items that can be embedded with sensors in daily life. Flexible sensors are these types of sensors, and today's advances in printed electronics and materials allow some tiny sensors to be worn as skin patches15. These biosensors can measure skin conductance, heart rate, and body temperature, as well as detect changes in pH, glucose, and salt in the human body16,17 (Table 1).Table 1 Sensing capabilities commonly found in wearable devices. SensorDescriptionPrivacy invasiveness26Accelerometer xMeasures the acceleration force that is applied to a deviceLow26Magnetometer xMeasures the geomagnetic field strengthLow26Gyroscope xMeasures a device’s rate of rotation around each of the three physical axes (x, y, and z)Low27Ambient lightMeasures ambient light levelLow28Proximity xHow far away an object is from the phone’s screenLow26Touch state xRecords movement, pressure, and size of screen touch interactionMedium29Screen stateRecords every time the screen is turned on—offMedium30VideoCaptures video and picturesHigh26GPS xProvides user location coordinatesHigh28Wi-Fi xProvides data about the BSSID and signal strength of the nearby Wi-Fi access pointsHigh31Cell towersProvides information aboutthe nearby cellphone towersHigh28Bluetooth xDetects nearby Bluetooth capable devicesHigh28Ambient temperature xMeasures the ambient room temperatureLow26Pressure xMeasures the ambient air pressureLow32Galvanic Skin ResponseMeasures electrical conductance of the skinMedium26Electrocardiogram xMeasures heartrate activityMedium28Skin temperature xMeasures the temperature of the skinMedium Smart gadgets can be used to track data with greater accuracy, thanks to advancements in miniaturization technology. They can be woven into clothing as in-textile electronics, implanted into our everyday necessities, or even buried in the human ear. One of the most promising areas for wearable healthcare technologies right now is remote patient monitoring18. Wearable smart goods have also evolved from counting steps to monitoring physical health, providing healthcare staff with vital information such as blood pressure readings and potential arrhythmias.
Regarding smartphones, their role has expanded to include not only monitoring health but also carrying out simple, right, and healthy life interventions for users19,20. Some programs can also provide simple remedies21, such as mental diseases, because of the high frequency of daily use of smartphones and the high degree of user dependence. According to market research firm Counterpoint, the smartwatch market's future development focus is still fitness and health applications, therefore blood oxygen monitoring functions are common in existing wearable devices3,22. On another note, breast cancer has always been the leading cause of cancer death in women, and Cyrcadia Health, an American medical biosensor company, has been working with Jabil Medical Technology to develop a bra that can detect breast cancer, hoping to provide users with an early diagnosis of breast cancer. The application of this healthcare technology also marks another trend of change in wearable devices, from wrist-worn devices to body-wearable devices. Smart clothing built for specific conditions has been shown to be more practical, comfortable, durable, and reliable than smartwatches or bracelets23. Smart clothing built for specific conditions has been shown to be more practical than smartwatches and bracelets as certain demographics of the population, including children, the elderly, and individuals with chronic illnesses as well as mental health disorders, may find electronically embedded clothing to be especially helpful. Clothing could be designed ergonomically to fit the wearer’s body shape; the functionality could be maximized without negatively intrude the user’s personal space24.
Figure 2 shows the thought process of developing wearable devices and their sensor designs. The figure illustrates seven potential types of wearable devices ranging from smart garments to implants and these devices are all dependent on the sensor device design. Every psychological signal has a different sensor that can detect and measure the signal; therefore, the sensor design application will dictate the wearable form that the device will become. Not only do the sensors matter but the materials used for the biosensors are also taken into consideration like biocompatible wearable sensors or even self-healing flexible wearable sensors. Currently, more biosensors are becoming miniaturized and wireless as technology is advancing. This will be discussed later in the report25.Figure 2Application principles and categorical examples of wearable biosensors25.
## Wearable sweat biosensor
Because skin covers so much of our body, skin-worn devices get a lot of attention in many types of wearable biosensors. Real-time analysis of biomarkers in biological fluids and real-time monitoring in several biomedical and fitness applications are possible using epidermal biosensors. Optical, electrochemical, and mechanical are common transduction modes used in skin-worn biosensors that bind to biocatalytic and ion recognition receptors33–35. Most of the reports in recent years have focused on electrochemical and colorimetric transduction methods that have led to significant advances in skin-wearable devices, allowing for easy sampling of epidermal bio-stream so that the wearer does not feel uncomfortable, such as the use of electronic skin or printed temporary tattoos. In addition, miniaturization of sensors into wristbands or embedding sensors into clothing ensures close contact with the skin while allowing the sensor to withstand the forces generated during body movement. Sweat glands are distributed throughout our bodies and are the most readily available biological fluid in chemical sensing applications. Sweat production can be achieved by exercise, or naturally in an environment with a relatively high body temperature, and then by pressure or electroosmotic stimulation36. *In* general, sweat contains various metabolites (such as lactate and urea) and minerals, and these biosensors perform a large amount of original humoral analysis for noninvasive monitoring of physiological health status and disease diagnosis management37. The epidermis is monitored noninvasively, which removes the issues associated with blood collection while still preserving the integrity of the protective stratum corneal skin. More research trials are needed, however, to show sweat's clinical utility as a diagnostic biofluid36. Because sweat analytes are mostly delivered into sweat via capillaries, establishing a clear and accurate link with blood concentrations is difficult. Here it is possible to monitor the rate change of sweat by simultaneously monitoring an analyte or skin impedance measurement of concentration distribution independent of sweat rate35,38. But there is also a problem here, the degree of dilution of the analyte during the excretion of sweat is affected by the rate of sweat and the rate of analyte distribution. Also, the epidermal biosensor has a target for measuring the concentration of analytes in the interstitial fluid (ISF). Within the active skin tissue of an organism, skin cells are surrounded by ISF, which provides nutrients, so there is a reliable correlation between blood and the concentration of ISF in many analytes. To evaluate ISF analytes in a non-invasive manner, these components must be extracted onto the surface of the skin, which can be done by reverse ion electroosmosis or ultrasonic electroosmosis. However, these methods are still flawed, and their accuracy will be affected by changes in extraction efficiency and skin surface contamination. Solving these problems requires advanced sampling methods and improvements to analyte monitoring methods38.
The epidermal wearable biosensor was first designed for detecting a single analyte for a variety of target analytes. This proof of concept is carried out using new compression-resistant materials and sensor structures to achieve a high degree of skin fit. This is critical for reliable and stable sweat sampling during exercise, such as electronic skin or temporary tattoos, in combination with screen-printed flexible electrodes, providing an appealing platform for skin-based biosensors39,40. As the skin has appealing electrochemical characteristics, it is ideal to design the electronic skin or temporary tattoos being in direct, continuous touch, and consistent flexibility with the base skin. Tattoo-based epidermal biosensors have now been shown to monitor critical sweat electrolytes and metabolites in real-time and non-invasively41–43. The first demonstrated continuous monitoring of lactic acid levels in sweat via an epidermal electrochemical biosensor, provides a dynamic real-time graph of an organism's lactate sweat during exercise44,45. Sweat lactic acid is a by-product of the metabolism of local sweat glands in the skin. When the organism undergoes vigorous exercise, it leads to a higher production rate of sweat lactate. Although lactic acid does not respond immediately to contemporary blood levels, it does indicate the amount of physical exertion consumed during long-term exercise and can be utilized as a metric of exercise efficiency without invasive blood sampling. The subject of study was requested to wear an electronically printed temporary tattoo biosensor in the study. Lactate oxidase is added to the sensor to measure the sweat lactate generated during exercise. The measurements confirmed that sweat lactate did indeed increase with the intensity of exercise44.
Great progress has been achieved in quantitatively analyzing sweat multiplexed sweat biosensor platforms based on a fully integrated chip wearable sensor array. Noninvasive multiplexed sensing is appealing, but accurate and dependable monitoring systems are required. By combining numerous sensing arrays, a team may simultaneously measure sweat metabolites, electrolytes, and skin temperature. This is a ground-breaking strategy that has aided the development of wearable sensors by bridging the gap between signal transduction, data processing, wireless transmission, and system integration, allowing for the processing and sharing of raw data. This advancement in the wearable sector is attributable to the use of flexible chip sensors and conformal circuit boards, which can be accurately evaluated using physiological state data signals processed during lengthy body movements. Multi-analyte sensing also detects human sweat and is used to calibrate the analyte signal of interest to increase physiologically relevant qualities. Because the sensor relies on the experimental embodiment of physical exertion to create perspiration, the actual effect of continuous monitoring will be influenced by restrictions, according to the systematic results of the experimental report46.
Sweat glucose monitoring systems that combine pH, humidity, and temperature sensors have led to improvements in the therapeutic application of diabetes care. Currently, epidermal biosensors confront substantial obstacles in reliably measuring sweat glucose concentration, such as temperature variations and blood pH values affecting glucose concentration, as well as many sources of glucose contamination, irregular sampling rates, and low collection. Figure 3 shows a flexible epidermal sensor utilizing bio-microfluidics for blood glucose monitoring developed by Pu et al.47.Figure 3Flexible epidermal bio-microfluidic blood glucose continuous monitoring sensor47. ( A) Proposed device. ( B) The detailed structure of integrated epidermal biological microfluidic device. ( C) Flexible glucose detection patch. ( D) Temperature control component. ( E) Glucose detection patch structure. ( F) Working mechanism of integrated flexible epidermal biological microfluidic device.
Much of this research has found a link between the concentration of glucose in sweat and the level of glucose in the blood, however, the accuracy of using sweat detection for threshold glucose level would need to be evaluated by further studies48. Functionalized graphene has been integrated onto flexible serpentine electrodes to improve the electrochemical bio-sensitivity of sweat glucose, according to research41. Although this operation can continually monitor and modify measurement results based on changes in immobilized enzyme activity to increase data accuracy, it can't overcome the problem of exogenous glucose sources interfering with the data. Figure 4 illustrates an iontophoretic biosensor that detects both sweat alcohol and glucose in the ISF developed by Kim et al.49. The electrode is a screen-printed tattoo fixed to flexible wireless electronics which utilizes iontophoretic extraction caused by the movement of cations from transdermal pilocarpine delivery. This development is able to sample two epidermal biofluids simultaneously using a cost-effective technique of screen printing and has shown promise in measuring sweat-alcohol and ISF-glucose levels. Figure 4Epidermal iontophoretic biosensor49.
Gao et al. demonstrates the efficacy of patch-based sweat biosensors in managing glucose levels therapeutically46. However, the report requires the wearer to exercise to monitor and evaluate blood glucose variations and effective and consistent real-time blood glucose monitoring. This is impossible when the wearer is not exercising. As a result, further research into this sweat monitoring gadget for diabetes management is required.
A new multiplexed wearable sensing approach that combines electrophysiological measures with biochemical marker data is currently being developed46. This technology uses a single sensor to monitor physiological chemistry and electrophysiology simultaneously, eliminating the requirement for separate physical and chemical sensors for detection. With the introduction of this novel wearable sensing approach, sensors can now take a significant stride forward in their multimodal development. Real-time optical monitoring of several markers and real-time sweat sampling with a microfluidic integrated colorimetric sensing device are examples of signal transduction techniques that can be used. The rapid collection of sweat, based on this technique, will not impair sweat collection owing to evaporation, and it will also not pollute the sweat collection target, overcoming the problem of traditional methods' inaccuracy and unreliability. Rogers Group has upgraded its epidermal pigment metric sweat-sensing microfluidics device to allow for multiple continuous sweat marker measurements50. A new skin-wearable flexible sweat sampling microfluidic flow.
system has also been developed by a research team51. This novel microfluidic sweat monitoring technique integrates fluorescent probes into skin surface systems to provide precise electrolyte measurements in real-time. This optical sensing fluid technology has a sensitivity that is comparable to that of established laboratory procedures52.
In addition, rather than just sensing perspiration or ISF, epidermal biosensors may also monitor and analyze the skin's surface. A bandage-style biosensor has recently been shown to detect tyrosinase on the skin's surface. The electrochemical equation of the benzoquinone product of the enzymatic reaction is used to determine the level of tyrosinase. This bandage-type biosensor can currently screen for melanoma swiftly, which bodes well for the healthcare monitoring sector53.
## Epidermal biosensor based on ion electrogenic therapy
In biological monitoring, ionic electrometry is used to get non-invasive epidermal biofluids54. An appropriate current is applied to the skin to guide ion migration induced by friction between the skin and the sensor. Cygnus, for example, has created a wearable wrist-worn system called the Glucowatch Biographer55. This system provided for noninvasive long-term blood glucose monitoring, but it was taken off the market in 2000. The reverse ion electroosmosis method has been observed to cause skin irritation, and its application necessitates a lengthy preparation period. Williams et al. provided a review on a new ionic electroosmosis platform that employs a flexible tattoo platform that conforms to the human body56. It considerably lowers the physical discomfort produced by reverse ion electrogenesis, and it is also easily imbedded in the skin's surface without making the wearer feel uncomfortable when exercising.
To capture more ISFs, a positive-charged hyaluronic acid is added into the wearable device, which dramatically improves glucose transport to the epidermis of the skin57. The glucose is extracted, processed with reverse ion electroosmosis, and then analyzed with a conformal glucose oxidase biosensor. This method greatly improves the rate of glucose extraction in the gap fluid and allows for more precise detection of data related to blood concentrations, implying that improved monitoring applications based on non-invasive reverse ion electroosmosis therapy may be able to overcome the limitations of previous reverse ion electroosmosis. However, the efficiency of glucose obtained by reverse ion electroosmosis is difficult to maintain, which may cause the volume of the sampling ISF to be inconsistent, impacting the change in glucose concentration.
A glucose monitoring patch based on graphene pixels with path selection has been developed in recent years to make the analyte collected by reverse ion electroosmosis more consistent58. Graphene offers excellent electrical and mechanical capabilities, as well as flexible lamination and ultra-thin transparency, and does not hurt human skin, making it a perfect material for this patch sensor. Graphene films were used in patches based on micropixel-based sensor arrays, and reverse ion electroosmosis was used to directly collect and measure glucose in the interstitial fluid of cells through hair follicles, allowing blood glucose levels to be monitored continuously and dynamically for a long time. The utilization of minuscule sensors in the array to monitor changes in blood glucose extraction inside and between skins through individual hair follicles is at the heart of the device, which also enhances measurement accuracy.
The development of epidermal wearable biosensors has allowed them to be used to detect a variety of medications, such as a sweat-based wearable sensor that uses pilocarpine's ionic electroosmosis or exercise to create sweat from which to detect caffeine. The sensing platform is based on current scanning for direct anode detection of caffeine. This sensing system has a lot of potential for monitoring drug-drug interactions in vivo, and it can be further developed and researched in the future59. Despite recent advancements, the current epidermal biosensor is still limited to the study of a single biological fluid.
## Tear-based wearable sensor
Tear fluid is another organism that may be used to assess physiological health, and biomarker molecules in tear fluid diffuse directly from the blood, indicating a positive correlation with blood pressure marker concentrations. As a result of the analysis of tear fluid, it allows for the identification of eye problems. Tear fluid is a component of the eye's anti-fouling mechanism, and its composition is significantly less complex than blood's, making it a great candidate for non-invasive monitoring and diagnosis60–64. Tear fluid is primarily produced by lacrimal glands and conjunctival cup cells with the main component of tear fluid being protein, along with ions, glucose, and urea. It's worth noting that the glucose concentration in the tear fluid released by the tear glands is significantly associated with the blood glucose level. However, if the eye is subjected to external stimuli, and tear fluid is released in an aberrant manner, this link can be broken.
Because the sample size of the diagnostic tear sample is small, and it will be accompanied by evaporation during the collection process. *The* generation of individual tear fluid changes throughout the day as well as the difficulty of the collection method; it is very easy to affect the concentration of markers in the sampled tear fluid. As a result, the technique of collection, which is most typically a glass capillary or a Schirmer’s strip, has a significant impact on the accuracy of this in vitro tear diagnostic assay65. Reflex tears are tears produced in response to emotional or mechanical stimuli, and they differ from tears produced by natural secretions in terms of composition. The relevance of developing wearable tear sensing platforms that do not require eye stimulation is highlighted by these developments and obstacles.
Because contact lenses can be worn without causing discomfort to the eyes and can always keep in direct touch with the tears shed by the eyes, contact lens-based devices are appealing for resolving tear collecting66–69. Incorporating all the requisite biosensing, data processing, and power supply into contact lenses, on the other hand, is a big difficulty in contact lens design. Due to the rapid development of various materials for contact lens manufacturing, such as soft materials, a high degree of flexibility can be provided to minimize wearing discomfort caused by eye irritation while also providing the required oxygen permeability, improving the accuracy of continuous monitoring of tear glucose or metabolites. Simultaneously, another new method is proposed: using holographic contact lenses to quantify the potential of glucose in human tears in the physiological environment. This method has the advantage of not requiring power and the data is easy to read, allowing for continuous signal monitoring70.
The University of Washington's Department of Electrical Engineering's research team has made more progress in this area. The researchers investigated various biosensing methodologies to obtain high sensitivity and data accuracy, which allowed them to tackle the interference problem by using a dual sensor configuration. The device is further enhanced by the inclusion of a 2.4 GHz-based wireless read chip that employs far-field electromagnetic radiation71. Figure 5 illustrates a contact lens that monitors glucose levels developed by Lin et al.72. This development works by a reversible covalent interaction which alters the thickness of the contact. This thickness is then read by the camera of a smartphone and presents a noninvasive measurement for glucose levels utilizing glucose sensing via chemical interactions. Figure 5Tear-based wearable biosensors72.
Over years of hard effort, the Google research team has made great progress in their separate areas of electronic downsizing and applied medical technology, establishing a contact lens platform for tear glucose monitoring in conjunction with Novartis73. A wireless control chip, a microelectrochemical converter, an antenna, and an embedded hydrogel skeleton for non-invasive glucose measurement of surrounding tears are all included in this soft contact lens platform idea product. However, given the delays in clinical trials and subsequent commercial releases of this product, developing a high-performance technology platform based on contact lenses poses considerable obstacles.
By merging glucose contact lens sensors and intraocular pressure contact lens sensors using wireless technology74,75, a research team from the National University of Science and Technology of Ulsan in South Korea has developed smart contact lenses for wireless eye diagnosis. Despite the device's ability to multiplex sensing, the simultaneous operation of the two sensors has yet to be confirmed. Interference and biocompatibility between the two sensors will be thoroughly examined in future human trial investigations. Later, in stealth biosensor research, the researchers used integrated wireless display and wireless power transmission circuitry to enable the real-time display of the in vivo glucose response in rabbit tears. This cutting-edge technology is made of transparent and soft materials that ensure the wearer's comfort without obstructing eyesight. It requires no additional power supply because it is a wireless power module. However, further research is needed to show the feasibility of sensing performance in the human body and its capacity to track glucose fluctuations throughout the day.
A photon microstructure sensor was put on commercially available contact lenses by a research team at the University of Birmingham in the United Kingdom76. The smartphone records the varying reflected powers, which correlate to changes in tear glucose. This technology has been proven to respond to glucose quickly and accurately. This capacity makes it perfect for replacing electrochemically based contact lens biosensors, overcoming power supply and data transmission problems in tiny devices. A small electrochemical sensor that resembles a spring is worth mentioning in addition to the contact lens platform. Multiple spiral electrodes are coated with a protective polysaccharide-based hydrogel substance in this NovioSense-designed sensor. A continuous conduit of tear fluid is provided by inserting the device under the conjunctival vault of the eyeball. It does not cause discomfort to the wearer because it is positioned at the bottom of the eye, beneath the eyelids, and wireless data transmission can be utilized to continually detect tear glucose. It was found in a clinical trial that there was a robust link between tear glucose and blood glucose levels77.
## Saliva-based wearable biosensors
Saliva has been more popular as a diagnostic fluid in recent years as the number of oral disorders has increased. Many biomarkers in saliva enter saliva by direct cell-to-cell transfer in the human body, allowing saliva to represent the physiological condition of the human body and serving as an ideal noninvasive sampling method to replace blood analysis. Saliva is easy to collect and has a high protein concentration, making it ideal for biomarkers that will be used to monitor disease and stress in biomedical and health settings78–82.
Saliva is made up of three pairs of big salivary glands and a slew of small buccal glands scattered around the oral mucosa's surface. Saliva is a colorless, clear, viscous liquid with a mildly alkaline pH that is primarily made up of water with a little quantity of inorganic and organic materials. There are many kinds of inorganic compounds, and the main components are potassium ions, sodium ions, chloride ions, and bicarbonate ions, with potassium ions and bicarbonate ions having the highest concentrations. Although some of these saliva biomarkers can provide useful diagnostic information for clinical trials, there is currently limited study on dental wearable biosensors due to the large protein population in saliva and the risk of biocontamination from low quantities of biomarkers83–87. Despite the difficulties, oral biosensing platforms may gather dynamic chemical information from saliva in a non-invasive manner, which is an appealing method.
It is believed that the first wearable oral sensors were exhibited in the 1960s. It works with a topical denture platform to track chewing, plaque pH, and fluoride levels. However, it necessitates the sensor replacement of numerous teeth, and the internal sensor may cause intraoral leaking and is not widely used. The Princeton University study team improved on this concept by printing graphene nanosensors on water-soluble silk threads and transferring them directly to tooth enamel to enable passive and wireless monitoring of microorganisms, advancing oral biosensing technology research88. This idea product is intended to monitor bacteria on the teeth remotely, but it can also be used to monitor other saliva biomarkers82,88–92.
In vitro investigations have revealed a strong correlation between blood and salivary metabolite levels, paving the way for the creation of current oral saliva metabolite sensors, notably the link with wearing dental goggles93. By incorporating screen-printed enzyme electrodes into the device, a research team from the University of California, San Diego's Department of Nanoengineering produced an electrochemical biosensor of saliva metabolites (mainly lactic acid) in the form of a tooth guard82. Salivary lactate is closely related to blood lactate and can be used to determine physiological response and performance. Using lactase oxidase, the gadget can detect salivary lactate selectively. Electro-polymerization of o-phenylenediamine protects sample targets from contamination in undiluted saliva, allowing for continuous and noninvasive monitoring of the organism's health.
Another team also developed a uric acid biosensor in the form of a tooth guard that measures uric acid levels in saliva, allowing for noninvasive indirect monitoring of blood uric acid levels89. Uric acid in the blood is a biomarker for several conditions, including hyperuricemia, gout, and renal syndrome. This platform has demonstrated sensitivity, specificity, stability, and speed of reaction. We may now acquire dynamic chemical data on oral saliva indicators because of this. While these dental biosensing devices are appropriate for fitness or diagnostic applications, more standalone platforms are needed to broaden the variety of applications, such as continuous glucose monitoring in daily life.
A tiny and removable "cavity sensor" has been developed by multinational research teams from Japan and the United Kingdom94. The sensor surface is constructed of a GOx modified polyethylene glycol polymer with an integrated wireless transceiver installed on a bespoke monolithic tooth guard that fits the contour of the wearer's teeth for sensing salivary glucose. The strong relationship between blood glucose and salivary glucose makes glucose sampling a relatively convenient and accessible option. Before micro-wearable platforms may be considered for screening or monitoring diabetes via salivary glucose, more large samples of human research are required. Kacheon University in South Korea recently showed a dental platform-based wearable sensor95. Introducing biocompatible materials and RF sensors into the teeth to assess alcohol level, salt, sugar, pH, and temperature in saliva allows for wireless food monitoring while feeding. To achieve accuracy, the approach also necessitates a thorough examination of biomarker directional selectivity. Figure 6 illustrates a lab on a chip (LOC) based optical sensor that measures glucose in saliva. Although this fabricated model requires multiple components; it shows the feasibility of extracting a glucose measurement from saliva. This model also uses a photodiode and a light-emitting diode to obtain a light absorbance value which relates to glucose concentration96.Figure 6Saliva-based LOC biosensor96.
A coarse cellulose membrane is produced on the glucose sensor as an inhibitory interference membrane in the sensor project research, allowing glucose to be measured in saliva90. This eliminates the requirement for any preparation of human saliva, allowing for accurate measurement of glucose in saliva in the human body. Another oral monitoring gadget uses ultra-thin, retractable electrical devices and small sensors to enable remote wireless telemetry sodium intake97. Human experiments have shown that real-time salt consumption monitoring is possible, which is exactly what is required to treat hypertension. Naturally, the device's toxicity is now only assessed in the absence of a chemical sensor layer. A thorough study of the recognition layer's biocompatibility is required for practical oral applications. Overall, major studies of oral sensing platforms are still needed to ensure their safety and reliability in real-world applications. Equipment safety, as well as minimizing surface contamination produced by other saliva components and food crumbs, requires specific attention.
## Machine learning applications in wearable devices
Health monitors worn by consumers could save lives in the future. But how do you tell the difference between a crisis and a normal state in an emergency? Because the sensor has the potential for false positives, if there are too many people wearing it, nearby hospitals may frequently receive false positive information, wasting valuable medical resources. Rejab's paper from 201498 mentioned an approach that can guarantee a high degree of sensitivity while lowering the risk of false positives. Because the sensor can be continuously monitored and the sample data is sufficient, effective weights can be trained to evaluate the patient's condition, reducing the possibility of false positives, he stated that it is necessary to generate a similar general model based on the patient's underlying disease, and then train the sensor data through support vector machines, such as LASVM, ISVM, and K-prototypes clustering. It is possible that machine learning can not only filter out false signals, but also create artificial neural networks to train the data based on various types of sensor data to determine the health state of users.
Machine learning is crucial in the data processing and sensor modelling processes. Stetter's study focuses on evaluating external knee flexion and adduction moments in a variety of sports using machine learning and wearable sensors99. This approach, which is based on joint torque modelling and the position of wearable sensors, can successfully assist knee osteoarthritis (KOA) patients in evaluating their own exercise intensity100. Stetter's study included 13 volunteers who had two inertial sensing measurement units (IMUs) attached on their right thigh and calf while doing six different motor activities. The two sensing units can gather two sets of data at the same time: the external knee flexion moment (KFM) and the external knee adduction moment (KAM) (KAM).
Stetter creates a collection of physical models for artificial neural network (ANN) training based on IMU inputs, whole-body kinematics, and ground reaction forces. The findings of the trials show that using only two IMUs can provide a helpful biofeedback system for KOA patients to a limited extent, and that the IMU signal was low-pass filtered by Stetter (zero-phase Butterworth fourth-order filter). Then, the signal is input into the ANN model for training. After 1000 iterations of ANN training, the different weights of the five action tasks are obtained according to the maximum correlation between KFM and KAM. This allows the patient to get more accurate feedback after the relevant data is passed through the model again99.
However, Stetter's research did not specify the artificial neural network's training approach but based on the maximum correlation of the final training result, it may be assumed that the clustering algorithm and unsupervised learning were utilized. Stetter's training method has a potential flaw; unsupervised learning can be employed if there are too many neurons, but with only five action tasks, two IMU signals, and thirteen persons, this does not imply a massive neural network. Meta, on the other hand, does not require unsupervised learning. The data of thirteen people can be taught in batches, the neuron input layer is made up of the two IMU signals from one person's five action tasks, the hidden layer is a multi-vector machine (MLP), and the training method is supervised learning or reinforcement learning. You can receive more useful data with weights, not simply the most relevant data from the clustering method.
## Major challenges for designing sensors
Wearable sensors are currently hampered by several technical issues79,92,101. Human–computer interaction, intelligent sensing, and flexible electronics are all inextricably linked to the advancement and spread of sensing technology as the core technology of wearable devices. The size, quality, power consumption, dependability, and stability of sensors are critical for the user experience, wearability, and power consumption of wearable devices, among other things. Power supply, communication security, and privacy are also major concerns.
It's vital for wearable sensors to be accurate and trustworthy for them to be accepted in the market. The surface contamination effect, which is the key factor impacting the sensor's ongoing operation, has a significant impact on the accuracy of wearable biosensors. Robust anti-pollution surface protection, as well as dynamic calibration techniques including multimode, multi-marker sensing, and drift correction, are required to assure long-term reliability on the body.
Concerns like hardware, power supply, and communication challenges are crucial to the practical deployment of these sensing devices, and they are not restricted to wearable biosensors. Hardware components must be tightly linked with biosensor platforms and customized to meet the specific needs of the application. Because of their versatility and cost-effectiveness, printed wireless circuit boards with full-featured microcontrollers are commonly employed in wireless platforms. This printed circuit board can be connected to the battery in a variety of ways.
Another important criterion for wearables is minimal power consumption during continuous monitoring to offer the wearer or other end users with valuable and timely chemical information. When high sampling frequencies are necessary, this may necessitate a trade-off between energy usage and data rate. The importance of efficient data processing and effective and secure data exchange cannot be overstated. A lithium-ion or alkaline battery is the most popular means to power a wearable biosensing platform. They are, however, bulky and can cause toxicity issues, particularly in lithium-ion systems. Batteries are currently being made using flexible materials to improve wearability; however, it has yet to be demonstrated that the energy density is sufficient for long-term use.
## Major applications and applicational limitations
Oral biosensors that are based on saliva are likely to be heavily contaminated. Saliva includes far more proteins than noninvasive bodily fluids like perspiration or tears because it contains complex components. As a result, surface protective coatings for oral biosensors must be prioritized. To decrease the impacts of biological contamination and to eliminate simultaneous electrically active interference, sensor coating materials should be carefully chosen. At the same time, enzymes are applied to the sensor's surface to prevent the release of potentially harmful components.
Wearable biosensors, unlike traditional lab-based biosensors, can compromise the stability of fragile biosensors when exposed to long durations of outdoor activities in an uncontrolled environment. Multiplexed sensing technology, which includes biosensors and physical sensors, allows for active temperature, pH, and humidity calibration. In addition to potential contamination from the surrounding environment, mixing with stale bodily fluids, and continuous signal drift involving the calibration of the associated sensors, accurate measurements worn on the body necessitate careful attention to potential contamination from the surrounding environment, mixing with stale bodily fluids, and continuous signal drift involving the calibration of the associated sensors. Using proper microfluidic sampling methods and improving surface coating techniques can help address some of these issues.
Traditional micro supercapacitors have a "sandwich" stacked structure that makes them unsuitable for application in wearable devices, particularly flexible wearables, due to issues such low flexibility, lengthy ion diffusion distances, and complicated integration. Cheng et al. developed a solution to use a novel equipment architecture and integration technique to overcome the problem of micro supercapacitor stretching and bending in a snake-shaped island bridge configuration, resulting in a new micro supercapacitor array. The conductivity and amount of charged ions absorbed are improved by using a 3D laser to produce graphene foam102.
Smart watches, fitness wristbands, sleep trackers, and other smart health monitoring devices that are popular today are not safe, accurate, or intelligent, and do not provide a high degree of service to users. There are numerous security, reliability, and other challenges with these IoT portable sensors. We know from the usefulness that the altimeter, ambient light sensor, accelerometer, optical heart rate monitor, and other sensors currently advertised for smart bracelets that can be used for sleep monitoring are altimeter, ambient light sensor, accelerometer, optical heart rate monitor, and so on. The user's physical condition is assessed and based on the dark environment and low-amplitude motion, the user's time in bed and wake-up time are calculated. Tabia et al. conducted a study using data from the Fitbit blaze fitness tracker. They discovered that the daily data of the smart bracelet is independent of the data of the preceding and subsequent days after 8 months of data analysis40.
Inconsistent sleep quality measurements occur regularly and there is no way to create a learning model in this situation. This is because the sensor data is insufficient, and there is no way to acquire reliable sleep data using EEG. As a result, relying alone on the smart wristband to monitor sleep is incorrect, and relying solely on the smart bracelet will result in the loss of some data, i.e., the generation of incorrect data and the loss of right data.
A long-term dark environment and a stable body will cause the bracelet to believe that the user is sleeping at this time, but this is not always the case, such as in a movie theatre, where a long-term dark environment and a stable body will cause the bracelet to believe that the user is sleeping at this time. In current studies, each day's sleep is strongly related to the next few days, allowing users to predict if today's sleep has improved based on the prior day's sleep using machine learning models103.
Smart bracelets, on the other hand, have such a high level of uncertainty that their learning model is unreliable and untrustworthy, and some smart bracelets do not even use artificial intelligence. This distinguishes the sleep data obtained by the user from the sleep data tracked by PSG, as described by Kwon104. As a result, the user can only get a rough idea of how much sleep he or she gets, but no proper health advice. As a result, the smart bracelet cannot function as a true wearable health sensor.
Regarding power supply, wearable supercapacitors provide quick charging and discharging characteristics, as well as a low weight and energy density capacity. Some wearable power supply will collect energy to charge during the wearer's exercise, depending on the charging type of the wearable platform. Solar energy, the movement of devices based on piezoelectric or electrostatic principles, heat generation of thermoelectric materials, or the chemical composition of sampled biological fluids to power wearable biological fuel cells are all possibilities for wearable batteries. Wearable biofuel cells have promise as a source of electricity for non-invasive wearable platforms. It can be utilized as a self-powered biosensor and can harvest energy from the same biological fluids, but its stability is uncertain at this time. Wearable power supply advancements are critical, especially as the demand for power from multiplexed sensing platforms grows. It can be compensated for via adaptive algorithms that reduce energy demand, in addition to the development of powered and more energy-efficient equipment.
## Future directions for sensors
Researchers are currently attempting to build more advanced sensors to make wearable gadgets with more humanized functions in response to the power supply problem noted above105–108. A self-powered stretchy health monitor constructed of graphene material is one of the wearable sensors developed by researchers at Pennsylvania State University in the United States. Although self-charging power supply units for stretchable energy harvesters already exist, Cheng et al. claims that they are costly to make, cumbersome to transport, and have "poor and unsteady output power"102. Currently, stretchable conductors, semiconductor materials, and devices, as well as self-healing and biocompatible materials, are widely used in electronic skin physical sensing platforms, and much-related research is focused on the fusion of nanomaterials and MEMS processes. Carbon nanotubes, graphene, and other nanomaterials are examples.
The island bridge design created by Cheng's team employed non-layered ultra-thin zinc phosphide nanosheets and 3D laser-induced graphene foam, which not only allowed for efficient charge and discharge, but also improved the tensile properties of the micro supercapacitor array102. Cheng's team's development of the self-powered wearable relies heavily on the micro supercapacitor array. Because there is no chemical reaction between energy storage and release, supercapacitors with more than 100,000 charge and discharge durations are widely anticipated in the field of self-power, and many applications aspire to use supercapacitors to replace ordinary lithium batteries.
Another challenge is that most wearable biosensors currently only assess a small number of biomarkers. In the future, the industry should work to develop novel biosensor formats and improve non-invasive biosomal fluid sampling to monitor a larger range of biomarkers. Understanding the composition of each organism's body fluids, as well as their relationship to blood chemistry and specific medical illnesses, is crucial to gaining general clinical adoption of wearable technology in healthcare. A major indicator of its detection is the real-time correlation of marker levels in noninvasive humoral sampling with concurrent marker concentrations in the blood. In the actual world, rigorous and repeatable interpretation of biosensor results is also a goal, especially in applications that may require a clinical or operational reaction.
To identify novel biomarkers in the future, systematic and in-depth examination of the composition of each organism's body fluids will need to be performed, which has never been done previously in the context of wearable sensor research. Noninvasive testing can be expanded beyond the detection of a small number of metabolites and electrolytes, for example, by employing noninvasive immunoassays to assess a variety of protein disease indicators, hormones, and stress markers. Similarly, in addition to existing fluid types, potential from novel fluid types should be explored (urine, mucus, and semen). Other fields of biomedicine, such as the clinical development of new experimental medicines guided by biomarkers, will benefit from this real-time study of a wider spectrum of biomarkers.
To detect very low concentration biomarkers, wearable immune sensors require complex microfluidic devices with several stages and extended reaction times. It streamlines label-less detection techniques and has a lot of potential in healthcare, fitness, and a wide range of biocontainment applications. While most wearable devices focus on a single parameter, efforts should be made to monitor a wide range of biomarkers simultaneously and noninvasively. This more complete analysis not only allows for a more thorough examination of physiological states, but it also allows for dynamic calibration and correction of responses for more precise monitoring. Biosensors with multiple sensing methods for the same analyte can also be more reliable.
In the future, smartphone identification of mentally healthy patients will become more comprehensive, mainly thanks to reinforcement learning and self-supervised learning. For patients, a learning model will be continuously upgraded, generating personalized data based on the labels provided by the patient, which will give accurate answers through the doctor's recommendations.
The pacemaker through the radio frequency signal can effectively notify the clinic to give first aid to the guests, and through the machine learning algorithm, the misjudgment of the pacemaker can be reduced. In the future, according to the current technology, the pacemaker can work together with multiple sensors. Atrial fibrillation can be identified not only by the pacemaker, but also by the heart rate sensor and galvanic skin sensor of the smart bracelet. The inference is that when an artificial neural network is modeled for this, the other sensor can provide an emergency signal to the pacemaker when one of the sensors does not work. This can more safely ensure the health of the user.
## Conclusion
Wearables have been found to decrease sedentary behavior and increase overall health in users. Many companies in industrialized nations are already using wearable sensors to motivate patients to participate in rehabilitation programs and daily activities. Obesity and diabetes are two chronic diseases that can be prevented by living a healthy lifestyle. Furthermore, using wearable sensors to monitor patients can assist individuals and medical professionals in making shared decisions regarding future treatment plans. The artificial neural network will be trained and learned from the patient's bodily data, and the proper information will be given to medical professionals, which will be a key tool for encouraging patient adherence to physician prescriptions.
Patients can undertake self-data monitoring and potentially ameliorate symptoms of chronic diseases using wearable sensors for health, which can help them enhance their self-control. Patients, for example, can collect their own motion data on their cellphones using sensors such as light sensors, gyroscopes, and accelerometers, and then use software apps to diagnose and counsel themselves on mental illness. The sleep situation may be obtained using the smart bracelet and patch sensor based on the EEG, accelerometer, heart rate sensor, and light sensor, and then the sleep situation of today can be compared to the sleep situation of yesterday, and the patient's health state can be compared.
Wearable gadgets with real-time positioning, voice aid, and emergency assistance for Alzheimer's patients can considerably boost their independence, allowing medical staff to operate in multiple lines and increase efficiency. Patients with Alzheimer's disease can also free up more space with real-time positioning and voice help, as well as have emergency rescue functions without risking their lives. Simultaneously, there are numerous chronic disease signs that are difficult to identify in everyday life. Patients with mental illness and heart problems, for example, can receive input via the smart bracelet's heart rate sensor. In the future, there will be further development. Healthy living is an important development direction for wearable sensors.
People will trust smart gadgets with sensors if data transmission and privacy are ensured. Sensors will enable basic diagnosis and illness intervention for people in the future, making medical resources accessible to all. When consumers receive data from wearable sensors for health and believe that the feedback is of reference value, the patient's undiagnosed conditions can be targeted with appropriate interventions.
Wearable devices, because of the extensive application of materials, have profited greatly and developed swiftly because of the development of many technologies, but there are still certain obstacles to be overcome, and there is a lot of potential for progress. First and foremost, there is an issue with sweat detection equipment because it cannot be reused, and by increasing the outflow system, the equipment may be recycled. Second, while most wearable devices can detect continuously for minutes to hours, they cannot meet long-term wear requirements, so a superior combination of supercapacitors and other capacitive devices can be used to build a smaller and more durable power source. Wearable gadgets also have a lot of components, which might impair comfort while wearing them, and biocompatibility is another factor to consider. Wearable devices can increase their intelligence and lower their size to make them more pleasant to wear while integrating the natural biocompatibility and biodegradability of biomaterials to solve their safety difficulties, thanks to the development of wireless transmission and novel materials. Finally, while wearable device detection is currently limited to physiological health detection, as materials science, nanotechnology, communication technology, and biotechnology advance, it will become increasingly important to synthesize sensing elements with novel properties to broaden the detection range of wearable devices.
In conclusion, while wearable devices still have some issues, with the continued development of synthetic materials and detecting methods, as well as the progress of detection platforms and transmission technologies, wearable devices will have a brighter future.
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|
---
title: Early life gut microbiota sustains liver-resident natural killer cells maturation
via the butyrate-IL-18 axis
authors:
- Panpan Tian
- Wenwen Yang
- Xiaowei Guo
- Tixiao Wang
- Siyu Tan
- Renhui Sun
- Rong Xiao
- Yuzhen Wang
- Deyan Jiao
- Yachen Xu
- Yanfei Wei
- Zhuanchang Wu
- Chunyang Li
- Lifen Gao
- Chunhong Ma
- Xiaohong Liang
journal: Nature Communications
year: 2023
pmcid: PMC10043027
doi: 10.1038/s41467-023-37419-7
license: CC BY 4.0
---
# Early life gut microbiota sustains liver-resident natural killer cells maturation via the butyrate-IL-18 axis
## Abstract
Liver-resident natural killer cells, a unique lymphocyte subset in liver, develop locally and play multifaceted immunological roles. However, the mechanisms for the maintenance of liver-resident natural killer cell homeostasis remain unclear. Here we show that early-life antibiotic treatment blunt functional maturation of liver-resident natural killer cells even at adulthood, which is dependent on the durative microbiota dysbiosis. Mechanistically, early-life antibiotic treatment significantly decreases butyrate level in liver, and subsequently led to defective liver-resident natural killer cell maturation in a cell-extrinsic manner. Specifically, loss of butyrate impairs IL-18 production in Kupffer cells and hepatocytes through acting on the receptor GPR109A. Disrupted IL-18/IL-18R signaling in turn suppresses the mitochondrial activity and the functional maturation of liver-resident natural killer cells. Strikingly, dietary supplementation of experimentally or clinically used *Clostridium butyricum* restores the impaired liver-resident natural killer cell maturation and function induced by early-life antibiotic treatment. Our findings collectively unmask a regulatory network of gut-liver axis, highlighting the importance of the early-life microbiota in the development of tissue-resident immune cells.
Liver-resident natural killer cells develop locally and have multiple immunological roles in situ. Here the authors investigate the gut-liver axis and show the impact of the intestinal microbiota on the development of liver-resident natural killer cells.
## Introduction
The liver is a key and frontline immune organ, particularly enriched for innate immune cells including innate lymphoid cells (ILCs), natural killer T (NKT) cells, macrophages and γδ T cells1,2. These innate immune cells act coordinately to eliminate invading pathogens, as well as to maintain liver functional homeostasis3. ILCs accounts for about $5\%$ intrahepatic lymphocytes in mice and $25\%$ in human at steady state and majorly consists of conventional natural killer (cNK) cells and liver-resident NK (LrNK) cells, and the latter is also known as liver type 1 ILCs (ILC1s)4. Phenotypically, mouse LrNK cells can be identified as NK1.1+NKp46+CD49a+CD49b−, distinct from NK1.1+NKp46+CD49a−CD49b+ cNK cells4. Likewise, a CD56brightCD16lowCD49a+ LrNK cell subset was identified in human liver5. LrNK cells reside in hepatic sinusoid and exhibit significant differences in terms of development, phenotype and effector functions compared to cNK cells6. Firstly, although cNK cells develop from the progenitors in the bone marrow, LrNK cells are generated from liver Lin−CD122+CD49a+ progenitors via an interferon (IFN)-γ-dependent loop7. Secondly, the development and maintain of LrNK cells uniquely rely on a panel of specific transcription factors, including T-bet, PLZF, Hobit, AhR and RORα8–14. Functionally, LrNK cells produce higher level of tumor necrosis factor (TNF)-α, lower level of IFN-γ and perforin, and have similar levels of granzyme B as compared to cNK cells14. Moreover, LrNK cells not only participate in anti-tumor immunity14, but also mediate local immune tolerance15 and immune memory16,17. Thus, further unraveling the mechanisms by which the liver microenvironment supports the development and functional specialization of LrNK cells would provide insights for understanding the liver biology and developing therapeutic strategies for liver diseases.
The liver is at the nexus of host-microbial interactions with respect to its unique anatomical location, allowing continuous blood flow from the gastrointestinal tract through the liver sinusoids18. Importantly, accumulating evidence unraveled that gut microbiota plays crucial roles in the establishment and maintenance of liver immune homeostasis. Gut commensal and microbial products such as lipopolysaccharide (LPS) induce the sustained MYD88-dependent signaling, which in turn orchestrate the polarized distribution of Kupffer cells and NKT cells concentrating around periportal regions and optimizes the effective host defense19. On the other hand, commensal bacteria are critical for maintaining Kupffer cells in a tolerant state, preventing subsequent NKT cell over-activation during liver regeneration20. Moreover, commensal lipid antigens presented by hepatocytes is required for sustaining liver-resident γδT-17 cell homeostasis, including activation, survival and proliferation21. However, although germ-free (GF) mice showed either reduced or enhanced activity of circulating NK cells in different infection models compared to specific pathogen free (SPF) mice22,23, the importance of gut commensal in the regulation of hepatic NK cells remains unexplored.
Early life is a critical period for establishing a healthy gut microbiota which imprints the immune system and persists long even into adulthood24–26. However, by one year, up to $50\%$ of infants will have suffered from the microbiota dysbiosis, especially due to antibiotic exposure27. Strikingly, intestinal dysbiosis in infant mice impairs antibody responses to vaccines28. On the contrary, early life flora disorder causes the hyperactivation of intestinal macrophages and the enhanced inflammatory Th1 responses to bacteria stimulation, leading to the increased risk for inflammatory bowel disease (IBD)29. Likewise, mice treated neonatally with antibiotics develop exacerbated experimental psoriasis through increased IL-22-producing γδ T cells30.
Considering the special developmental pathway of LrNK cells in liver and the close interplay between gut and liver, we hypothesized that gut microbiota would have a profound impact on the homeostasis of LrNK cells. By using a maternal antibiotic treatment mouse model, we demonstrated that early-life microbiota depletion persistently blunted functional maturation of LrNK cells even at adulthood. Mechanistically, durative microbiota dysbiosis caused by early-life antibiotic treatment significantly downregulated hepatic butyrate level and IL-18 expression from Kupffer cells and hepatocytes, which in turn impaired functional maturation of LrNK cells. Strikingly, dietary supplementation of experimentally or clinically used *Clostridium butyricum* restored the impaired LrNK cell maturation and function induced by antibiotic exposure at early life. Our study uncovers an interplay network of gut-liver axis for LrNK maturation, highlighting the importance of the early-life microbiota in the development of tissue-resident immune cells.
## Early-life gut microbiota is crucial for functional maturation of LrNK cells
To determine the impact of gut microbiota on liver NK cell development, we used a maternal antibiotic treatment mouse model28,31,32. Briefly, dams and their pups were treated with combination of antibiotics (Abx, including ampicillin, vancomycin, neomycin and metronidazole), in their drinking water in late pregnancy and throughout the pre-weaning mouse infant period, and then fed with normal chow and drinking water (early-Abx) (Fig. 1a). Similar to the literature33, early-Abx treatment slightly reduced the mouse body weight (Supplementary Fig. 1a). We then characterized LrNK and cNK cells from control or early-Abx mice at weaning or at adult age (8-week-old). Gating strategy and the phenotype characterization of LrNK and cNK cells were shown in Supplementary Fig. 1b, c. There were no significant differences in the percentage and number of both cNK and LrNK cells in early-Abx and control mice (Fig. 1b, Supplementary Fig. 1d). Consistently, there were also no differences in the proliferation and apoptosis of LrNK cells (Supplementary Fig. 1e, f). Intriguingly, compared to those in control mice, LrNK cells in weaning or adult early-Abx mice had decreased mean fluorescence intensity (MFI) and percentage of CD11b and increased CD27 (Fig. 1c, d, Supplementary Fig. 2a), which was demonstrated to be a more immature phenotype34. Consistently, LrNK cells of early-Abx mice expressed relatively lower levels of kill cell lectin-like receptor subfamily G member 1 (KLRG1), a molecule associated with NK cell terminal maturation (Fig. 1c, d, Supplementary Fig. 2a). In addition, the expression of transcription factors related with LrNK cell development and maturation, Rorα and Zfp683, was also downregulated in LrNK cells of early-Abx mice (Supplementary Fig. 2b). In contrast to LrNK cells, cNK cells in early-Abx mice displayed comparable levels of CD11b, CD27 and KLRG1 with control mice (Supplementary Fig. 2c, d). Since LrNK cells, distinct from cNK cells, develop locally from Lin−Sca-1+Mac-1+ hematopoietic stem cells (LSM), which then differentiate into Lin−CD122+CD49a+ cells, as precursor cells for LrNK7, we thus detected the LSM cells and progenitor cells of liver and bone marrow. Results showed that there were no differences in the percentage and number in LSM cells and progenitor cells between control and early-Abx mice (Supplementary Fig. 2e–g).Fig. 1Early-life gut microbiota sustains the maturation and function of LrNK cells.a Schemes of early-Abx mouse model experimental design. b Representative FACS plots and bar graphs for the percentages and absolute number of LrNK cells from control and early-Abx mice (weaning mice: $$n = 6$$ per group; 8-week-old mice: control $$n = 6$$, early-Abx $$n = 7$$). c, d Representative FACS plots and bar graph for the expression level (MFI) of CD11b, CD27 and KLRG1 in LrNK cell subsets from weaning or adult control and early-Abx mice (weaning mice: control $$n = 6$$, early-Abx $$n = 6$$; 8-week-old mice: control $$n = 6$$, early-Abx $$n = 7$$). e, f Representative FACS plots bar graph and for the expression (MFI) of effector molecules in PMA and Ion-stimulated LrNK cells from weaning or adult control and early-Abx mice (weaning mice: $$n = 6$$ per group; 8-week-old mice: control $$n = 6$$, early-Abx $$n = 7$$). g Representative FACS plots and bar graph for the expression (MFI) of IFN-γ expression in IL-$\frac{12}{15}$ or poly(I:C) - stimulated LrNK cells from 8-week-old control and early-Abx mice ($$n = 6$$ per group). h Cytotoxicity of LrNK cells against CFSE-labeled YAC-1 cells. 7-AAD+ CFSE+ cells represented the killed cells (control $$n = 6$$, early-Abx $$n = 8$$). i Representative FACS plots and bar graph for the expression (MFI) of Tim-3, NKG2A and PD-1 in LrNK cells from control and early-Abx mice (control $$n = 6$$, early-Abx $$n = 7$$). Each symbol represents data from an individual mouse, and error bars represent SEM per group in one experiment. Data were analyzed using two-tailed Student’s t-test. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. All experiments were repeated two or three independent experiments. Source data are provided as a Source Data file.
Next, we investigated whether the function of LrNK and cNK cells was affected by early-life antibiotics treatment. Upon stimulation with phorbol 12-myristate 13 acetate (PMA) and ionomycin (Ion), LrNK cells of weaning or adult early-Abx mice displayed markedly lower production of effector molecules than those of control mice (Fig. 1e, f, Supplementary Fig. 3a). Reduced expression of IFN-γ were also observed in LrNK cells of early-Abx mice under stimulation of either IL-12/IL-15 or polyriboinosinic: polyribocytidylic acid (poly (I: C) (Fig. 1g). By using an in vitro flow-based killing assay, LrNK cells of 8-week-old early-Abx mice exhibited significant lower killing activities against Yac-1 cells than those from control mice (Fig. 1h). Moreover, early-life antibiotic exposure led to increased expression of inhibitory receptors Tim-3, PD1, and NKG2A in LrNK cells of weaning or adult mice (Fig. 1i and Supplementary Fig. 3b). Distinct from LrNK cells, there was no significant changes in the expression of effector molecules and inhibitory receptors, as well as cytotoxic activities, of liver cNK cells between adult control and early-Abx mice (Supplementary Fig. 3c–e). In addition, to further disentangle the effect of antibiotics exposure during pregnancy or breastfeeding period on LrNK functional maturation, we introduced utero-Abx or breastfeeding-Abx mouse model. Results showed that antibiotic treatment during pregnancy or breastfeeding period also impaired the maturation and function of LrNK cells (Supplementary Fig. 4a–f). These results strongly suggest that early-life microbiota sustains the homeostasis of LrNK cells.
It is well established in previous studies that blockade of NK cell maturation impairs their anti-tumor potential35,36. Most recently, the homeostasis of LrNK cells were demonstrated to be critical for suppressing liver tumor progression14. Thus, we wonder whether early-life antibiotics exposure also blunts LrNK-mediated anti-tumor effects. To address this, we introduced both c-myc/AKT-driven37,38 (Fig. 2a) and STZ-HFD-induced39 (Supplementary Fig. 5a) hepatocellular carcinoma (HCC) mouse model. It was found that early-Abx mice developed more severe liver tumors than control mice in both HCC models (Fig. 2b, c, Supplementary Fig. 5b, c). Accordingly, compared to those of control mice, tumor-infiltrating LrNK cells of early-Abx mice had reduced expression of effector molecules, decreased cytotoxic activities, but increased level of inhibitory receptors (Fig. 2d–f, Supplementary Fig. 5d). Importantly, depletion of both LrNK and cNK cells with anti-NK1.1, but not cNK depletion alone with anti-asialo GM1, almost completely abrogated the shortening of lifespan of HCC-bearing Abx mice. ( Fig. 2g, h). Together, these findings suggest that the impaired functional maturation of LrNK cells induced by early-life microbiota depletion contributes to the accelerating HCC development. Fig. 2Impaired LrNK function contributes to early-Abx enhanced HCC progression.a Experimental design of c-myc/AKT/SB100-induced HCC model in control or early-Abx mice. b, c In vitro tumor imaging and liver /body weight was shown ($$n = 5$$ per group). d Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in LrNK cells from control and early-Abx HCC mice ($$n = 5$$ per group). e Representative FACS plots and bar graph for the cytotoxicity of LrNK cells from control or early-Abx HCC mice against CFSE-labeled YAC-1 cells ($$n = 5$$ per group). f Representative FACS plots and bar graph for the expression (MFI) of TIGIT and Tim3 on LrNK cells from control or early- Abx HCC mice ($$n = 5$$ per group). g, h Experiment scheme and survival curves of control and early-Abx mice injected with AKT/Myc/SB100 plasmids and treated with anti-asialo GM1 or anti-NK1.1($$n = 5$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Statistical significance was tested by two-tailed Student’s t test (c–f) or Long-Rank test (h). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
## The persistent alteration of gut microbiota is responsible for LrNK cell maturation arrest in early-Abx treated mice
It has been demonstrated that the colonization of microbiota in early life has long-term impact on the diversity and stability of intestinal microbial communities. Thus, we wonder whether the persistent gut dysbiosis serves impaired LrNK cell maturation in mice with early-life antibiotics exposure. We analyzed gut microbiota using 16 S rRNA sequencing with feces from 8-week-old control or early-Abx mice. Feces of early-Abx mice contained significantly less α-diversity than the control group, indicated as decreased index of operational taxonomic unit (OTU) level (Fig. 3a). Microbial culture on blood agar plate and qPCR analysis for fecal bacterial DNA content and total 16 S rRNA also confirmed that early life antibiotic treatment had persistent alteration on the total abundance of gut microbiota (Supplementary Fig. 6a–c). Principal coordinate analysis (PCoA) plots also revealed significant discrete clustering in microbial community structure (β-diversity) of control and early-Abx groups (Fig. 3b). Further, linear discriminant analysis effect size (LEfSe) showed that the mean relative abundance of top 15 abundant bacteria was significantly different between control and early-Abx mice (Fig. 3c, d). Especially, some probiotic bacteria such as Akkermansia, Bifidobacterium, Prevotellaceae, Allobaculum were significantly reduced in early-Abx mice (Fig. 3c, d). These verify that early-life antibiotic treatment significantly alters microbial community composition in adulthood. Next, we co-housed early-Abx mice for 5 weeks with control littermates immediately after weaning (Fig. 3e), as previously reported30. As expected, co-housing treatment eliminated the differences in the expression of CD27, CD11b and KLRG1 in LrNK cells between control and early-Abx mice (Fig. 3f, Supplementary Fig. 6d). Consistently, there were comparable levels of IFN-γ production and CD107a mobilization of LrNK cells between cohousing control and early-Abx mice (Fig. 3g, Supplementary Fig. 6e). These findings suggested that the disruption of commensal microbiota by early-life antibiotic treatment accounts for the impaired functional maturation of LrNK cells. Fig. 3The persistent microbiota dysbiosis is responsible for maturation arrest of LrNK cells in early-Abx-treated mice.a–d 16 S rRNA sequencing was performed with feces from control and early-Abx mice (control $$n = 11$$, early-Abx $$n = 10$$). Alpha diversity comparison via chao index analysis and beta diversity comparison via PCoA analysis. c Genus level comparison of gut commensal microflora between control (red) and early-Abx (blue) mice. d LEfSe (linear discriminant analysis size effect) predictions for bacterial families in fecal pellets of control (red) and early-Abx (blue) mice were shown. e Experimental scheme of cohousing mouse model. f Representative FACS plots and bar graph for the expression (MFI) of CD27, CD11b, and KLRG1 on LrNK cells from the four groups of mice ($$n = 6$$ per group). g Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in PMA and Ion-stimulated LrNK cells from the four groups of mice ($$n = 8$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Statistical analysis was performed by two-tailed Student’s t-test (a), two-tailed Wilcoxon rank sum test (c), non-parametric factorial Kruskal–Wallis (KW) sum-rank test (d) or one-way ANOVA with Tukey’s multiple comparisons test (f, g). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
## Microbiota-derived butyrate facilitates functional maturation of LrNK cells
The gut microbiota can impact the physiology of the host by generating abundant commensal metabolites40. We, therefore, turned to evaluate the roles of microbiota-derived metabolites in the impairment of LrNK maturation in early-Abx mice. Liquid chromatograph-mass spectrometer (LC-MS) revealed the difference in abundance and clustering of fecal metabolites between 8-week-old control and early-Abx mice (Supplementary Fig. 7a, b). KEGG pathway showed the enrichment of differentially expressed metabolites in multiple metabolic pathways, including lipid metabolism, amino acid metabolism and carbohydrate metabolism (Supplementary Fig. 7c). Considering the importance of short-chain fatty acids (SCFAs) in regulating immune homeostasis, we further detected their levels in feces of control and early-Abx mice by Gas Chromatography-Mass Spectrometer (GC-MS). Results showed that several SCFAs, such as butyrate, were significantly reduced in feces of early-Abx mice (Fig. 4a). Notably, butyrate, but not acetate and propionate, had close correlation with the abundance of the dominant bacterial in early-Abx mice (Fig. 4b). Moreover, compared to that of control mice, feces of early-Abx mice had significantly decreased abundance of butyrate-producing microbiome like Faecalibacterium, Roseburia, Fusobacteria, and Eubacterium41 (Fig. 4c). These indicate that early-Abx treatment reduces the production of butyrate in adult mice. Fig. 4Microbiota-derived butyrate facilitates functional maturation of LrNK cells.a GC -MS analysis of short-chain fatty acids (SCFAs) was performed with feces from control or early-Abx mice (control $$n = 11$$, early-Abx $$n = 10$$). b The correlation heatmap between dominant bacteria and SCFAs in faeces through correlation numerical visualization. c The percentage of butyrate-producing bacteria on genus level from 16 S rRNA sequencing of faeces from control and early-Abx mice (control $$n = 11$$, early-Abx $$n = 10$$). The boxplot represents the median shown as a line in the center of the box, the boundaries are the first and third quartile, and whiskers represent the minimum and maximum values in the data. d Levels of butyrate in liver and spleen tissues from control and early-Abx mice by GC-MS analysis ($$n = 5$$ per group). e Experimental schemes of butyrate gavage mouse model. f Representative FACS plots and bar graph for the expression (MFI) CD27, CD11b, and KLRG1 on LrNK cells from different groups of mice ($$n = 6$$ per group). g Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in PMA and Ion-stimulated LrNK cells from different groups of mice ($$n = 6$$ per group). h Grouping according to butyrate level in normal liver tissue from hepatic hemangioma patients. i, j Representative FACS plots and bar graph of the percentage of CD27, IFN-γ and CD107a in LrNK cells from patients with high levels of butyrate and those with low butyrate levels. Dots represent data from individual mice or human patients, and error bars represent SEM per group in one experiment. Statistical analysis was performed by two-tailed Student’s t-test (a, d), parametric factorial Kruskal–Wallis (KW) sum-rank test (b) or one-way ANOVA with Tukey’s multiple comparisons test (f, g). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
Next, we wonder whether microbiota-derived butyrate is responsible for the maturation of LrNK cells. Interestingly, the content of butyrate in liver of Abx mice was significantly lower than that of control mice, while there was comparable level of butyrate in spleen of two groups of mice (Fig. 4d). Moreover, administration of butyrate (Fig. 4e) partially or completely recovered the expression of CD27, CD11b and KLRG1 in LrNK cells of early-Abx mice to the level of control mice (Fig. 4f, Supplementary Fig. 7d). In accordance, butyrate supplementation improved IFN-γ expression and CD107a mobilization of LrNK cells from early-Abx mice (Fig. 4g, Supplementary Fig. 7e). In addition, we analyzed the correlation of the maturation and function of LrNK cells with butyrate level in normal liver tissues from hepatic hemangioma patients. We found that, consistent with the mouse experimental data, LrNK cells from butyrate-high liver tissues had lower expression of CD27, but higher level of IFN-γ expression and CD107a mobilization than those cells from butyrate-low livers (Fig. 4h–j). Together, these results support the hypothesis that microbiota metabolite butyrate promotes the maturation of LrNK cells.
## Butyrate indirectly enhances LrNK cell functional maturation through acting on Kupffer cells and hepatocytes by GPR109A
To further determine whether microbiota-derived butyrate influences LrNK cell functional maturation in a cell-intrinsic or extrinsic manner, we transferred purified NK cells from adult control or early-Abx mice (CD45.2) into recipient CD45.1 mice and measured the activity of donor-derived LrNK cells after application of poly(I:C) (Fig. 5a). We found that transferred LrNK cells from control and early-Abx mice had comparable IFN-γ expression and CD107a mobilization (Fig. 5b). In contrast, when we transferred NK cells (CD45.2) separately into control or early-Abx mice (CD45.1) (Fig. 5c), donor LrNK cells in early-Abx mice had lower level of IFN-γ and CD107a than those in control mice (Fig. 5d). Consistently, ex vivo exposure to butyrate did not alter the IFN-γ and CD107a level of LrNK cells (Fig. 5e). These data suggest that early-life gut microbiota affects the functional maturation of LrNK cells in a cell-extrinsic manner. Fig. 5Butyrate indirectly enhances LrNK function through acting on Kupffer cells and hepatocytes through GPR109A.a, b Experimental scheme and FACS analysis of IFN-γ and CD107a expression in transferred LrNK cells from control or early-Abx mice (CD45.2) after injection of poly(I:C) ($$n = 6$$ per group). c, d Experimental scheme and FACS analysis of IFN-γ and CD107a expression in LrNK cells (CD45.2) transferred into control or early-Abx mice (CD45.1) after injection of poly(I:C) ($$n = 6$$ per group). e Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and TNF-α in LrNK cells were shown in DDW or butyrate. ( $$n = 6$$ per group). f Representative FACS plots and bar graph for the expression (MFI) of IFN-γ in LrNK cells in different groups were shown ($$n = 6$$ per group). g RT-qPCR analysis for GPR41, GPR43 and GPR109A expression level in LMNCs or hepatocytes ($$n = 6$$ per group). h Purified LrNK cells from C57BL/6 mice were co-cultured with hepatocytes infected with GPR109A shRNA lentivirus (Lv-shGPR109A), treated with DDW or butyrate. Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in LrNK cells were shown ($$n = 6$$ per group). i RT-qPCR analysis of GPR109A expression level in different immune cell subsets in liver ($$n = 6$$ per group). j Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in LrNK cells were shown co-cultured with Kupffer cells ($$n = 5$$ per group). k Purified LrNK cells from C57BL/6 mice were co-cultured with Kupffer cells infected with GPR109A shRNA lentivirus (Lv-shGPR109A), treated with DDW or butyrate. Representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in LrNK cells were shown ($$n = 6$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Data were analyzed using two-tailed Student’s t test (b, d, e, h, j, k) or one-way ANOVA with Tukey’s multiple comparisons test (f). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
Multiple cell components including hepatocytes and liver mononuclear cells (LMNCs) consists of the complicated liver microenvironment. To test their potential involvement in LrNK cell functional maturation regulated by microbiota-butyrate axis, we purified LrNK cells and co-cultured with hepatocytes or LMNCs with/without the presence of butyrate respectively. We found that butyrate treatment indeed increased IFN-γ production of LrNK cells when co-cultured with both hepatocytes and LMNCs (Fig. 5f). Butyrate could act either by inhibiting histone deacetylases (HDACs), or through G protein-coupled receptors (GPCRs), such as GPR41, GPR43, and GPR109a42–44. However, HDAC inhibitor, Trichostatin A (TSA), did not affect the upregulated expression of IFN-γ in co-cultured LrNK cells by butyrate treatment (Fig. 5f, Supplementary Fig. 8a). Then, we wonder whether GPCRs take effects in this process. RT-qPCR showed that although GPR41, GPR43 and GPR109A were all expressed in LMNCs, only GPR109A was detected in hepatocytes (Fig. 5g). Further, GPR109A knockdown in hepatocytes dampened the augmented IFN-γ and CD107a expression in co-cultured LrNK cells induced by butyrate treatment (Fig. 5h, Supplementary Fig. 8b). For LrNK cells co-cultured with LMNCs, the GPR109A agonist MK-0354, but not the agonists for GPR41 and GPR43 (AR-420626 and 4-CMTB), increased their expression of IFN-γ and CD107a (Supplementary Fig. 8c). Notably, among different subsets of LMNCs, GPR109A was highly expressed in Kupffer cells (Fig. 5i). Moreover, co-culture of LrNK cells with Kupffer cells upregulated the expression of effector molecules of LrNK cells with the treatment of butyrate (Fig. 5j, Supplementary Fig. 8d), while GPR109A silencing abrogated this promoting effect (Fig. 5k, Supplementary Fig. 8e). Together, these findings support the view that microbiota-butyrate axis indirectly promotes LrNK function by acting on GPR109A in hepatocytes and Kupffer cells.
## Butyrate triggers IL-18 production in hepatocytes / Kupffer cells to improve functional maturation of LrNK cells
To gain insight into the mechanism underlying the indirect action of microbiota-butyrate on LrNK cell functional maturation, we examined the expression of a panel of cytokines/chemokines in liver tissues from control or early-Abx mice. RT-qPCR analysis showed that the expression of IL-18 was obviously downregulated in early-Abx mice (Fig. 6a). In accordance, IL-18 protein was reduced in both liver and small intestine of early-Abx mice, while the level of IL-18 in liver was significantly higher than that in small intestine (Fig. 6b). Intriguingly, intraperitoneal injection of recombinant IL-18 protein (rIL-18) markedly rescued the expression of maturation markers, CD11b and KLRG1, as well as the level of effector molecules in LrNK cells from early-Abx mice (Fig. 6c, d, Supplementary Fig. 9a, b). These results suggest the critical role of IL-18 in the gut microbiota-mediated regulation of LrNK maturation. Fig. 6Butyrate triggers IL-18 production in hepatocytes / Kupffer cells to improve functional maturation of LrNK cells.a RT-qPCR analysis of indicated cytokines/chemokines expression level in liver tissues from control or early-Abx mice ($$n = 8$$ per group). b ELISA assay for IL-18 levels in livers and small intestines of control ($$n = 6$$ per group) and early-Abx ($$n = 5$$ per group) mice. c, d Representative FACS plots and bar graph for the expression (MFI) of maturation markers or effector molecules in LrNK cells from control or early-Abx mice with or without recombinant IL-18 treatment ($$n = 6$$ per group). e, f Representative FACS plots and bar graph for the expression (MFI) of KLRG1, IFN-γ and TNF-α or perforin in LrNK cells co-cultured with hepatocytes or Kupffer cells in the presence of butyrate and/or anti-IL-18 ($$n = 6$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Statistical analysis was performed by two-tailed Student’s t-test (a, b) or one-way ANOVA with Tukey’s multiple comparisons test (c–f). ** $P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
Then, we wonder whether microbiota-butyrate axis promotes LrNK maturation through upregulating IL-18 in liver microenvironment. Supplementation of butyrate increased IL-18 level in liver (Supplementary Fig. 9c). Consistent with the involvement of hepatocytes and Kupffer cells in butyrate-mediated enhancement of LrNK functional maturation, addition of butyrate upregulated IL-18 level in both hepatocytes and Kupffer cells ex vivo (Supplementary Fig. 9d, e). Importantly, blockade with specific IL-18 antibody (anti-IL-18) almost abolished butyrate-induced upregulation of KLRG1 and effector molecules in LrNK cells co-cultured with hepatocytes or Kupffer cells (Fig. 6e, f). These data suggest that IL-18 is crucial for the microbiota-butyrate axis mediated improvement of LrNK cell maturation.
## IL-18 / IL18R promotes LrNK maturation through improving mitochondrial oxidative phosphorylation
IL-18 has been well-known for its enhancement on NK cell activity45. However, little is known about its regulatory action and mechanism in LrNK cell maturation. Profiling of maturation markers disclosed that LrNK cells expressing IL-18 receptor α chain (IL-18Rα+ LrNK) had higher expression of CD11b and KLRG1 than IL-18Rα- LrNK cells (Fig. 7a). Accordingly, there were relatively high levels of IFN-γ and CD107a in IL-18Rα+ LrNK cells, when compared to IL-18Rα- subset (Fig. 7b). Further, transfer of IL-18Rα-deficient LrNK cells into control or early-Abx mice showed the comparable expression of IFN-γ and CD107a (Fig. 7c, d). These findings further support the hypothesis that IL-18 / IL18R axis accounts for LrNK cell maturation, which is disrupted by early-Abx treatment. Fig. 7IL-18 / IL18R promotes LrNK maturation through improving mitochondrial oxidative phosphorylation.a, b Representative FACS plots and bar graph for the expression (MFI) of maturation markers (A) or effector molecules (B) in IL-18Rα- LrNK and IL-18Rα+ LrNK in C57BL/6 mice ($$n = 6$$ per group). c, d Experimental scheme and representative FACS plots and bar graph for the expression (MFI) of IFN-γ and CD107a in IL-18Rα-deficient LrNK cells (CD45.2) transferred into control or early-Abx mice (CD45.1) mice ($$n = 6$$ per group). e Representative images for oxygen consumption rate (OCR) analysis of purified IL-18Rα+ and IL-18Rα- LrNK cells stimulated with IL-15 ex vivo and the basal respiration, maximum respiration and ATP production were analyzed ($$n = 6$$ per group). f, g Representative FACS plots and bar graph for the mitochondrial membrane potential, indicated by Mitotracker Red staining and mitochondrial ROS levels measured by MitoSOX in IL-18Rα- LrNK and IL-18Rα+ LrNK cells ($$n = 5$$ per group). h Representative FACS plots and bar graph for the expression (MFI) of KLRG1 and IFN-γ in purified IL-18Rα-LrNK or IL-18Rα+LrNK cells treated with DMSO or Rotenone ($$n = 6$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Statistical analysis was performed by two-tailed Student’s t-test (a–g) or one-way ANOVA with Tukey’s multiple comparisons test (h). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
To better understand how IL-18 / IL-18R facilitates the functional maturation of LrNK cells, we performed RNA sequencing for IL-18Rα- positive and -negative liver CD3-NK1.1+ cells. Analysis of this dataset identified 2420 differentially expressed genes (false discovery rate [FDR] < 0.05; 2-fold change or greater) (Supplementary Fig. 10a) and gene-set enrichment analysis (GSEA) revealed that transcriptional signatures associated with mitochondrial oxidative phosphorylation pathway was positively enriched in IL-18Rα+ CD3-NK1.1+ cells (Supplementary Fig. 10b). We, therefore, measured mitochondrial oxidative phosphorylation in IL-15 activated IL-18Rα+ and IL-18Rα- LrNK cells before and after the addition of Oligomycin (Oligo), fluoro-carbonyl cyanide phenylhydrazone [FCCP], rotenone [Rot] and antimycin A [AA]) (Fig. 7e). We detected a relatively high level of basal and maximal oxygen consumption rate (OCR) as well as ATP production in IL-18Rα+ LrNK cells (Fig. 7e). Further, flow cytometry analysis showed that IL-18Rα+ LrNK cells had higher mitochondrial membrane potential (MMP), indicated by Mitotracker Red staining (Fig. 7f), while lower mitochondrial ROS levels as measured by MitoSOX, than IL-18Rα- LrNK cells (Fig. 7g). Conversely, LrNK cells from early-Abx mice showed decreased MMP and increased mitochondrial ROS, when compared to those cells from control mice (Supplementary Fig. 10c, d). Intriguingly, inhibition of mitochondrial oxidative phosphorylation activity by Rotenone significantly decreased IFN-γ production and abolished the difference between IL-18Rα+ and IL-18Rα- LrNK cells (Fig. 7h, Supplementary Fig. 10e). Similar results were observed in LrNK cells from control and early-Abx mice (Supplementary Fig. 10f). Together, these findings indicate that IL-18/IL-18R enhances LrNK functional maturation through improving mitochondrial activity.
## Dietary Clostridium butyricum rescues the impaired maturation of LrNK cells in early-Abx-treated mice
Then we resorted to demonstrate whether dietary supplementation of butyrate-producing bacteria could recover the status of LrNK cells from early-Abx treated mice. Clostridium butyricum (C. butyricum), a butyrate - producing human gut symbiont that has been safely used as a probiotic for decades46, was administrated to early-Abx mice after weaning (Fig. 8a). Strikingly, C. butyricum treatment significantly decreased the expression of CD27, but increased CD11b and KLRG1 level, of LrNK cells from early-Abx mice (Fig. 8b, Supplementary Fig. 11a). In accordance, supplementation of C. butyricum enhanced IFN-γ production and CD107a mobilization of LrNK cells in early-Abx mice (Fig. 8c, Supplementary Fig. 11b). Moreover, hepatic IL-18 expression in C. butyricum treated early-Abx mice was almost recovered to the comparable level of control mice (Fig. 8d). Intriguingly, dietary supplementation of *Clostridium butyricum* Powder (2 × $\frac{106}{0.2}$ ml per day) (trade name: Baolean, Qingdao Donghai Pharmaceutical Co., Ltd), a commercially probiotic preparation which has been commonly used to treat infantile diarrhea in clinics, partially or completely recovered the expression of CD27, CD11b and KLRG1, as well as IFN-γ level, in LrNK cells of early-Abx mice (Fig. 8e, f, Supplementary Fig. 11c, d). Together, these results preliminarily suggest that dietary supplementation of C. butyricum is a potential intervention strategy for rescuing LrNK cell maturation after early exposure to Abx. Fig. 8Dietary *Clostridium butyricum* rescues LrNK maturation impairment in early-Abx treated mice.a Experimental scheme of the *Clostridium butyricum* (C. butyricum) gavage mouse model. b, c Representative FACS plots and bar graph for the expression (MFI) of maturation markers and effector molecules in LrNK cells from different groups of mice ($$n = 5$$ per group). d ELISA assay of IL-18 levels in liver tissues from different groups of mice ($$n = 5$$ per group). e, f Representative FACS plots and bar graph for the expression (MFI) of maturation markers and effector molecules in LrNK cells from different groups of mice ($$n = 8$$ per group). Dots represent data from individual mice, and error bars represent SEM per group in one experiment. Statistical analysis was performed by one-way ANOVA with Tukey’s multiple comparisons test. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$; ns, no significance. Source data are provided as a Source Data file.
## Discussion
Gut microbial development in the first year of life occurs concomitantly to the establishment of our immune system and forms an interactive signaling network47. LrNK cells reside in liver sinusoids and are supposed to be potentially regulated by commensal bacterial or their metabolites. In this study, we defined that early-life gut microbiota sustains the functional maturation of LrNK cells. Mechanistically, this regulatory effect is achieved through butyrate production and the subsequent IL-18 production by hepatocytes and Kupffer cells. Our data reveal a mechanism by which gut microbiota influence hepatic immune homeostasis and underlines the long-lasting effects ensuing antibiotics administration at early life.
LrNK cells develop from liver Lin−CD122+CD49a+ progenitors7 and then mature into CD27- population through CD27+ counterparts34. The local development model of LrNK cells clue that liver microenvironment may be critical for this process; however, the regulatory mechanisms remain largely unknown. Gut microbial and their metabolites from portal vein exhibit profound impacts on liver physiology and immune homeostasis. In the present study, we found that early antibiotics treatment hindered the maturation and anti-tumor activity of LrNK cells even at adulthood, while liver cNK and splenic NK cells remained unaffected. Co-housing treatment abrogated the inhibitory effect of early-Abx exposure on LrNK cells, indicating the decisive roles of gut commensal dysbiosis. This persistent impact of early-Abx treatment on LrNK cell maturation further substantiate the importance of early-life microbiota colonization on the development of immune system. Further, adoptive transfer experiments revealed that there was no LrNK-cell intrinsic defect in early-Abx mice. These insights highlight that early-life antibiotic exposure disrupts the liver microenvironment and in turn leads to the persistent maturation arrest of LrNK cells. However, the disparity in the regulation of LrNK and cNK cells by early-life gut microbiota needs to be further investigated and roles of other factors related with human early-life microbiota perturbations, such as way of birth, feeding, in LrNK cells regulation remains largely unknown. In addition, antibiotics exposure was reported to potentially link with the increased incidence of tumors, including HCC48, which is in compliance with the involvement of impaired LrNK cell function in HCC development14. It would be also interesting to further investigate whether early-Abx treatment-induced LrNK maturation hindrance correlates with the increased risk of human HCC.
Microbial metabolites are important mediators to modulate host immune responses by microbiota49. Of the many bacterial metabolites in the gut, SCFAs including acetate, propionate, and butyrate, are most abundant and have emerged as critical regulators of immune responses50. Here we found that early Abx exposure significantly reduced the level of butyrate in both feces and liver, but not in spleen. Furthermore, supplementation of butyrate or butyrate-producing symbiont, C. butyricum, rejuvenated the maturation and function of LrNK cells in early-Abx mice. Recently, butyrate has attracted great attention due to its versatile roles in immune regulation. Provision of butyrate in vivo not only boosted the antitumor CD8+ T cell response51, but also promoted the memory potential of antigen-activated CD8+ T cells52, and induced the differentiation of macrophages with potent antimicrobial function53. Unexpectedly, we did not detect an obvious improvement of butyrate on the function of purified LrNK cells ex vivo. However, the promoting effects of butyrate were recapitulated in LrNK cells co-cultured with hepatocytes or Kupffer cells. It was previously reported that the maturation of LrNK cells was greatly impaired in the absence of CD8+ T cells, but not CD4+ T cells or NKT cells34. Together with our findings, it is clearly demonstrated that liver microenvironment collaboratively supports the maturation of LrNK cells. Furthermore, here we emphasize the importance of gut microbiota in the regulatory network for LrNK cell maturation in liver microenvironment. However, apart from butyrate, early-Abx exposure prominently affected a varity of microbial metabolites lipid metabolism, amino acid metabolism and carbohydrate metabolism, all of which has been demonstrated to involve in regulating immune homeostasis54–56. The probability that other metabolites might also involves in this inhibition could not be ruled out.
A panel of cytokines have been reported to sequentially regulate the development and maturation of NK cells2. IFN-γ produced by LrNK cells was demonstrated to promote their own development from the progenitors7. Our transwell co-culture experiment of LrNK cells with hepatocytes or Kupffer cells indicated that the soluble factors involved in the improvement of butyrate-mediated LrNK cell maturation and function. In early-Abx mice, we detected the significant reduction of IL-18 in liver tissues, while butyrate replenishment fixed this deficiency in a GPR109A-dependent manner. Moreover, recombinant IL-18 protein rescued the maturation and function of LrNK cells in early-Abx mice. IL-18 has been well-known as a stimulator for NK cell activity45,57. IL-18R-deficient NK cells were unable to secrete IFN-γ in response to ex vivo stimulation with IL-12, indicating that IL-18 signaling is essential for NK cell priming58. In the liver metastatic colon cancer model, IL-18 is required for the suppression of Nlrp3 inflammasome on tumor growth and its promotion of intratumoral NK cell maturation and tumoricidal activity59. However, the exact roles of IL-18 in the functional maturation of NK cells remain elusive. Here, we found that compared to IL-18Rα+ LrNK cells, IL-18Rα- LrNK cells exhibited an immature phenotype, accompanied by relatively low mitochondrial activity. Moreover, inhibition of mitochondrial oxidative phosphorylation neutralized the functional difference between IL-18Rα+ and IL-18Rα- LrNK cells. It will be interesting to further interrogate the molecular circuits for IL-18 regulation on the mitochondria activity of LrNK cells. Furthermore, clinical studies showed that IL-18 has the ability to enhance the function of human liver NK cells60, and human IL-18 gene polymorphisms were reported to be associated with the risk and severity of HCC61. Thus, whether IL-18 deficiency also contributes to human LrNK maturation and HCC suseptibility induced by early-life dysbiosis would also worthy of further investigation.
In summary, the present study demonstrate that early-life gut microbiota sustains functional maturation of LrNK cells through finely modulating liver microenvironment in a butyrate/IL-18 dependent manner. Our results reveal a crosstalk between microbiota and immunity in gut-liver axis and provide a potential intervention strategy after antibiotic exposure at early life.
## Human samples
Human liver tissue included in this study were obtained from 10 hepatic (5 female and 5 male, aged: 26-65) hemangioma patients in Qilu hospital, Shandong University and Shandong Provincial Hospital from May 2022 to September 2022. All human tissues used in this study were approved by the Ethics Committee of Shandong University School of Basic Medical Sciences (ECSBMSSDU2019-1-41), and all patients provided informed written consent.
## Cell lines
The mouse cell line Yac-1 cells (BFN608006355) was donated by Shandong Academy of Medical Sciences and cultured in DMEM (Gibco) plus $10\%$ FBS (Gibco) and $1\%$ Penicillin-Streptomycin (solarbio). Cells were cultured at 37 °C in a constant temperature incubator with $5\%$ CO2.
## Bacterial preparation
Clostridium butyricum (C. butyricum) was supplied by the China General Microbiological Culture Collection Center and incubated in Reinforced Clostridial Medium under anaerobic conditions for 72 h at 37 °C. The bacteria were harvested by centrifugation (3000 g x 5 min) and resuspended in PBS to a final experimental concentration of 2 × 109 CFU/0.2 ml and gavage to early-Abx mice after weaning 2 × 109 CFU/0.2 ml 3 times a week until 8-week-old.
Clostridium butyricum Powder (trade name: Baolean, Qingdao Donghai Pharmaceutical Co., Ltd) was bought at ShuYu Civilian Pharmacy and gavage to early-Abx mice after weaning 2 × $\frac{106}{0.2}$ ml per day until 8-week-old.
## Experimental animals
C57BL/6 mice (6–8 weeks of age) were purchased from Beijing Vital River Laboratory Animal Technology. IL-18Rα knockout mice were gifted by prof. Wei Wang from Sichuan University. CD45.1 mice were kindly provided by Dr. Xiaolong Liu (Center for Excellence in Molecular Cell Science, CAS). Mice were mated after 8 weeks of age and 4~5 male littermates from at least 2 dams were randomly assigned to different cages at post-weaning. All mice were maintained under specific pathogen-free conditions with a 12 h light, 12 h dark cycle and given free access to food and water. Experiments were carried out under the Shandong University Laboratory Animal Center’s approval. Animal Ethics Number: ECSBMSSDU2020-2-86.
## Antibiotic treatment
Commensal microbes were depleted using antibiotics as previously reported32. Specifically, four kinds of antibiotics including ampicillin (1 g/l) (Solarbio), vancomycin (0.5 g/l) (Solarbio), neomycin sulfate (1 g/l) (Solarbio) and metronidazole (1 g/l) (Solarbio) were dissolved in sterile water and stored in 4 °C no more than a week before using. This antibiotic-contained water was supplied as drinking water to pregnant, breastfeeding mice and was changed every 3 days. Meanwhile, the offspring mice from pregnant C57BL/6 dams without antibiotic treatment were maintained in parallel as controls in each experiment. For early-Abx model, 8-week-old control or early-Abx male mice (18~22 g body weight) were subjected to HCC models, 16 S rRNA sequencing and microbiota metabolic or liver NK cell analysis (5~11 mice per group, born from 2~4 different dams and fed in 2~3 different cages).
## HCC mouse models
For AKT/Myc model, 8-week-old control or early-Abx male mice were hydrodynamically injected with the sleeping-beauty transposition system and AKT/Myc plasmids (2 ml physiological saline including 1 μg pCMV-SB100, 8 μg pT3-AKT and 16 μg pT3-cMyc). Six weeks later, the mice were sacrificed and liver tissues were photographed, and the phenotype and function of LrNK cells were analyzed. For depletion of both NK cells and ILC1 or cNK cells only, mice were intraperitoneally injected with 200 μg anti-NK1.1 (BioXcell) or 200 μg anti-asialo GM1 (WAKO) respectively, or the same volume of IgG (BioXcell) every three days for 4 weeks.
For STZ-HFD induced HCC model, a mouse model mimicking nonalcoholic fatty liver disease -related HCC39, control or early-Abx male mice was induced by a single subcutaneous injection of 200 μg Streptozocin (Sigma) at 2 days after birth and feeding with HFD (cat#: D12492, Rodent diet with 60 kcal% fat) after 4 weeks of age. At month 5, the mice were sacrificed and tumor nodes in murine livers were photographed, and the phenotype and function of LrNK cells were analyzed.
The maximal tumor weight was not exceeded $10\%$ weight of the chosen animal as stipulated by the Ethics Committee of Shandong University Laboratory Animal Center.
## Adoptive transfer model
CD3-NK1.1+ cells were purified from 8-week-old control or early-Abx mice (CD45.2). Then 1 × 106 purified cells were transferred into CD45.1 mice. Twenty-four hours later, the recipient mice were intraperitoneally injected with 150 μg poly (I:C) (Sigma) to stimulate NK cell activation62,63 and the function of CD45.2+CD49a+CD3-NK1.1+ cells were analyzed 16 h later. On the other hand, CD3-NK1.1+ cells or LrNK cells were puried from CD45.2 wild-type or IL-18Rα-deficient mice and separately transferred into control or early-Abx mice (CD45.1). Then, the function of CD45.2+CD49a+CD3-NK1.1+ cells were analyzed in the recipient mice after injection of poly (I:C) for 16 h.
## Isolation of liver mononuclear cells (LMNCs), bone marrow mononuclear cells and cell subset purification
Briefly, the mouse livers were washed and passed through a 200-gauge stainless steel mesh. The single cells were washed, red blood cells were lysed and then the cell suspension were centrifuged over $40\%$ Percoll gradient medium. For bone marrow mononuclear cells, bone marrow was obtained by flushing femurs and tibias and then RBC lysed and washed with PBS.
For purification of LrNK cells, Kupffer cells, IL-18Rα- LrNK and IL-18Rα+LrNK cells, LMNCs were stained with specific antibodies and subjected to Moflo Astrios EQ.
For the isolation the liver mononuclear cells (LMNCs) from hepatic hemangioma patients, fresh tissues were washed with PBS, cut into small pieces and digested with collagenase typeII(1 mg/mL, Worthington) and DNaseI(0.01 mg/mL, ThermoFisher Scientific) in RPMI 1640 medium (Gibco) for 1 h at 37 °C. Then, lymphocytes were isolated with $30\%$ Percoll density gradient and washed twice with PBS.
## Flow cytometry analysis
For cell surface staining, cell suspensions were incubated with the specific labeled antibodies for 30 min at 4 °C. For intracellular staining, freshly isolated cells were stimulated with PMA (50 ng/ml) (Sigma) and Ion (1 μg/ml) (Biolegend) for 2 h, or IL-12 (20 ng/ml) (Proteintech) and IL-15 (50 ng/ml) (Proteintech) for 16 h, then cultured with Brefeldin A (BFA) (Biolegend) at a final concentration of 10 μg/mL for 4 h. After surface staining, cells were fixed with intracellular fixation buffer for 20 min, then permeabilized with permeabilization buffer for 10 min. Intracellular staining was performed with antibodies diluted into permeabilization buffer. For CD107a staining, cells were incubated with CD107a antibody for 4 h. Flow cytometry was carried out on Cytoflex S (Beckman coulter) and analyzed by FlowJo 10.6.2 or CytExpert 2.3.0.
All antibodies for flow cytometry staining are shown in Supplementary Table 1.
## 16 S rRNA sequencing and microbiota metabolic analysis
Feces were immediately incubated on ice and rapidly transferred to −80 °C for storage after collection. The bacteria were tested by gene sequencing on the Illumina Hiseq platform with Majorbio (Shanghai, China). The V3-V4 region of the 16SrRNA genes was amplified using the universal primers (338 F: ACTCCTACGGGAGGCAGCA, 806 R: GGACTACHVGGGTWTCTAAT). The data were analyzed on the online platform of Majorbio Cloud Platform (www.majorbio.com).
For analysis of fecal microbiota metabolites, feces (~10 mg) were subjected to LC-MS by Shanghai Majorbio Bio-pharm Technology Co.,Ltd. Feces (~10 mg) or liver and spleen tissue (~100 mg) were subjected to a targeted GC-MS analysis to quantify short chain fatty acid levels.
## Isolation and culture of hepatocytes
Mice were anaesthetized and the portal vein was dropped with solution A (D-hanks without Ca2+ and Mg2+ containing 0.5 mM EGTA (Santa), 10 mM HEPEs (Coolaber) for 8 ml/min. When the liver was saturated, the inferior vena cava was opened and the mice were perfused with another 20 ml of solution A and 20 ml of solution B (Hanks’ solution containing 10 mM HEPEs, 0.5 mg/ml Type IV Collagenase (Gibco)). Then the liver was taken out, washed, torn with tweezers, and digested with 10 ml mixture of solution A and solution B in an incubator at 37 °C for 20 min. The cell suspension was filtered, and centrifugated at 47 g. The cell pellet was resuspended in DMEM (Gbico) containing $10\%$ fetal bovine serum (Gbico), $1\%$ penicillin-streptomycin Liquid (Solarbio), 2 uM sodium pyruvate (Sigma), 0.4 μg/ml dexamethasone (Sigma), 14 U/L insulin (Solarbio and cultured for 3 h and then cultured in DMEM containing $10\%$ fetal bovine serum, $1\%$ penicillin-streptomycin, 2 μM sodium pyruvate, 0.04 μg/ml dexamethasone, 0.14 U/L insulin.
## Transwell co-culture assay
Co-culture assay was performed for LrNK cells with LMNCs, hepatocytes or Kupffer cells using Transwell system. Briefly, 1 × 104 purifed LrNK cells were plated in the transwell insert (upper compartment) and co-cultured with 1 × 105 LMNCs, hepatocytes or Kupffer cells at bottom of transwell (lower compartment) for 24 h. Poly(I:C), the GPCR agonists AR-420626 (R&D), 4-CMTB (TOPSCIENCE) or MK-0354 (MCE) or butyrate (Sigma) were added at the indicated experiments.
For GPR109A knockdown, isolated hepatocytes or Kupffer cells (1 × 105 cells) were grown overnight in 12-well plates and infected with negative control or GPR109A shRNA lentivirus (Shanghai Genechem Co.,Ltd.) and incubated for 72 h. Knockdown efficiency was verified by RT-qPCR and were used for the co-culture experiments.
## Cytotoxicity assay
CFSE-labelled YAC-1 cells were co-cultured with purified LrNK cells with IL-2 (100 U/mL) at the effector: target ratio of 5:1 at 37 °C in a $5\%$ CO2 incubator for 4 h. Then 7-AAD was stained, and FCM identified lysed cells as CFSE+7-AAD+.
## Seahorse analysis
2 × 106 purified LrNK cells were pre-treated with IL-12 (20 ng/ml) and IL-15 (50 ng/ml) for 12 h, then seeded in a Seahorse Bioscience culture plate coated with Cell-Tak solution (Corning), and cultured in XF Base Medium Minimal DMEM medium (Agilent) with 25 mM glucose, 2 mM glutamine and 1 mM pyruvate in a non-CO2 incubator for 1 h. Basal, maximal OCR and ATP production were measured by an XF96 Seahorse Extracellular Flux Analyzer (Agilent) following the manufacturer’s instruction.
## Reverse transcription-quantitative PCR (RT-qPCR)
Total RNA was extracted from liver tissues or cells using TRIzol reagent. cDNA synthesis was done using Revert Aid First Strand cDNA Synthesis Kit and random primers according to the manufacturer instructions. PCR was carried out using SYBR® Green Real-Time-qPCR Master Mix. Primers pairs for target genes are shown in Supplementary Table 2.
## Statistical analysis
Statistical significance was determined using Prism 8. Student’s t tests (two-tailed unpaired) between two groups or one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test were used to determine significance. The difference in overall survival was tested using log-rank tests. The results of 16 s rRNA were tested using Wilcoxon rank sum test and non-parametric factorial Kruskal–Wallis (KW) sum-rank test. Data are presented as mean ± SEM. Statistical significance was reported as *$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; and ns, no significance.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37419-7.
## Source data
Source Data
## Peer review information
Nature Communications thanks Domenico Mavilio and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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---
title: Alzheimer’s Disease Prediction Using Attention Mechanism with Dual-Phase 18F-Florbetaben
Images
authors:
- Hyeon Kang
- Do-Young Kang
journal: Nuclear Medicine and Molecular Imaging
year: 2022
pmcid: PMC10043070
doi: 10.1007/s13139-022-00767-1
license: CC BY 4.0
---
# Alzheimer’s Disease Prediction Using Attention Mechanism with Dual-Phase 18F-Florbetaben Images
## Abstract
### Introduction
Amyloid-beta (Aβ) imaging test plays an important role in the early diagnosis and research of biomarkers of Alzheimer’s disease (AD) but a single test may produce Aβ-negative AD or Aβ-positive cognitively normal (CN). In this study, we aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via a deep learning–based attention method and evaluate the AD positivity scores compared to late-phase FBB which is currently adopted for AD diagnosis.
### Materials and Methods
A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests, were retrospectively analyzed. Early- and delay-phase FBB images were spatially normalized with an in-house FBB template. The regional standard uptake value ratios were calculated with the cerebellar region as a reference region and used as independent variables that predict the diagnostic label assigned to the raw image.
### Results
AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 0.858, AUROC: 0.831) than those from delay phase FBB imaging (ACC: 0.821, AUROC: 0.794). AD positivity score estimated by dual-phase FBB (R: −0.5412) shows a higher correlation with psychological test compared to only dFBB (R: −0.2975). In the relevance analysis, we observed that LSTM uses different time and regions of early-phase FBB for each disease group for AD detection.
### Conclusions
These results show that the aggregated model with dual-phase FBB with long short-term memory and attention mechanism can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only a single phase FBB.
## Introduction
Approximately 50 million people worldwide suffer from dementia, and nearly 10 million new cases occur every year. The total population with such dementia is expected to be 82 million by 2030 and 152 million by 2050 [1]. Alzheimer’s disease (AD), the most common cause of dementia, is complex and multi-factorial in elucidating the continuum of conditions leading to asymptomatic, mild cognitive impairment, and dementia. Amyloid-β (Aβ), which can be measured through positron emission tomography (PET) scan or cerebrospinal fluid analysis, is one of those defining the pathology of AD and is known as the earliest sign among AD biomarkers. Therefore, Aβ-related biomarkers have been studied for a clinical diagnostic index as well as for early diagnosis or prediction [2–4]. However, as AD is known to be affected by neurofibrillary tangles aggregated by hyperphosphorylated tau protein, genetics, and environmental influences as well [5], both Aβ-negative AD and Aβ-positive CN inevitably exist [6]. In addition, it is difficult to monitor the patient’s condition because Aβ plaques are already saturated by the time cognitive function clinically declines [7]. These facts remind us how additional AD biomarkers are required to understand and respond to AD. 18F-Fluorodeoxyglucose (FDG), which is a radiopharmaceutical that enables imaging of changes in glucose metabolism in brain tissue, is another one of representative AD biomarker. Hypometabolism, which is measured using FDG-PET, is known to be associated with neurodegeneration and cognitive decline [8]. However, such a series of PET imaging tests have drawbacks that make patients who need a diagnosis or longitudinal studies for AD undergo relatively frequent radiation exposure and high financial expenditure.
Aβ uptake in early-phase Aβ-PET is known to be a potential perfusion imaging modality that reflects cerebral blood flow [9–11]. Reference [4] reviewed the coupled relationship between hypoperfusion which causes deleterious changes in neurons and cerebral hypometabolism which underlies neuronal/synaptic dysfunction with the respective associations with cognitive impairment. Given an adequate evaluation of neuronal function and Aβ load from dual-phase Aβ-PET imaging, we may be able to provide patients with a more accurate AD diagnosis and prognostic evaluation without compromising patient convenience. Compared to late-phase Aβ-PET, however, there is no consensus or a well-established guide regarding how to interpret and evaluate the potential perfusion imaging for AD.
In the field of imaging biomarkers, various efforts have been made to provide an improved quality of medical services continuously. In particular, the latest technologies incorporating artificial intelligence have been reported to show a consistent inference and classification performance comparable to a human doctor. Such technologies are excellent at not only reducing a portion of manual labor of human doctors but also addressing inter-observation problems [12, 13]. In addition, machine learning–based studies on imaging for AD biomarkers are also actively reported [14]. Existing machine learning–based studies for AD have commonly suggested some predictive models that learn single or more than two kinds of imaging data such as magnetic resonance imaging, FDG, or Aβ-PET. Those attempts using a variety of information for AD detection could be appropriate solutions that address the complex and heterogeneous characteristics of AD.
In this study, we aimed to develop and evaluate an improved AD prediction model in the machine learning algorithm by engaging with dynamic early-phase Aβ-PET as well as single late-phase Aβ-PET conventionally used for AD diagnosis. The method included [1] extracting the mean of the standard uptake value ratio (SUVr) with a consistent area from individual dual-phase Aβ-PET imaging; [2] selecting a machine learning–based predictive model, which estimates the AD positivity score; and [3] comparing the classification performance among models and evaluating the association between predicted AD positivity scores and cognitive function or occurrence of AD.
## Participants
We adopted FBB PET as an imaging biomarker to evaluate Aβ and retrospectively recruited subjects who visited the Department of Neurology and Nuclear Medicine of the Dong-A University Hospital (DAUH) and underwent dual-phase FBB from November 2015 to June 2020. The total number of subjects was 264, consisting of 74 cognitive normal (CN) and 190 AD. Detailed demographic data of the participants are presented in Table 1. All CN cases had normal age-, gender-, and education-adjusted performance on standardized cognitive tests. The AD participants met the following inclusion criteria: [1] criteria for dementia according to the Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV-TR) [15] and [2] the criteria for probable AD according to the NIA-AA core clinical criteria [16]. The individual FBB PET imaging for Aβ load was visually evaluated by the brain Aβ plaque load (BAPL) scoring system, which defines a BAPL score of 1 (no Aβ load), 2 (minor Aβ load), and 3 (significant Aβ load) [17]. Dong-A University Hospital Institutional Review Board (DAUHIRB) reviewed this study with the member who participated in Institutional Review Board Membership List III and finally approved this study protocol (DAUHIRB-17-108). All procedures for data acquisition were by the ethical standards of DAUHIRB with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. We guarantee that informed consent was obtained from all participants for this study. Table 1Demographics of experimental data with dual-phase F18-Florbetaben imagingVariablesCNADTotalp-value#74190264N/ASex (F/M)$\frac{49}{25102}$/$\frac{88151}{1130.065}$Age70.24 ± 7.4171.95 ± 8.8471.47 ± 8.480.142FBB reading(Aβ (−)/Aβ (+))$\frac{59}{1532}$/$\frac{15891}{173}$< 0.001Education (y)9.30 ± 4.0910.10 ± 4.489.88 ± 4.390.188K-MMSE27.50 ± 1.7119.52 ± 4.2521.62 ± 5.14< 0.001CN, cognitively normal; AD, Alzheimer’s disease; K-MMSE, Korean version of Mini Mental State Examination
## PET Acquisition
All FBB PET imaging was performed using a Biograph 40mCT Flow PET/CT scanner (Siemens Healthcare, Knoxville, TN, USA) and reconstructed through UltraHD-PET (TrueX-TOF). A dose of 300 MBq FBB was injected intravenously in resting conditions. Dynamic frames were acquired from 0 to 20 min and from 90 to 120 min post-injection after helical CT with a 0.5-s rotation time at 100 kVp and 228 mAs. The image acquisition time for dual-phase FBB PET was determined by related studies to sufficiently include the peak of Aβ uptake for early-phase FBB PET (eFBB) and the manufacturer’s recommendations for delay-phase FBB PET (dFBB) [10, 17, 18]. The acquired dynamic eFBB and static dFBB were 27 frames of 128 × 128 × 110 (3.19 mm × 3.19 mm × 1.5 mm) resliced from a field of view of 408 mm × 408 mm × 165 mm, and one frame of 400 × 400 × 110 (1.02 mm × 1.02 mm × 1.5 mm) resliced from a field of view of 408 mm × 408 mm × 165 mm, respectively. Static eFBB to evaluate potential perfusion was made by averaging the frames corresponding to 2–7 mins from dynamic eFBB. The optimal time period required to obtain static eFBB was internally determined using the approach in Reference [10].
## Data Pre-processing
We adopted a series of pre-processing procedures to extract regional mean SUVr for dynamic eFBB or static eFBB/dFBB, respectively, and each step was as follows. For the spatial normalization of all PET images, we used an in-house eFBB PET template [19], which averaged 8 CN and 8 AD randomly selected from the spatially normalized FBB data pool in Montreal Neurological Institute (MNI) space [20]. Each static eFBB was spatially non-linearly registered to the template space. For the dynamic eFBB of a case, we created a deformation field to represent the transformation from the mean of the total number of frames to the template space and applied it to each frame. The deformation field for a dFBB was identical to that derived from spatial normalization of the matched static eFBB [21]. As a result, the spatially registered imaging was in a voxel space of 95 × 79 × 68 (height × width × depth). We merged the Hammers atlas [22] into 7 representative regions (frontal lobe, temporal lobe, parietal lobe, anterior cingulate cortex, posterior cingulate cortex, and cerebellum) for the reference region for count normalization and volume of interest for estimating the mean SUVr. After spatial normalization, the intensities of each image were normalized with respect to the mean uptake of the whole cerebellar region as a reference region. Finally, for static eFBB/dFBB and dynamic eFBB, regional mean SUVr of 6 × 1 and regional time-activity curve (TAC) data of 6 × 27 (number of target regions × temporal length) were obtained, respectively.
## Calculation of AD Positivity Score Based on Brain Blood Perfusion and Amyloid-β Plaque
To calculate the AD positivity score from regional SUVr, we build a neural network (NN)–based classification model to predict the probability of whether the given regional TAC or mean SUVr data belong to the CN or AD distribution. Figure 1 shows the structure of our proposed framework that predicts AD using dual-phase FBB. The whole aggregated NN (NNaggregated) in Fig. 1 consists of three modular networks: long short-term memory (LSTMeFBB) model to extract temporal features from dynamic eFBB, feedforward neural network (NNdFBB) for dFBB, and following NN (NNDx) to make a final diagnosis decision from the phase-specific features for each phase of FBB delivered from the preceding layers. In particular, we adopted an attention mechanism [23] to adaptively select the phase-specific features for AD detection under biomarkers’ disagreement. We describe the details of each modular networks and the attention mechanism layer connecting them in the following section. Fig. 1Overview of the proposed deep learning–based framework to estimate AD positivity score with dual-phase 18F-Florbetaben (FBB) PET imaging. For a given dual-phase FBB, we extracted regional meanTAC and SUVr data after pre-processing step for both phases of FBB and feed them into LSTM and simple dense layer, respectively, to obtain the phase-specific feature vectors. Those features are aggregated with concatenation and followed dense layer. The phase-specific features and 1st aggregated feature are used to make context vector using attention mechanism. Finally, the context vector and 1st aggregated features are pooled with concatenation and used to infer AD positivity score Context vector encoded by attention mechanism layer and 1st aggregated features are pooled and used to infer AD positivity score. NNDx has an output layer with two nodes leading to the softmax function to interpret the model output as the probability for diagnostic labels, and their model parameters were trained to minimize the cross-entropy loss between the predicted probability and one-hot encoded actual label. To evaluate the efficacy and feasibility of the proposed model, we compared it against representative methods such as support vector machine (SVM) [24], and random forest (RF) [25] as a baseline.
## Three Modular Networks for Independent Feature Extraction and Aggregation
We built the whole network into a combination of individual modules that are responsible for the independent task of performing AD classification. Long short-term memory (LSTM) is well known for handling long-term dependencies of temporal features using three types of gates (input, forget, and output gates) and memory cells [26, 27]. LSTMeFBB produces phase-specific features for AD classification from regional TAC data. We first applied this LSTM layer on regional TAC data (6 × 27) to produce the temporal feature. Then, we applied layer normalization to reduce training time and stabilizing the hidden state dynamics in the previous recurrent neural network layer [28]. All of LSTM layers in NNaggregated were followed by individual layer normalization. After two layers of LSTM, we applied feed forward layer (FC) on the output (6 × 1) at the last time step to encode high-level phase-specific feature. All of FC in NNaggregated were followed by the pre-defined layer block, which are batch normalization, ReLU activation [29], and dropout layer [30]. To encode phase-specific feature for dFBB, we used a 4-layer FC followed by the pre-defined layer block which was explained above. Finally, we produced comprehensive functional features from two types of phase-specific features and phase attention which we present in the following section and AD positivity score by applying single-layer FC (NNDx).
## Attention Mechanism for Adaptive Phase-Specific Feature Selection
In this work, we focus on adaptive phase-specific feature selection to address biomarkers’ disagreement. We adopt an attention method proposed by Luong et al. [ 23] to adaptively select proper evidences to predict AD positivity. Assume that a subject has N phase-specific hidden features hi with i ∈ [1, P], H ∈ RD and h′ with H′ ∈ RD as a 1st aggregated hidden feature by concatenating N phase-specific hidden features and applying single-layer FC (Fig. 1). To highlight more informative phase to form the 1st aggregated feature for AD detection, we introduce a phase context vector C created from hi, h′ as the input of this mechanism as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_i=f\left({h}_i,\kern0.5em {h}^{\prime}\right),$$\end{document}ei=fhi,h′,2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}_i=\frac{\exp \left({e}_i\right)}{\sum_{$k = 1$}^N\exp \left({e}_k\right)},$$\end{document}ai=expei∑$k = 1$Nexpek,3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C=\sum\nolimits_{$i = 1$}^N{a}_i{h}_i$$\end{document}C=∑$i = 1$Naihiwhere f is simple neural network that aggregates all of phase-specific hidden features hi and reference feature h′. The simple network can be written as follows:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A= softmax\left(\mathit{\tanh}\left(XW+b\right)\right)$$\end{document}A=softmaxtanhXW+b Here, X is concatenated feature according to each phase between hi and h′ as X ∈ RN × 2D. W and b are model parameters which will be learned to make attention score A with W ∈ R2D × 1, b ∈ RN × 1, and A ∈ RN × 1. Finally, phase context vector C is the weighted sum of H with A as [3]. And the context vector C and 1st aggregated feature will be used to encode 2nd aggregated feature in Fig. 1.
## Detailed Parameters for Model Selection and Model Evaluation
For our experiment, we focused on showing that the model with dual-phase FBB is more useful for estimating AD positivity than a model with only dFBB. Therefore, we tried to simplify and unify the model structure and detailed parameters of each model as much as possible. NN-based models, including LSTM, have two hidden layers, with six nodes of each hidden layer. To prevent neural networks from overfitting, we apply L2 regularization with a weight of 0.01 and dropout layer with dropout rate of 0.2. The learning curves of all models were set to be trained up to 10,000 epochs but were stopped if the validation loss was not updated more than 200 times. The learning rate was 0.00001, and the Adam optimizer [31] was used for each setting. If the validation loss was not updated more than 100 times at a point, 0.001 of the decay rate was applied to the learning rate of the point.
SVM used in the experiment used a linear kernel as a kernel function. A radial basis function or polynomial kernel was also tested in an internal experiment but no meaningful difference was observed, and a simpler model was finally adopted to prevent overfitting. RF was trained with a max depth of 2 and a number of estimators of 1000, and gini inpurity [32] was used to measure the quality of a split. The hyperparameters of both comparative models were heuristically determined.
For model selection and evaluation, our dataset was split into training, validation, and testing with ratios of 0.6, 0.1, and 0.3, respectively. We use stratified sampling so that the ratio of diagnostic labels according to Aβ load in each data was same. The data split was the same for each phase of the dataset and all experiments. The previously preprocessed TAC and SUVr datasets were last subjected to min-max normalization before being input to a predictive model after the split.
The software used in this experiment was the SPM12 library and MATLAB R2020a for the data pre-processing, including spatial normalization, and count normalization, for evaluating the pre-processed image with t-contrast, and for calculating regional mean SUVr based on the Hammers atlas [22]. Keras 2.2.4 library and Python 3.6.9 were used to select and evaluate a model for estimating AD positivity. The experimental tool was implemented and tested on Linux Ubuntu 16.04 LTS with an Intel Core i7-6800K CPU and two GPUs (NVIDIA GeForce GTX 1080).
## Statistical Analysis
We used independent-sample t-tests for numerical variables such as age and education and Pearson’s Chi-square test for categorical variables such as sex, FBB reading, and K-MMSE to determine whether the characteristics of subjects in our experimental dataset are biased according to the diagnostic label. For the demographic analysis, we used IBM SPSS statistics version 23. To evaluate the classification performance of trained models, we calculated the accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection using DeLong’s method [33] and Spearman correlation between predicted AD positivity scores and neuropsychological tests/actual diagnostic label. For these processes, we used MedCalc version 18.9.1 (MedCalc Software). In all tests, the statistical significance level was set at $p \leq 0.001$ with a two-sided test.
## Data Demographics
As Table 1 shows, there was no statistically significant difference between the CN and AD groups in age, sex, and education variables. The results of K-MMSE (which is the dominant variable in the diagnosis of AD and reflects cognitive function) and dFBB readings (which reflect a state of Aβ plaque load) showed statistically significant differences between groups. Therefore, the retrospective data used in the experiment differed only in the cognitive function and hallmark pathology that directly affect the diagnostic label, but no bias was observed in other factors. Our experimental data included $20.83\%$ of Aβ-positive CN and $16.84\%$ of Aβ-negative AD.
## Pre-processed Imaging Data for TAC and SUVr
For the result of spatial registration, Fig. 2a shows static eFBB and dFBB registered in MNI space, which is randomly selected from each diagnostic label, compared with raw images of those in native space. As a result of pre-processing, it was confirmed that the spatial characteristics of individual imaging disappeared after they were transformed into MNI space but functional characteristics remained according to the diagnostic label. Fig. 2Pre-processed dual phase FBB PET (a) and t-contrast of early phase F18-Florbetaben PET according to Aβ distribution (b). The contrast was set to [1, −1] for cognitive normal vs. Alzheimer’s disease in SPM12 In Fig. 2b, to check whether the functional information of eFBB on our pre-processing method and selected time period is feasible, eFBB (2–7 min) was observed by t-contrast according to the diagnostic label. The functional information of dFBB was omitted because the results have already been verified through previous studies [21]. In this study, t-contrast was applied to the eFBB images, and the voxel-wise difference between the two group (CN vs. AD) was calculated and visualized. As a result of t-contrast, the relative contrast of AD group is dominantly lower than CN, except for the cerebellar area in all of the 4 comparisons regardless of Aβ distribution.
## AD Classification Performance
Table 2 shows the AD classification performance of ML-based predictive models. LSTM (ACC: 0.792, AUROC: 0.775, F1: 0.849, G-mean: 0.773) was the best model for eFBB. RF (ACC: 0.736, AUROC: 0.584, F1: 0.835, G-mean: 0.467), NN (ACC: 0.726, AUROC: 0.648, F1: 0.813, G-mean: 0.467), and SVM (ACC: 0.708, AUROC: 0.746, F1: 0.763, G-mean: 0.740) followed. For the classifier of static dFBB, which is used for conventional FBB reading, NN (ACC: 0.821, AUROC: 0.794, F1: 0.872, G-mean: 0.792) was the best model for AD detection with dFBB (NNdFBB) and RF (ACC: 0.802, AUROC: 0.721, F1: 0.868, G-mean: 0.696) and SVM (ACC: 0.755, AUROC: 0.799, F1: 0.803, G-mean: 0.792) followed. In comparison among all kinds of FBB, the NNaggregated was the best model (ACC: 0.858, AUROC: 0.831, F1: 0.901, G-mean: 0.828), which trained dual-phase FBB, followed by NNdFBB that learned dFBB.Table 2Comparison of predictive performance for Alzheimer’s disease classificationAc. phaseModelAccuracyAUROCF1G-meanEarlySVM0.7080.7460.7630.740RF0.7360.5840.8350.467NN0.7260.6480.8130.622LSTM0.7920.7750.8490.773DelaySVM0.7550.7990.8030.792RF0.8020.7210.8680.696NN0.8210.7940.8720.792DualLSTM+NN0.8580.8310.9010.828Ac. Phase acquisition phase, AUROC area under receiver operating characteristic AD positivity scores measured by three models (NNaggregated, LSTMeFBB, and NNdFBB) with each phase of FBB (dual-phase FBB, dynamic eFBB, and static dFBB) in the test data are presented in Table 3. NNaggregated (AUROC: 0.854) trained dual-phase FBB was able to detect AD better than LSTMeFBB (AUROC: 0.841) and NNdFBB (AUROC: 0.851). In comparison of AUROC in Aβ-negative distribution (Aβ (−) CN vs. Aβ (−) AD), the NNaggregated (AUROC: 0.837) was the best, followed by LSTMeFBB (AUROC: 0.792) and NNdFBB (AUROC: 0.731). In Aβ-positive distribution (Aβ (+) CN vs. Aβ (+) AD), the NNaggregated (AUROC: 0.901) was the best as well, followed by LSTMeFBB (AUROC: 0.812) and NNdFBB (AUROC: 0.706). Figure 3 shows the distribution of AD positivity scores predicted by the trained models on the test set. Figure 3(b) shows that there are many misclassifications in the distribution of Aβ (+) CN and Aβ (−) AD because NNdFBB is only referring to the Aβ pathology. On the other hand, Fig. 3a and c demonstrate that LSTMeFBB and NNaggregated can relatively correctly predict the AD positivity score in the distribution of Aβ (+) CN and Aβ (−) AD. In particular, NNaggregated using two features shows a remarkably correct classification of Amyloid negative AD than LSTMeFBB.Table 3Comparison of AUROC of AD positivity scores according to specific distributionPop./test (Ac. phase-model)AUROC (SE)Total Early FBB (LSTMeFBB)0.841 (0.0417) Delay FBB (NNdFBB)0.851 (0.0394) Dual FBB (LSTM+NN)0.854 (0.0465)Aβ (−) CN vs. Aβ (−) AD Early FBB (LSTMeFBB)0.792 (0.0811) Delay FBB (NNdFBB)0.731 (0.1010) Dual FBB (LSTM+NN)0.837 (0.0729)Aβ (+) CN vs. Aβ (+) AD Early FBB (LSTMeFBB)0.812 (0.0846) Delay FBB (NNdFBB)0.706 (0.0964) Dual FBB (NNaggregated)0.901 (0.1570)Fig. 3Predicted AD scores in Aβ-negative normal (Aβ (−) NC), Aβ-positive normal (Aβ (+) NC), Aβ-negative AD (Aβ (−) AD), Aβ-positive AD cases (Aβ (+) AD)
## Input and Feature Distribution According to the Visual Reading of dFBB and Diagnostic Label
Feature visualization provides a useful means of guessing how well a deep learning model understands the input data to achieve its learning goals. This can be addressed using t-distributed stochastic neighbor embedding (t-SNE), which is a kind of dimensionality reduction method designed to visualize high-dimensional data in a two- or three-dimensional map [34]. t-SNE prepares a neural network to understand target data distribution and is iteratively trained by gradient descent method so that the distance between data points low-dimensional data representation is similar to that in high-dimensional space. In Fig. 4, the distributions of inputs and features in the last hidden layer of the NN-based model according to the phase of FBB are shown in a two-dimensional space using t-SNE. Figure 4c and e show the distribution of mean SUVr and features extracted from dFBB, and those do not seem to fully explain Aβ-positive CN and Aβ-negative AD. The distribution of mean SUVr and features extracted from eFBB shown in Fig. 4a, b, and d appears to be that Aβ-negative AD distribution is closer to Aβ-positive AD distribution compared to those extracted from dFBB. However, it is observed that the Aβ-positive CN distribution is still close to that of Aβ-positive AD. On the other hand, in Fig. 4f, the feature distribution extracted from dual-phase FBB showed the separated representation rather than entangled for Aβ-negative CN and Aβ-positive AD.Fig. 4Distributions of model inputs and model features observed through t-SNE according to F18-Florbetaben reading label and diagnostic label. a–c show distributions of mean SUVr values used for a predictive model. d–f show distributions of feature vectors obtained from the last hidden layer of a neural network
## Association Between AD Positivity Score and Neuropsychological Test
Figure 5 shows the AD positivity score distribution of each phase of FBB according to neuropsychological test results. For AD cases with a low score of MMSE, NNdFBB hardly shows a high AD positivity score. On the other hand, NNaggregated and LSTMeFBB suggested high AD positivity scores for cases with decreased cognitive function. In the correlation analysis, AD positivity score from NNaggregated is best correlated with neuropsychological test results (R: −0.5412, $p \leq 0.0001$). The correlation of LSTMeFBB (R: −0.4613, $p \leq 0.0001$) and NNdFBB (R: −0.2975, $p \leq 0.0022$) followed. Fig. 5Association between predicted AD positivity scores from machine learning–based models and neuropsychological tests
## Observation of the Overall Behavior of the LSTM on Early-Phase FBB
Explaining a model prediction helps to understand the distribution of training data or the behaviors taken by the model to solve a given problem [35–37]. One approach for explaining deep NN decisions is by multiplying the partial derivative of the model prediction and the actual input feature, also referred to as simple Taylor decomposition [38], and this method also serves as a baseline for many related studies [39, 40]. The resulting relevance map can provide a feature-wise heatmap same as the input size and be understood as the product of sensitivity of how much the feature contributes to the model prediction and saliency of how much the feature is presented in the sample [40]. Figure 6 shows which part of the data the LSTM trained on eFBB observes for AD detection. In the comparison of the mean composite relevance in Fig. 6(b), CN shows a markedly high relevance in the 2nd to 5th frames and a remarkably low relevance in the 9th to 15th frames. On the other hand, AD shows a rather high relevance in the 4th to 7th frames and was generally maintained until the last frame. In the comparison of the mean regional relevance maps shown in € and (f), CN shows a remarkably high relevance in the anterior cingulate in the 15th to 25th frames. AD shows a higher overall relevance than CN, including the anterior cingulate and occipital lobe regions. Fig. 6Regional time activity curve (TAC) learned by the LSTM to classify AD and the relevance map calculated from the model predicting the test set. In the second and third columns, the regional TAC with b and c, which were used for prediction by LSTM, and the relevance value applied to the model for the prediction explanation were visualized (relevance map) with e and f. a and d show the mean regional TAC along time axis (mean composite TAC) and mean regional relevance score along time axis (mean composite) according to the diagnostic labels. The diagnostic labels include cognitive normal (CN, black) and Alzheimer’s disease (AD, red). We can observe that the distribution of relevance scores presenting the model behaviors as well as the TAC data is different between CN and AD population. In the relevance map, each column represents a regional SUVr at each time in the early phase FBB, and each row is an individual cortical area of the brain arranged in order of temporal lobe, anterior/posterior cingulate, frontal lobe, occipital lobe, and parietal lobe
## Discussion
We designed a predictive model to successfully improve the conventional imaging biomarkers with only static dFBB by engaging in dynamic eFBB based on the following two assumptions: [1] The potential blood flow information included in eFBB is sufficiently distinguished from dFBB and they provide complementary information with respect to AD diagnosis. [ 2] The temporal information included in dynamic eFBB can be represented as an embedding vector representing blood flow information by the LSTM model. In the remaining paragraphs, we will elucidate the experimental results or related problems concerning the hypotheses above.
Compared with the use of only dFBB in the conventional context, to improve the accuracy of AD detection by engaging dual-phase FBB, eFBB and dFBB must contain sufficient complementary information regarding AD, that is, eFBB should be able to sufficiently explain AD in different aspects from dFBB. As shown in Fig. 2 and Table. 3, we tried to confirm whether the potential perfusion information of eFBB is suitable for this experiment. Even though the deformation field used for registration in eFBB was applied to dFBB, both eFBB and dFBB were located in the MNI space in our visual observation, and the Aβ load pattern in a region of gray matter was still observed in each preprocessed dFBB. In voxel-based analysis, hypo-perfusion was observed in the AD group regardless of the Aβ distribution (Fig. 2). From the comparison of characteristics between the same Aβ distributions in Table 3, it was observed that the AD positivity score from eFBB explained AD distribution better than from dFBB, which meant that it was difficult to discriminate the diagnostic label with dFBB in the same Aβ distribution. Therefore, the dynamic or static eFBB acquired from our experimental protocol is meant to be complementary to the uptake of dFBB for AD detection, and the improved classification performance of the NNaggregated could be based on the additional potential blood flow data.
LSTM is a representative NN for time series data that ultimately understands the long-term contextual information by managing the cell state necessary to determine the output from the input over time through input, output, and forget gates [41]. In terms of research on medical data, LSTM has been frequently used in EEG/ECG [42], imaging reports, electronic health records, and static or dynamic imaging data [43, 44], which include temporal information. A common delay-phase static PET image is acquired at the acquisition time determined by investigating the pseudo-equilibrium interval in which specific binding remains stable through TAC data and considering other parameters such as image quality and diagnostic accuracy. In the case of the FBB radiotracer, the manufacturer provides acquisition time for the delay phase, not for the early phase. In eFBB, the optimal acquisition time interval closest to potential perfusion cannot be found in the stable state owing to the curve that changes rapidly around the peak; therefore, the interval must be determined exploratory. Even the interval for ideal potential perfusion imaging is not deterministic and may vary from case to case. As a related work, it was mainly considered in studies that explored a specific acquisition time based on the similarity between eFBB and FDG images. They randomly selected an interval, including the peak uptake[11], or searched for a combination of the start time and time window to determine the acquisition time with the correlation most similar to FDG [10]. Figure 6 shows that the temporal and spatial features observed by LSTM trained on the eFBB differ according to the diagnostic label. These results suggest the presence of the temporal features of the eFBB for AD detection and non-determinism of the acquisition time interval of the ideal potential perfusion image. In Table 2, the LSTM model showed better performance than the NN model trained static eFBB at 2–7 min, which had a good correlation with FDG in our prior study [18]. These experimental results may indicate that the LSTM could understand the temporal features required for AD classification from potential perfusion information in dynamic eFBB and the calculation of optimal acquisition time could be omitted.
Figure 3a and b show that eFBB and dFBB discriminate AD from CN using different features of each image. In Fig. 3b, most misclassifications occurred in the Aβ-positive CN and Aβ-negative AD populations, whereas, in Fig. 6a, the eFBB classifier consistently scores a proper AD positivity for CN or AD regardless of Aβ distribution. Therefore, it could be considered that the performance of the dual-phase FBB classifier originates from the state of neuronal injury by comprehensively evaluating the degree of hypo-perfusion from eFBB and Aβ plaque deposition from dFBB, respectively (Fig. 3c). In Table 3, AD positivity scores calculated by dual-phase FBB for the entire population showed the AUROC, which had no statistically significant difference compared to the MMSE, and better classification performance than those calculated using only dFBB regardless of Aβ distribution. These results may indicate that it is possible to improve the evaluation of the degree of neuronal damage in research or clinically when the AD positivity score of dual-phase FBB is provided. In addition, it could provide a quantitative index to nuclear medicine physicians to explain false negative/positive cases in FBB imaging tests. This quantitative method could be considered for application to other types of tracers or PET imaging where early-phase PET reflects potential perfusion information.
This study proposes a quantitative method for the interpretation of dual-phase FBB at this point when the evaluation criteria for potential perfusion information of eFBB have not yet been established. Ultimately, it could help to reduce the radiation exposure and costs for patients with AD, and for a nuclear medicine physician, it could be a helpful tool in visual assessment for dual-phase FBB. On the other hand, as a limitation of this study, the predictive model analyzing dual-phase FBB needs to be evaluated in terms of external validation or clinical validity in the future. As mentioned earlier, AD is associated with neurofibrillary tangles aggregated by phosphorylated tau, CSF biomarker, genetics, and environmental factors, in addition to Aβ plaque accumulation. Given additional clinical and laboratory data in the future, it would be possible to develop a predictive model that aggregates various predictive factors for AD in addition to improving the performance of the quantitative model in this study. Furthermore, if a suitable amount of data is collected for the study, the application of the CNN algorithm, which is recently playing an important role as an image processing method, is left for our future work.
## Conclusion
In this paper, we report on how to interpret dual-phase FBB using ML-based models and their evaluation results. In comparison with the AD classification, the model trained on mean SUVr extracted from dual-phase FBB imaging (ACC: 0.858, AUROC: 0.831) showed better AD classification than single-phase FBB, eFBB (ACC: 0.792, AUROC: 0.775), or dFBB (ACC: 0.821, AUROC: 0.794). In addition, the AD positivity score estimated by dual-phase FBB (RMMSE: −0.5412) shows a higher correlation with psychological test result compared to only dFBB (RMMSE: −0.2975). These experimental results show that the proposed method could be used to interpret eFBB in dual-phase FBB and that by reflecting eFBB into the current reading system, Aβ-PET reading, AD diagnosis, or the monitoring system could be improved.
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|
---
title: 'Impact of Resting Heart Rate on Cardiovascular Mortality According to Serum
Albumin Levels in a 24-year Follow-up Study on a General Japanese Population: NIPPON
DATA80'
authors:
- Yiwei Liu
- Aya Hirata
- Tomonori Okamura
- Daisuke Sugiyama
- Takumi Hirata
- Aya Kadota
- Keiko Kondo
- Takayoshi Ohkubo
- Katsuyuki Miura
- Akira Okayama
- Hirotsugu Ueshima
journal: Journal of Epidemiology
year: 2023
pmcid: PMC10043153
doi: 10.2188/jea.JE20210114
license: CC BY 4.0
---
# Impact of Resting Heart Rate on Cardiovascular Mortality According to Serum Albumin Levels in a 24-year Follow-up Study on a General Japanese Population: NIPPON DATA80
## Abstract
### Background
Elevated resting heart rate (RHR) is associated with an increased risk of cardiovascular disease (CVD) and all-cause mortality. However, the findings of cohort studies differed. Thus, the impact of RHR on CVD mortality might be different according to the background of the population. Therefore, we examined the relationship of RHR and CVD mortality according to serum albumin (ALB) levels in a *Japanese* general population.
### Methods
In total, 8,363 individuals without a history of CVD were followed for 24.0 years. The participants were divided into four groups according to the quartiles of RHR (Q1–Q4), and they were further classified into the high and low ALB groups based on a median value of 44 g/L. We estimated the multivariable-adjusted hazard ratios (HRs) of CVD mortality in each RHR group based on ALB levels, and the interaction between RHR and ALB groups on CVD mortality was evaluated.
### Results
We found no significant association between RHR and CVD mortality. However, the Q4 of RHR was significantly associated with an increased risk for CVD mortality (HR 1.27; $95\%$ confidence interval [CI], 1.02–1.57) in participants with a low ALB level. Meanwhile, the Q4 of RHR was significantly correlated with a decreased risk for CVD morality in those with a high ALB level (HR 0.61; $95\%$ CI, 0.47–0.79) after adjusting for covariates. A significant interaction between RHR and ALB for CVD mortality was shown ($P \leq 0.001$).
### Conclusion
The impact of RHR on CVD mortality differed according to ALB levels in a general Japanese population.
## INTRODUCTION
Elevated resting heart rate (RHR) is associated with an increased risk of coronary heart disease (CHD)1,2 heart failure (HF),3,4 cardiovascular disease (CVD),5,6 and all-cause mortality7,8 in the general population. This association is observed in all age groups in different clinical settings, irrespective of other atherosclerotic risk factors.9,10 However, the mechanism underlying the relationship between RHR and cause-specific mortality or morbidity remains unknown. To date, clinical trials have not shown the importance of slow RHR in hypertensive patients without CVD.10,11 Accordingly, comorbidities underlying a high RHR should be further validated. A high RHR is an index of increased sympathetic nervous system activity,12 which might be associated with the development of arrythmia, arterial stiffness, and hypertension. In the general population, sympathetic nervous system activity is commonly accelerated by individual responses to continuous mental stress.13 Further, lack of regular aerobic exercise, smoking, and heavy alcohol drinking are associated with a high RHR.14 Serum albumin (ALB) is a nutritional factor influencing general health status.15 Moreover, it has anti-inflammatory, antioxidant, anticoagulant, and antiplatelet aggregation properties. From a mechanism perspective, ALB is able to combine with nitric oxide (NO)16 to maintain a normal level. ALB is also involved in recovery from ischemia injury.17 ALB, even within the clinical normal range, can significantly influence the incidence of CVD and all-cause mortality.18 Such subclinical inflammation might increase RHR and decrease heart-rate variability, ultimately leading to mortality.19 A previous study has shown that nutritional intake influences RHR variability.20 However, to the best of our knowledge, the association of ALB level and RHR with mortality has not been comprehensively assessed. To validate this notion, we conducted a long-term cohort study to assess the impact of RHR on cause-specific mortality according to ALB levels.
## Study participants
We performed a cohort study based on the National Survey on Circulatory Disorders in 1980, which was referred to as the National Integrated Project for Prospective Observation of Non-communicable Diseases and Its Trends in the Aged, 1980 (NIPPON DATA 80). Details of the cohort are provided elsewhere.21–23 In brief, we randomly selected 300 districts in Japan, and a total of 10,546 participants (4,640 men and 5,906 women) aged >30 years participated in the baseline survey, with a participation rate of $77\%$. Details of baseline examinations and follow-up surveys are provided in eMaterials 1.
Of 10,546 participants, 2,183 were excluded due to the following reasons: history of CVD ($$n = 676$$), missing information at baseline survey ($$n = 598$$), and lost to follow-up because of incomplete access to residential information ($$n = 909$$). There was no selection bias in terms of baseline characteristics between participants with and without follow-up. Finally, 8,363 participants (3,666 men and 4,697 women) were included in the analysis.
## Ethical approval
The current study was conducted in accordance with the ethical guidelines of Shiga University of Medical Science (R2005-021) and Keio University School of Medicine [2018-0108].
## Statistical analysis
The participants were divided into four groups according to RHR (beats/min) quartiles (Q1–Q4) as follows: Q1, <62; Q2, 62–68; Q3, 69–77; and Q4, >77 beats/min in all participants; Q1, <60; Q2, 60–66; Q3, 67–73; and Q4, >73 beats in men; and Q1, <64; Q2, 64–70; Q3, 71–77; and Q4, >77 beats in women. The mean values and standard deviation were presented as continuous variables, and the number and proportion as categorical variables according to each RHR category. Continuous variables were compared between groups using one-way analysis of variance, and categorical variables were compared using the χ2-test. We assessed the age- and multivariable-adjusted hazard ratios (HRs) and $95\%$ confidence intervals (CIs) of each RHR category for all-cause death, total CVD death, cause-specific CVD mortality, and non-CVD mortality using a Cox proportional hazards model. Model 1 was adjusted for age; model 2 for the variable in model 1 plus body mass index, blood glucose levels, systolic blood pressure, hypertension treatment, total cholesterol level, smoking status, and alcohol drinking status; and model 3 for the variables in model 2 plus ALB level. Then, sex was adjusted in the sex-combined analysis. Further, we estimated the HRs of RHR quartiles according to ALB levels based on the median values (low: <44 and high: ≥44 g/L) for outcomes using models 1 and 2. In addition, we generated the interaction term by multiplying RHR as a continuous variable and ALB groups, and evaluated the interaction on all-cause, CVD, and non-CVD mortality in model 2 using a Cox proportional hazards. To validate the influence of arrhythmia on HR, we performed the same analysis with exclusion of individuals with atrial fibrillation or frequent supraventricular and/or ventricular premature beats. Two-sided P-values of <0.05 were considered statistically significant. For baseline characteristics, we used one-way ANOVA to test if covariates were distributed equally between groups. Statistical analysis was performed with the R package version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria).
## RESULTS
The characteristics of participants according to RHR quantiles at the baseline survey are shown in Table 1. The mean non-fasting blood glucose levels were high in both men and women with a high RHR. The proportion of individuals receiving hypertension treatment was highest among women in the group with the lowest RHR.
**Table 1.**
| Total (Quantiles in beats/min) | Q1 (<62) | Q2 (62–68) | Q3 (69–77) | Q4 (>77) | P value |
| --- | --- | --- | --- | --- | --- |
| N | 2225 | 2049 | 2138 | 1951 | — |
| Age, years | 51 (13) | 49 (13) | 49 (13) | 50 (14) | <0.001 |
| Sex, male, % | 59 | 44 | 37 | 34 | <0.001 |
| Smoking, % | 33 | 29 | 26 | 26 | <0.001 |
| Drinking, % | 39 | 38 | 35 | 32 | <0.001 |
| Body mass index, kg/m2 | 22 (2.9) | 23 (3.1) | 23 (3.5) | 23 (3.5) | 0.01 |
| Total cholesterol, mmol/L | 4.7 (0.8) | 4.7 (0.9) | 4.7 (0.9) | 4.8 (0.8) | <0.001 |
| Non-fasting blood glucose, mmol/L | 6.9 (1.6) | 7.1 (1.9) | 7.2 (1.8) | 7.7 (2.4) | <0.001 |
| Systolic blood pressure, mm Hg | 138 (23) | 135 (22) | 136 (23) | 139 (22) | <0.001 |
| Serum albumin, g/L | 44 (2.6) | 44 (2.6) | 44 (2.6) | 44 (2.8) | 0.04 |
| Hypertension treatment, % | 15.1 | 14 | 11 | 9.6 | 0.09 |
| Men (Quantiles in beats/min) | Q1 (<60) | Q2 (60–66) | Q3 (67–73) | Q4 (>73) | P value |
| N | 903 | 910 | 945 | 908 | — |
| Age, years | 51 (13) | 50 (13) | 49 (13) | 50 (13) | 0.08 |
| Smoking, % | 56.3 | 63.3 | 64.9 | 67.1 | <0.001 |
| Drinking, % | 73.3 | 75.1 | 74.1 | 75.8 | 0.82 |
| Body mass index, kg/m2 | 22 (2.8) | 22 (2.7) | 23 (2.8) | 23 (3.1) | <0.001 |
| Total cholesterol, mmol/L | 4.8 (0.8) | 4.8 (0.9) | 4.9 (0.8) | 5.0 (0.9) | <0.001 |
| Non-fasting blood glucose, mmol/L | 6.9 (1.7) | 7.1 (2.0) | 7.3 (1.8) | 7.8 (2.6) | <0.001 |
| Systolic blood pressure, mm Hg | 135 (21) | 137 (20) | 139 (21) | 143 (21) | <0.001 |
| Serum albumin, g/L | 44 (3.1) | 44 (3.0) | 45 (3.0) | 44 (3.0) | 0.002 |
| Hypertension treatment, % | 9.2 | 10.8 | 8.5 | 10.9 | 0.19 |
| Women (Quantiles in beats/min) | Q1 (<64) | Q2 (64–70) | Q3 (71–77) | Q4 (>77) | P value |
| N | 1273 | 1151 | 1102 | 1171 | — |
| Age, years | 52 (13) | 50 (13) | 50 (14) | 50 (14) | <0.001 |
| Smoking, % | 7.3 | 8.1 | 10.2 | 9.2 | 0.21 |
| Drinking, % | 20.9 | 20.8 | 20.5 | 17.7 | 0.33 |
| Body mass index, kg/m2 | 23 (3.0) | 23 (3.4) | 23 (3.5) | 23 (3.6) | 0.87 |
| Total cholesterol, mmol/L | 4.8 (0.9) | 4.9 (0.8) | 4.9 (0.9) | 5.0 (1.0) | 0.49 |
| Non-fasting blood glucose, mmol/L | 6.9 (1.5) | 7.1 (1.6) | 7.2 (1.9) | 7.6 (2.3) | <0.001 |
| Systolic blood pressure, mm Hg | 132 (22) | 131 (21) | 134 (21) | 137 (21) | <0.001 |
| Serum albumin, g/L | 43 (2.2) | 44 (2.3) | 44 (2.9) | 44 (2.9) | <0.001 |
| Hypertension treatment, % | 14.7 | 10.2 | 10.3 | 9.9 | <0.001 |
During a mean follow-up of 24.0 years, with 203,021 person-years of observation time, the total number of deaths was 3,128 (1,055 CVD deaths and 2,073 non-CVD deaths). Among CVD deaths, 219 were caused by CHD and 455 by stroke. The crude mortality rates and HRs for all-cause, CVD, and non-CVD mortality according to RHR quartiles are presented in Table 2. Compared with Q1, Q4 was significantly associated with an increased risk for all-cause mortality after adjusting for confounders in all participants (HR 1.15; $95\%$ CI, 1.04–1.28). In addition, Q4 was significantly associated with an increased risk for non-CVD mortality in both men and women (HR 1.21; $95\%$ CI, 1.03–1.41 vs HR 1.19; $95\%$ CI, 1.00–1.43). Meanwhile, RHR was not significantly associated with an increased risk for CVD mortality. ALB level was significantly associated with CVD mortality (HR 0.49; $95\%$ CI, 0.38–0.65).
**Table 2.**
| All-cause mortality | All-cause mortality.1 | All-cause mortality.2 | All-cause mortality.3 | All-cause mortality.4 | All-cause mortality.5 |
| --- | --- | --- | --- | --- | --- |
| Total | Quantiles (beats/min) | Q1 (<62) | Q2 (62–68) | Q3 (69–77) | Q4 (>77) |
| | Number of participants | 2225 | 2049 | 2138 | 1951 |
| | Person-years | 53526 | 51022 | 52743 | 46836 |
| | Number of events | 892 | 733 | 750 | 753 |
| | Crude mortality | 16.6 | 14.4 | 14.2 | 16.1 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.07 [0.97, 1.18] | 1.07 [0.97, 1.19] | 1.21 [1.10, 1.34]*** |
| | Model 2 | 1 | 1.03 [0.94, 1.14] | 1.03 [0.93, 1.14] | 1.11 [1.00, 1.23]* |
| | Model 3 | 1 | 1.04 [0.94, 1.15] | 1.04 [0.95, 1.15] | 1.15 [1.04, 1.28]* |
| Men | Quantiles (beats/min) | Q1 (<60) | Q2 (60–66) | Q3 (67–73) | Q4 (>73) |
| | Number of participants | 1044 | 920 | 794 | 908 |
| | Person-years | 24376 | 21914 | 19057 | 20579 |
| | Number of events | 591 | 375 | 327 | 338 |
| | Crude mortality | 24.2 | 17.1 | 17.1 | 16.4 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.05 [0.92, 1.21] | 0.99 [0.86, 1.14] | 1.21 [1.06, 1.37]** |
| | Model 2 | 1 | 0.98 [0.86, 1.12] | 0.95 [0.82, 1.09] | 1.10 [0.96, 1.25] |
| | Model 3 | 1 | 1.01 [0.88, 1.15] | 0.96 [0.83, 1.10] | 1.13 [0.99, 1.29] |
| Women | Quantiles (beats/min) | Q1 (<64) | Q2 (64–70) | Q3 (71–77) | Q4 (>77) |
| | Number of participants | 1273 | 1151 | 1102 | 1171 |
| | Person-years | 32002 | 29402 | 27637 | 29159 |
| | Number of events | 414 | 346 | 357 | 380 |
| | Crude mortality | 12.9 | 11.7 | 12.9 | 13.0 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.05 [0.90, 1.21] | 1.08 [0.94, 1.24] | 1.16 [1.01, 1.34]* |
| | Model 2 | 1 | 1.03 [0.89, 1.19] | 1.05 [0.91, 1.21] | 1.09 [0.94, 1.26] |
| | Model 3 | 1 | 1.05 [0.91, 1.21] | 1.09 [0.95, 1.26] | 1.15 [0.99, 1.33] |
| CVD mortality | CVD mortality | CVD mortality | CVD mortality | CVD mortality | CVD mortality |
| Total | Quantiles (beats/min) | Q1 (<62) | Q2 (62–68) | Q3 (69–77) | Q4 (>77) |
| | Number of participants | 2225 | 2049 | 2138 | 1951 |
| | Person-years | 53526 | 51022 | 52743 | 46836 |
| | Number of events | 315 | 230 | 260 | 250 |
| | Crude mortality | 5.9 | 4.5 | 4.9 | 5.3 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 0.93 [0.78, 1.10] | 1.02 [0.86, 1.20] | 1.07 [0.91, 1.27] |
| | Model 2 | 1 | 0.87 [0.74, 1.04] | 0.94 [0.80, 1.11] | 0.93 [0.78, 1.10] |
| | Model 3 | 1 | 0.88 [0.74, 1.05] | 0.96 [0.81, 1.14] | 0.98 [0.82, 1.16] |
| Men | Quantiles (beats/min) | Q1 (<60) | Q2 (60–66) | Q3 (67–73) | Q4 (>73) |
| | Number of participants | 1044 | 920 | 794 | 908 |
| | Person-years | 24376 | 21914 | 19057 | 20579 |
| | Number of events | 149 | 124 | 99 | 132 |
| | Crude mortality | 6.1 | 5.7 | 5.2 | 6.4 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.07 [0.84. 1.36] | 0.99 [0.77, 1.28] | 1.14 [0.90, 1.45] |
| | Model 2 | 1 | 0.96 [0.76, 1.22] | 0.90 [0.70, 1.17] | 0.96 [0.75, 1.22] |
| | Model 3 | 1 | 0.98 [0.77, 1.25] | 0.92 [0.71, 1.18] | 0.99 [0.78, 1.26] |
| Women | Quantiles (beats/min) | Q1 (<64) | Q2 (64–70) | Q3 (71–77) | Q4 (>77) |
| | Number of participants | 1273 | 1151 | 1102 | 1171 |
| | Person-years | 32002 | 29402 | 27637 | 29159 |
| | Number of events | 158 | 123 | 131 | 139 |
| | Crude mortality | 4.9 | 4.2 | 4.7 | 4.8 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 0.98 [0.77, 1.24] | 1.01 [0.80, 1.27] | 1.08 [0.86, 1.36] |
| | Model 2 | 1 | 0.97 [0.76, 1.23] | 0.99 [0.78, 1.26] | 0.99 [0.78, 1.26] |
| | Model 3 | 1 | 0.99 [0.78, 1.26] | 1.05 [0.83, 1.33] | 1.08 [0.85, 1.37] |
| Non-CVD mortality | Non-CVD mortality | Non-CVD mortality | Non-CVD mortality | Non-CVD mortality | Non-CVD mortality |
| Total | Quantiles (beats/min) | Q1 (<62) | Q2 (62–68) | Q3 (69–77) | Q4 (>77) |
| | Number of participants | 2225 | 2049 | 2138 | 1951 |
| | Person-years | 53526 | 51022 | 52743 | 46836 |
| | Number of events | 577 | 503 | 490 | 503 |
| | Crude mortality | 10.8 | 9.9 | 9.3 | 10.7 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.15 [1.02, 1.29]* | 1.10 [0.98, 1.25] | 1.28 [1.14, 1.45]*** |
| | Model 2 | 1 | 1.12 [0.99, 1.27] | 1.07 [0.95, 1.21] | 1.22 [1.08, 1.38]** |
| | Model 3 | 1 | 1.13 [1.01, 1.28]* | 1.09 [0.96, 1.23] | 1.26 [1.11, 1.42]*** |
| Men | Quantiles (beats/min) | Q1 (<60) | Q2 (60–66) | Q3 (67–73) | Q4 (>73) |
| | Number of participants | 1044 | 920 | 794 | 908 |
| | Person-years | 24376 | 21914 | 19057 | 20579 |
| | Number of events | 324 | 270 | 221 | 312 |
| | Crude mortality | 13.3 | 12.3 | 11.6 | 15.2 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.05 [0.89, 1.23] | 0.99 [0.83, 1.17] | 1.24 [1.06, 1.44]** |
| | Model 2 | 1 | 0.99 [0.85, 1.17] | 0.96 [0.81, 1.14] | 1.18 [1.01, 1.38]* |
| | Model 3 | 1 | 1.02 [0.86, 1.20] | 0.98 [0.82, 1.16] | 1.21 [1.03, 1.41]* |
| Women | Quantiles (beats/min) | Q1 (<64) | Q2 (64–70) | Q3 (71–77) | Q4 (>77) |
| | Number of participants | 1273 | 1151 | 1102 | 1171 |
| | Person-years | 32002 | 29402 | 27637 | 29159 |
| | Number of events | 256 | 223 | 226 | 241 |
| | Crude mortality | 8.0 | 7.6 | 8.2 | 8.3 |
| | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio | Hazard ratio |
| | Model 1 | 1 | 1.08 [0.91, 1.30] | 1.12 [0.94, 1.34] | 1.20 [1.01, 1.43]* |
| | Model 2 | 1 | 1.07 [0.89, 1.28] | 1.09 [0.91, 1.30] | 1.14 [0.95, 1.37] |
| | Model 3 | 1 | 1.08 [0.90, 1.30] | 1.12 [0.93, 1.34] | 1.19 [1.00, 1.43]* |
The crude mortality rates and HRs for all-cause, CVD, and non-CVD mortality according to RHR quartiles in the low and high ALB level groups are depicted in Table 3. In the low ALB level group, compared with Q1, Q4 was significantly associated with an increased risk for all-cause mortality after adjusting for confounders in all participants and women (HR 1.35; $95\%$ CI, 1.18–1.53 vs HR 1.24; $95\%$ CI, 1.03–1.49). Moreover, a correlation was observed between Q4 and an increased risk for CVD mortality in the low ALB level group (HR 1.27; $95\%$ CI, 1.02–1.57). Meanwhile, Q4 was significantly associated with a decreased risk for CVD morality in all participants in the high ALB level group (HR 0.61; $95\%$ CI, 0.47–0.79). Moreover, Q4 was significantly correlated with an increased risk for non-CVD mortality in all participants in the low ALB level group (HR 1.27; $95\%$ CI, 1.07–1.50). A significant interaction between RHR and ALB for CVD mortality was shown ($P \leq 0.001$); however, such an interaction was not evident for non-CVD mortality ($$P \leq 0.31$$). After excluding individuals with atrial fibrillation ($$n = 57$$) and frequent supraventricular and/or ventricular premature beats ($$n = 99$$), or those who received antihypertensive treatment, the results remained did not change significantly (data not shown).
**Table 3.**
| All-cause mortality | Low albumin | Low albumin.1 | Low albumin.2 | Low albumin.3 | High albumin | High albumin.1 | High albumin.2 | High albumin.3 | Unnamed: 9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| All-cause mortality | | | | | | | | | |
| Total(RHR Quantile in beats/min) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | P-values for interaction |
| Number of participants | 1007 | 896 | 910 | 797 | 1218 | 1153 | 1228 | 1154 | |
| Person-years | 21936 | 31589 | 20661 | 30361 | 20863 | 31879 | 17253 | 29582 | |
| Number of deaths | 533 | 418 | 427 | 411 | 359 | 315 | 323 | 342 | |
| Crude mortality rate/1,000 person-years | 24.3 | 13.2 | 20.7 | 13.5 | 17.2 | 9.9 | 18.7 | 11.6 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 0.82[0.72, 0.94]** | 0.83[0.73, 0.94]** | 0.98[0.86, 1.11] | 0.45[0.39, 0.51]*** | 0.40[0.35, 0.47]*** | 0.40[0.34, 0.45]*** | 0.45[0.40, 0.52]*** | |
| Model 2 | 1 | 1.02[0.89, 1.16] | 1.12[0.99, 1.28] | 1.35[1.18, 1.53]*** | 0.85[0.74, 0.97]* | 0.98[0.85, 1.13] | 0.88[0.77, 1.02] | 0.96[0.83, 1.10] | P = 0.007 |
| Men(RHR Quantile in beats/min) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | |
| Number of participants | 424 | 328 | 270 | 325 | 620 | 592 | 524 | 583 | |
| Person-years (total) | 8350 | 6483 | 5486 | 6048 | 16025 | 15430 | 13570 | 14530 | |
| Number of deaths | 283 | 220 | 175 | 234 | 190 | 174 | 145 | 210 | |
| Crude mortality rate/1,000 person-years | 33.9 | 33.9 | 31.9 | 38.7 | 11.9 | 11.3 | 10.7 | 14.5 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.05[0.88, 1.25] | 0.99[0.82, 1.20] | 1.26[1.06, 1.50]** | 0.78[0.64, 0.94]** | 0.84[0.69, 1.02] | 0.78[0.64, 0.96]* | 0.94[0.78, 1.12] | |
| Model 2 | 1 | 0.99[0.83, 1.18] | 0.94[0.78, 1.14] | 1.18[0.99, 1.40] | 0.85[0.69, 1.02] | 0.83[0.68, 1.02] | 0.81[0.65, 0.99]* | 0.90[0.74, 1.08] | P = 0.25 |
| Women(RHR Quantile in beats/min) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | |
| Number of participants | 670 | 581 | 504 | 508 | 603 | 570 | 598 | 663 | |
| Person-years (total) | 16065 | 14380 | 12081 | 11818 | 15936 | 15022 | 15555 | 17340 | |
| Number of deaths | 257 | 204 | 202 | 214 | 157 | 142 | 155 | 166 | |
| Crude mortality rate/1,000 person-years | 16.0 | 14.2 | 16.7 | 18.1 | 9.9 | 9.5 | 10.0 | 9.6 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.03[0.85, 1.24] | 1.12[0.93, 1.35] | 1.29[1.08, 1.55]** | 0.88[0.72, 1.07] | 0.95[0.77, 1.17] | 0.93[0.76, 1.14] | 0.93[0.77, 1.13] | |
| Model 2 | 1 | 1.03[0.86, 1.23] | 1.11[0.92, 1.34] | 1.24[1.03, 1.49]* | 0.89[0.73, 1.09] | 0.95[0.77, 1.16] | 0.90[0.74, 1.11] | 0.87[0.71, 1.06] | P = 0.01 |
| CVD mortality | Low albumin | Low albumin | Low albumin | Low albumin | High albumin | High albumin | High albumin | High albumin | |
| Total(RHR Quantile in beats/min) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | |
| Number of participants | 1007 | 896 | 910 | 797 | 1218 | 1153 | 1228 | 1154 | |
| Person-years (total) | 21936 | 31589 | 20661 | 30361 | 20863 | 31879 | 17253 | 29582 | |
| Number of deaths | 193 | 139 | 141 | 159 | 122 | 91 | 119 | 91 | |
| Crude mortality rate/1,000 person-years | 8.8 | 4.4 | 6.8 | 5.2 | 5.8 | 2.9 | 6.9 | 3.1 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 0.91[0.73, 1.13] | 0.99[0.80, 1.23] | 1.35[1.09, 1.67]** | 0.92[0.73, 1.16] | 0.89[0.69, 1.16] | 0.98[0.77, 1.23] | 0.75[0.58, 0.96]* | |
| Model 2 | 1 | 0.87[0.70, 1.09] | 0.95[0.76, 1.19] | 1.27[1.02, 1.57]* | 0.95[0.76, 1.20] | 0.83[0.64, 1.07] | 0.88[0.70, 1.12] | 0.61[0.47, 0.79]*** | P < 0.001 |
| Men(RHR Quantile in beats/min) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | |
| Number of participants | 424 | 328 | 270 | 325 | 620 | 592 | 524 | 583 | |
| Person-years (total) | 8350 | 6483 | 5486 | 6048 | 16025 | 15430 | 13570 | 14530 | |
| Number of deaths | 283 | 220 | 175 | 234 | 190 | 174 | 145 | 210 | |
| Crude mortality rate/1,000 person-years | 33.9 | 33.9 | 31.9 | 38.7 | 11.9 | 11.3 | 10.7 | 14.5 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.14[0.84, 1.55] | 0.92[0.65, 1.30] | 1.32[0.97, 1.79] | 0.93[0.67, 1.30] | 0.92[0.64, 1.31] | 1.01[0.71, 1.45] | 0.91[0.65, 1.28] | |
| Model 2 | 1 | 1.07[0.78, 1.45] | 0.85[0.60, 1.20] | 1.18[0.87, 1.61] | 1.00[0.71, 1.41] | 0.84[0.59, 1.21] | 0.97[0.67, 1.40] | 0.75[0.53, 1.07] | P = 0.25 |
| Women(RHR Quantile in beats/min) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | |
| Number of participants | 670 | 581 | 504 | 508 | 603 | 570 | 598 | 663 | |
| Person-years (total) | 16065 | 14380 | 12081 | 11818 | 15936 | 15022 | 15555 | 17340 | |
| Number of deaths | 257 | 204 | 202 | 214 | 157 | 142 | 155 | 166 | |
| Crude mortality rate/1,000 person-years | 16.0 | 14.2 | 16.7 | 18.1 | 9.9 | 9.5 | 10.0 | 9.6 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 0.97[0.72, 1.31] | 0.96[0.71, 1.30] | 1.32[0.99, 1.75] | 0.85[0.62, 1.19] | 0.86[0.61, 1.22] | 0.95[0.69, 1.31] | 0.72[0.51, 1.02] | |
| Model 2 | 1 | 0.97[0.72, 1.32] | 0.98[0.72, 1.33] | 1.32[0.98, 1.76] | 0.83[0.60, 1.16] | 0.82[0.58, 1.17] | 0.88[0.64, 1.22] | 0.59[0.42, 0.85]** | P = 0.002 |
| Non-CVD death | Low albumin | Low albumin | Low albumin | Low albumin | High albumin | High albumin | High albumin | High albumin | |
| Total(RHR Quantile in beats/min) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | Q1(<62) | Q2(62–68) | Q3(69–77) | Q4(>77) | |
| Number of participants | 1007 | 896 | 910 | 797 | 1218 | 1153 | 1228 | 1154 | |
| Person-years (total) | 21936 | 31589 | 20661 | 30361 | 20863 | 31879 | 17253 | 29582 | |
| Number of deaths | 340 | 279 | 286 | 252 | 237 | 224 | 204 | 251 | |
| Crude mortality rate/1,000 person-years | 15.5 | 8.8 | 13.8 | 8.3 | 11.4 | 7.0 | 11.8 | 8.5 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.08[0.92, 1.27] | 1.20[1.02, 1.40]* | 1.33[1.13, 1.56]*** | 0.82[0.69, 0.97]* | 1.02[0.86, 1.21] | 0.83[0.70, 0.99]* | 1.06[0.90, 1.25] | |
| Model 2 | 1 | 1.07[0.91, 1.25] | 1.16[0.99, 1.36] | 1.27[1.07, 1.50]** | 0.87[0.73, 1.03] | 1.05[0.88, 1.25] | 0.85[0.71, 1.02] | 1.05[0.89, 1.24] | P = 0.31 |
| Men(RHR Quantile in beats/min) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | Q1(<60) | Q2(60–66) | Q3(67–73) | Q4(>73) | |
| Number of participants | 424 | 328 | 270 | 325 | 620 | 592 | 524 | 583 | |
| Person-years (total) | 8350 | 6483 | 5486 | 6048 | 16025 | 15430 | 13570 | 14530 | |
| Number of deaths | 89 | 75 | 51 | 77 | 60 | 49 | 48 | 55 | |
| Crude mortality rate/1,000 person-years | 10.7 | 11.6 | 9.3 | 12.7 | 3.7 | 3.2 | 3.5 | 3.8 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.01[0.81, 1.25] | 1.02[0.82, 1.28] | 1.24[1.01, 1.53]* | 0.72[0.57, 0.90]** | 0.80[0.63, 1.01] | 0.70[0.54, 0.90]** | 0.94[0.75, 1.16] | |
| Model 2 | 1 | 0.96[0.77, 1.20] | 0.99[0.78, 1.24] | 1.18[0.95, 1.46] | 0.79[0.62, 0.99]* | 0.83[0.66, 1.05] | 0.75[0.58, 0.97]* | 0.97[0.77, 1.21] | P = 0.52 |
| Women(RHR Quantile in beats/min) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | Q1(<64) | Q2(64–70) | Q3(71–77) | Q4(>77) | |
| Number of participants | 670 | 581 | 504 | 508 | 603 | 570 | 598 | 663 | |
| Person-years (total) | 16065 | 14380 | 12081 | 11818 | 15936 | 15022 | 15555 | 17340 | |
| Number of deaths | 155 | 128 | 131 | 123 | 101 | 95 | 95 | 118 | |
| Crude mortality rate/1,000 person-years | 9.6 | 8.9 | 10.8 | 10.4 | 6.3 | 6.3 | 6.1 | 6.8 | |
| Hazard ratio | | | | | | | | | |
| Model 1 | 1 | 1.07[0.84, 1.35] | 1.22[0.96, 1.54] | 1.25[0.99, 1.59] | 0.89[0.70, 1.15] | 1.01[0.78, 1.30] | 0.92[0.71, 1.19] | 1.06[0.83, 1.35] | |
| Model 2 | 1 | 1.05[0.83, 1.33] | 1.19[0.94, 1.51] | 1.18[0.93, 1.50] | 0.93[0.73, 1.20] | 1.02[0.79, 1.33] | 0.92[0.71, 1.19] | 1.05[0.82, 1.34] | P = 0.46 |
## DISCUSSION
The current study found no significant association between RHR and CVD mortality. In the analysis stratified according to ALB levels, a higher RHR was significantly associated with an increased risk for CVD mortality among individuals with low ALB levels. Meanwhile, in those with high ALB levels, a higher RHR was correlated with a lower risk for CVD mortality. A positive association was observed between RHR and non-CVD mortality, and a higher RHR was significantly associated with a greater risk among individuals with low ALB levels. However, this result was not observed among individuals with high ALB levels. Thus, the effect of RHR on outcomes, particular on CVD mortality, differed according to ALB levels.
In the current study, we did not observe a direct association between a high RHR and CVD mortality in all participants. In previous cohort studies, meanwhile, RHR was found to be associated with an increased risk for CVD and all-cause mortality in the general population.10,11,24 As we previously reported, an elevated RHR was found to be correlated with cardiovascular or all-cause mortality in a 16.5-year follow-up. However, it was only observed in participants aged 30–59 years.23 *In previous* cohort studies on a *Japanese* general population with a mean age of about 45 years at baseline, a higher RHR was associated with the development of obesity and diabetes within 20 years25 and hypertension within 3 years.26 These findings suggest that the activation of the sympathetic nervous system correlated with a high RHR might play a role in the development of these conditions in relatively younger populations. Furthermore, participants with an activated sympathetic nervous system may develop adverse events in the early follow-up period.
Regarding the effect of reducing RHR, previous studies have reported that all-cause mortality can be reduced in patients with HF by lowering RHR with the use of medications, such as beta-blockers.27 However, no findings showed the additional value of RHR-lowering therapy with beta-blockers in hypertensive patients without CVD.10,11,24 *In a* consensus conference wherein recommendations for the management of hypertensive patients with increased RHR were updated, gathering panelists reconfirmed that RHR-lowering therapy had no additional benefit for tachycardia hypertension. They recommended that further research should be performed to validate unresolved discrepancies between the findings of cohort studies and clinical trials.28 Accordingly, the effect of RHR on mortality is controversial, which might be caused by the characteristics of participants.
In our analysis stratified according to ALB levels, a higher RHR was significantly associated with CVD mortality and non-CVD mortality among individuals with low ALB levels. Since ALB is a marker of anti-inflammatory, antioxidant, anticoagulant, and antiplatelet aggregation activities, as well as nutritional status, it might be correlated with the risk for mortality.29 In the present study, individuals with lower ALB levels could present with subclinical changes in health status, although they might not experience actual malnutrition. However, the protein intake proportion showed no significant difference between ALB groups (mean 15.30; standard deviation [SD], 2.12 g/1,000 kcal in low ALB vs mean 15.27; SD, 2.10 g/1,000 kcal in high ALB) in the present study, a significant difference was shown in total energy intake (mean 2,063; SD, 491 kcal/day in low ALB vs mean 2,193; SD, 510 kcal/day in high ALB, $P \leq 0.001$). Hypoalbuminemia is associated with an increased risk of death in HF patients with reduced ejection fraction, in whom the magnitude of heart rate reduction is associated with the survival benefit of beta-blockers.30 Moreover, ALB is associated with myocardial fibrosis, adverse pulsatile aortic hemodynamics, and prognosis in HF patients with preserved ejection fraction.31 There are individuals with latent HF even in the general population.32 ALB has protective effects against low-density lipoprotein cholesterol from Cu2+ oxidation, which is a crucial step for atherogenesis, in addition to HF.33 Glycated or oxidated ALB can no longer hold copper ion.34 Abnormal ALB levels can decrease the ALB/high-sensitivity C-reactive protein ratio, and this proinflammation imbalance can result in the development or worsening of coronary slow flow.35 Overall, a normal ALB level is crucial for preventing CVD, and low ALB levels caused by high oxidative levels and glycation stress disrupt anti-atherogenesis functioning. Such a strong effect might account for the impact of RHR. Accordingly, individuals with health impairment might be at risk for a high RHR, which is considered a marker of mortality. Also, if we take a deeper subgroup analysis for cause-specific mortality in CVD, the cerebral infarction made a great contribution to it (eTable 2). In this analysis, ALB level differently impacted males and females according to RHR quantile, which could explain why RHR quantile elevation caused both increase and decrease in ALB in the stratified CVD mortality model. This is similar to a previous study in Japan.36 On the contrary, in the present study, a lower risk in higher RHR was shown among individuals with high ALB. This result might be controversial compared to previous evidence. Actually, there is a little physiological evidence supporting our results. Some studies have shown that lower RHR was associated with left ventricular hypertrophy (LVH) progression.37,38 LVH is a risk factor for CVD events and all-cause mortality even in a general population. The present study might suggest that lower RHR among individuals with high ALB increases risk of CVD mortality mediated by potential LVH progression. We conducted an additional analysis considering recorded findings of left high R-wave (l-high-R) on electrocardiogram, which was performed as the National Survey on Circulatory Disorders in 1980. Participants with lower RHR had higher proportion of those with l-high-R. However, the association between RHR and CVD mortality was almost unchanged after adjusting for l-high-R, irrespective of ALB levels. Another explanation is that, while elevated heart rate was associated with increased peripheral blood pressure, there was an inverse relationship between resting heart rate and augmentation index,39 and elevated augmentation index and consequently increased central blood pressure were risk factors for CVD events.40,41 Particularly, the inverse association was more apparent in higher level of aortic stiffness among normotensive and untreated hypertensive participants.42 Furthermore, in the CAFÉ study,37 the use of a beta-blocker was associated with RHR reduction and central aortic pressure elevation simultaneously, despite similar brachial systolic blood pressure. These findings suggest that RHR might reflect central hemodynamics, which relate to CVD events. Additionally, when we take a closer look to cause-specific CVD mortality influenced by RHR at high ALB, they were mostly from CHD and cerebral infarction, sequelae of atherosclerotic cardiovascular disease. As described earlier, ALB has anti-atherosclerotic properties, such as anti-inflammatory and antioxidant effects, as well as being a marker of nutritional status; therefore, individuals with relatively higher ALB would be protected against atherosclerosis. Accordingly, we speculate that RHR at high ALB has a different impact on CVD from that of low ALB. However, there is little evidence on the relationship between RHR and ALB, which may include many unexamined confounding factors, such as socioeconomic status and mental stress. Although the background on the results is not clear, we believe our results are important because this is a first finding, at least to our knowledge. Future studies are needed to clarify the clinical significance of RHR.
The current study had several limitations. First, we can only adjust some basic confounders in mortality models. Other potential risk factors, such as thyroid hormone function, physical activity, high-density lipoprotein cholesterol, and inflammatory marker levels could not be adjusted in the statistical models. Second, since this study was based on risk factor measurement on one occasion only, our findings did not reflect changes in RHR and confounding risk factors, such as blood pressure and serum glucose level during follow-up. Third, we did not have a precise information on the treatment of hypertension; thus, participants who used beta-blockers were not identified. Finally, we measured RHR via electrocardiogram during daytime, and data on RHR obtained during nighttime or daytime immediately after waking were not available. A follow-up study on six populations in several countries, including Japan, used the RHR obtained during office measurements, and data on 24-h ambulatory blood pressure were used. Results showed that morning and nighttime, not daytime, RHRs were associated with CVD mortality.37
## Conclusion
The impact of RHR on CVD mortality differed according to ALB levels in a general Japanese population. Such an effect might depend on health status, including nutrition. Thus, further studies must be conducted to elucidate the underlying mechanism.
## SUPPLEMENTARY MATERIAL
The following is the supplementary data related to this article: eTable 1. Baseline characteristics for participants eTable 2. Crude mortality rates and hazard ratios for CHD, stroke, cerebral infarction and cerebral hemorrhage mortality according to RHR quantiles in low and high serum ALB eMaterials 1. Baseline examinations
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|
---
title: 'Dietary magnesium and calcium intake is associated with lower risk of hearing
loss in older adults: A cross-sectional study of NHANES'
authors:
- Xinmin Wei
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043168
doi: 10.3389/fnut.2023.1101764
license: CC BY 4.0
---
# Dietary magnesium and calcium intake is associated with lower risk of hearing loss in older adults: A cross-sectional study of NHANES
## Abstract
### Aim
Dietary intake as a modifiable factor has been reported to be associated with hearing loss (HL). The relationship between magnesium (Mg) and calcium (Ca) as common dietary nutrients and HL in the elderly has rarely been reported. This study aimed to assess the association between Mg and Ca intake and HL in older adults.
### Method
This cross-sectional study included participants aged ≥70 years from the National Health and Nutrition Examination Survey (NHANES) 2005–2006, 2009–2010, and 2017–2018. Outcomes were low-frequency [pure-tone averages (PTAs) at 500, 1000, and 2000 Hz >25 dB] and speech-frequency (PTAs at 500, 1000, 2000, and 4,000 Hz >25 dB) HL. Multivariate logistic analysis was utilized to explore the association between dietary Mg and Ca intake and their combined intake (Ca/Mg, Ca*Mg) and HL, and was described as odds ratio (OR) and $95\%$ confidence interval (CI).
### Results
A total of 1,858 participants were included, of which 1,052 ($55.95\%$) had low-frequency HL and 1,349 ($72.62\%$) had speech-frequency HL. Dietary Ca intakes [OR = 0.86, $95\%$CI: (0.74–0.99)] and Mg intakes [OR = 0.81, $95\%$CI: (0.68–0.95)] and Ca * Mg [OR = 0.12, $95\%$CI: (0.02–0.87)] were associated with lower odds of low-frequency HL after adjusting for confounders. Similar, dietary Ca intakes [OR = 0.85, $95\%$CI: (0.77–0.95)] and Mg intakes [OR = 0.78, $95\%$CI: (0.68–0.90)] and Ca * Mg [OR = 0.23, $95\%$CI: (0.05–0.78)] were related to lower odds of speech-frequency HL. For different levels of Mg and Ca intake, the combined intake of Ca (≥1,044 mg) and Mg (≥330 mg) was related to lower odds of low-frequency HL [OR = 0.02, $95\%$CI: (0.00–0.27)] and speech-frequency HL [OR = 0.44, $95\%$CI: (0.21–0.89)].
### Conclusion
Dietary intakes of Mg and Ca were associated with lower odds of HL and are a promising intervention to be further explored in older adults with HL.
## Introduction
Approximately 360 million people suffer from permanent hearing loss (HL) worldwide, accounting for $5.3\%$ of the global population [1]. The prevalence of HL is rapidly rising due to an aging population, increasingly noisy environments, and increased use of hearing devices [2]. Several studies have shown that more than half of people over the age of 70 suffer from age-related HL [3, 4]. HL affects elderly people’s daily life and quality of life, negatively affects their mental health [5, 6], and the health costs consumed by the prevention and treatment of HL also put pressure on socioeconomic development [7, 8]. Identifying modifiable factors contributing to HL and good health management of HL are important to reduce the burden of HL in the elderly.
Age-related HL experienced by older adults usually starts with high-frequency HL and gradually affects the middle and low frequencies [9]. Škerková et al. showed that hearing thresholds increase significantly with age, and people over 50 years of age can only perceive sounds at 11.2 kHz [10]. Moreover, once HL has progressed into the 2–4 kHz range, speech understanding in any situation is compromised [9]. It is necessary to explore the relevant factors affecting the low- and middle-frequency Hl in the elderly. Studies have shown that risk factors for HL include age, smoking, and noise exposure [11]. Recently, the relationship between dietary intake and HL has received attention, and good nutritional status can help prevent or repair initial HL [12]. Choi et al. found that dietary magnesium (Mg) intakes may contribute to lower hearing thresholds [13]. Jung et al. reported that higher dietary Mg intake was associated with a lower incidence of HL [12]. This may be due to the fact that Mg can reduce HL caused by noise-induced vasoconstriction and free radical formation [14, 15]. Furthermore, calcium dysregulation is a well-recognized cause of noise HL [16], and there is an antagonistic effect between Ca and Mg [17]. Ca levels affect bone density, decreased bone density is common in older adults. Curhan et al. found that osteoporosis caused by decreased bone density may increase the risk of HL [18]. However, the association between dietary Mg and Ca intakes and HL at different frequency HL in the elderly remain unclear.
Therefore, the purpose of this study was to examine the association between dietary Ca and Mg intakes and the risk of HL and their interaction effects. From the perspective of improving eating habits, explore and propose health management countermeasures, to lay a theoretical foundation for hearing loss health management services.
## Study population
Data for this cross-sectional study were extracted from the National Health and Nutrition Examination Survey (NHANES) 2005–2006, 2009–2010, and 2017–2018 [19]. NHANES was conducted by the National Centers for Health Statistics (NCHS), the Centers for Disease Control and Prevention (CDC) to assess health and nutritional information for a representative sample of the U.S. civilian, noninstitutionalized U.S. population [20]. The NHANES survey uses complex, multistage, probability sampling methods based on broad population distributions. Each survey cycle in the NHANES database focused on the hearing status of participants in a specific age group [21]. For example, the 2007–2008 survey focused on the hearing status of adolescents aged 12–19 years, the 2009–2010 survey focused on the hearing status of adolescents aged 12–19 years and seniors aged 70 years and older, and the 2011–2012 survey focused on the hearing status of adults aged 20–69 years. After the screening, only three survey cycles of 2005–2006, 2009–2010, and 2017–2018 focused on the hearing status of participants aged 70 and above.
A total of 3,935 eligible participants aged ≥70 who received audiometric examination was in NHANES 2005–2006, 2009–2010, and 2017–2018. Participants were excluded for nonresponse or unavailable response at the audiometric examination ($$n = 7$$,243). Moreover, participants with missing information on dietary and zero total energy intake ($$n = 205$$) were excluded. Finally, 1,858 eligible participants were included in the analysis. NHANES is a publicly available dataset and was approved by the NCHS Ethics Review Board, and all participants provided written informed consent.
## Outcome variable
Audiometry examination was performed in a dedicated sound-isolating room in the Mobile Examination Center by trained examiners on participants. The outcome variables of this study were low-frequency HL and speech-frequency HL. Low-frequency HL was defined as the pure-tone averages (PTAs) at 500, 1000, and 2000 Hz >25 dB HL in either ear and speech-frequency HL was defined as PTAs at 500, 1,000, 2,000, and 4,000 Hz >25 dB HL in either ear [22]. For patients with low-frequency HL, the control group was all participants except those with low-frequency HL. For patients with speech-frequency HL, the control group was all participants except those with speech-frequency HL. Among patients with both low-frequency and speech-frequency HL, the control group was participants with only low-frequency or speech-frequency HL, and participants with neither low-frequency nor speech-frequency HL.
## Intake of calcium and magnesium
Dietary Ca, Mg, vitamin C, vitamin E, and dietary supplement intakes were estimated by 24-h dietary recall interviews. Diet recall interviews require respondents to report all food and beverages (other than regular drinking water) consumed in the 24 h prior to the interview, the quantity of food reported, and a detailed description of the food [23]. Food consumption data were converted to United States Department of Agriculture (USDA) standard reference codes, and food intake was linked to the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS) [23, 24].
## Covariates
Demographic variables included age (70–80/ ≥80 years), sex (male/ female), race (Mexican American/ other Hispanic/ non-Hispanic White/ non-Hispanic Black/ other race), marital status (married/ widowed/ divorced or separated/never married), body mass index (BMI), education level (less than high school/high school/more than high school), physical activity, and ratio of family income to poverty (PIR). PIR was categorized as ≤130, >130 to $350\%$, and ≥ $350\%$ by the federal poverty level (FPL) [25]. The FPL reflects income relative to household size and was used as an indicator of socioeconomic status [26]. BMI was calculated as weight (kg)/height (m)2. Hypertension, diabetes mellitus and cerebrovascular disease (CVD) were defined as self-reported physician diagnoses. Smoking status was defined as smoking at least 100 cigarettes in life. Loud noise exposure in past 24-h was assessed by the question of “Outside of a job, have you ever been exposed to steady loud noise or music for 5 or more hours a week? *This is* noise so loud that you have to raise your voice to be heard. Examples are noise from power tools, lawn mowers, farm machinery, cars, trucks, motorcycles, or loud music.” Hear protection was determined by the survey item, “How often do you wear hearing protection devices (ear plugs, ear muffs) when exposed to loud sounds or noise? ( Include both job- and off-work exposures),” and it was divided into four groups: most of the time, sometimes, rarely/seldom and never. Loud noise exposure at work was participants ever had a job where exposed to loud noise for 5 or more hours a week. Physical activity was expressed as the metabolic equivalent task (MET) and calculated as follows: physical activity (met·min/week) = recommended MET × exercise time for corresponding activities (min/day) × the number of exercise days per week (day) [27]. Ototoxic drug use was identified based on participants’ self-reported use of the following drugs: aminoglycosides, macrolides, antineoplastic drugs, loop diuretics, salicylates, and antimalarials [28].
## Statistical analysis
The study population was divided into two groups according to whether HL, characteristics were performed for comparison between groups. Continuous data were expressed as mean and standard error (S.E.), and the weighted t-test was used for comparison between groups. Categorical variables were described as the number and percentage [n (%)], and comparisons between groups used the weighted χ2 test. Missing values were filled using multiple imputations. Weighted logistic multivariate analysis was utilized to explore the association between dietary Ca intakes, dietary Mg intakes, Ca/Mg and Ca*Mg and HL. Model 1 was the crude model. Model 2 adjusted for age, sex, race/ethnicity, marital status, education level, and PIR. Model 3 adjusted for age, sex, race/ethnicity, marital status, education level, PIR, loud noise exposure in the past 24 h, loud noise exposure at work, total energy intake, vitamin C intake, vitamin E intake, physical activity, ototoxic medication use, and dietary supplement. Sensitivity analysis was performed to compare whether the results were different before and after data imputation. Moreover, Ca and Mg levels were categorized into quartiles to explore the relationship between different levels of Ca and Mg intake and HL. Odds ratio (OR) and $95\%$ confidence interval (CI) were used to assess the association. All statistical analyzes were performed using R v. 4.20 (R Foundation for Statistical Computing, Vienna, Austria) and SAS v. 9.4 (SAS Institute, Cary, North Carolina) software. A p-value <0.05 (two-sided) was considered statistically significant.
## Characteristics of the study population
A total of 1,858 participants were included in the study. Characteristics of included participants were shown in Table 1. Of these participants, 1,052 ($55.95\%$) had low-frequency HL, 1,349 ($72.62\%$) had speech-frequency HL, and 1,046 ($55.68\%$) had both low-and speech-frequency HL. There were 1,220 ($65.32\%$) participants aged 70–80 years, 950 ($51.37\%$) male participants, and 1,268 ($68.10\%$) non-Hispanic white participants. The mean (S.E.) BMI was 28.52 (0.13) kg/m2. The mean (S.E.) Ca and Mg intakes were 825.84 (10.20) mg and 268.69 (3.23) mg, respectively. Dietary supplements were used by 1,503 ($80.11\%$) participants. There were 228 ($12.15\%$) participants who had loud noise exposure in the past 24 h and 651 ($34.95\%$) participants who had loud noise exposure at work.
**Table 1**
| Variables | Total (n = 1858) | Low-frequency HL | Low-frequency HL.1 | P | Speech-frequency HL | Speech-frequency HL.1 | P.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Total (n = 1858) | No (n = 806) | Yes (n = 1,052) | P | No (n = 509) | Yes (n = 1,349) | P |
| Age, n (%) | | | | <0.001 | | | <0.001 |
| 70–80 years | 1,220 (65.32) | 650 (80.67) | 570 (54.46) | | 436 (86.30) | 784 (58.36) | |
| ≥80 years | 638 (34.68) | 156 (19.33) | 482 (45.54) | | 73 (13.70) | 565 (41.64) | |
| Race/ethnicity, n (%) | | | | <0.001 | | | <0.001 |
| Mexican American | 148 (7.78) | 63 (7.45) | 85 (7.69) | | 45 (8.65) | 103 (7.18) | |
| Other Hispanic | 72 (4.04) | 41 (5.01) | 31 (2.95) | | 25 (4.67) | 47 (3.55) | |
| Non-Hispanic White | 1,268 (68.10) | 506 (63.67) | 762 (73.80) | | 288 (57.62) | 980 (73.76) | |
| Non-Hispanic Black | 278 (14.92) | 155 (18.82) | 123 (11.30) | | 118 (22.94) | 160 (11.47) | |
| Other race | 92 (5.15) | 41 (5.06) | 51 (4.27) | | 33 (6.13) | 59 (4.04) | |
| Sex, n (%) | | | | 0.059 | | | <0.001 |
| Male | 908 (48.63) | 378 (46.08) | 530 (50.18) | | 195 (37.88) | 713 (52.33) | |
| Female | 950 (51.37) | 428 (53.92) | 522 (49.82) | | 314 (62.12) | 636 (47.67) | |
| Marital status, n (%) | | | | <0.001 | | | 0.022 |
| Married | 1,001 (53.65) | 452 (56.01) | 549 (53.51) | | 270 (53.15) | 731 (55.16) | |
| Widowed | 572 (30.67) | 205 (25.42) | 367 (33.34) | | 139 (27.24) | 433 (30.84) | |
| Divorced/separated | 220 (12.06) | 118 (14.67) | 102 (9.81) | | 78 (15.39) | 142 (10.65) | |
| Never married | 65 (3.62) | 31 (3.90) | 34 (3.33) | | 22 (4.22) | 43 (3.34) | |
| BMI, kg/m2, Mean (S.E) | 28.52 (0.13) | 28.80 (0.22) | 28.37 (0.15) | 0.090 | 28.63 (0.32) | 28.54 (0.14) | 0.809 |
| Education level, n (%) | | | | <0.001 | | | <0.001 |
| Less than high school | 540 (29.13) | 201 (24.07) | 339 (31.65) | | 113 (21.57) | 427 (30.86) | |
| High school | 491 (26.49) | 208 (25.71) | 281 (26.63) | | 125 (24.53) | 364 (26.87) | |
| More than high school | 827 (44.38) | 397 (50.21) | 432 (41.72) | | 271 (53.89) | 558 (42.28) | |
| PIR, n (%) | | | | 0.047 | | | 0.229 |
| <130 | 432 (23.05) | 204 (24.23) | 312 (28.33) | | 129 (23.88) | 387 (27.52) | |
| 130–350% | 906 (49.08) | 373 (47.49) | 499 (48.27) | | 237 (47.92) | 635 (47.93) | |
| ≥350% | 520 (27.87) | 229 (28.28) | 241 (23.39) | | 143 (28.20) | 327 (24.55) | |
| Smoking, n (%) | | | | 0.727 | | | 0.533 |
| Yes | 968 (51.89) | 428 (52.53) | 540 (51.61) | | 260 (50.83) | 708 (52.46) | |
| No | 890 (48.11) | 378 (47.47) | 512 (48.39) | | 249 (49.17) | 641 (47.54) | |
| Physical activity, MET·min, Mean (S.E) | 1486.64 (111.58) | 1701.22 (209.16) | 1328.44 (139.84) | 0.159 | 1542.94 (166.27) | 1473.71 (139.16) | 0.734 |
| Diabetes mellitus, n (%) | | | | 0.832 | | | 0.981 |
| No | 1,414 (75.74) | 609 (75.75) | 805 (76.18) | | 387 (75.95) | 1,027 (76.00) | |
| Yes | 444 (24.26) | 197 (24.25) | 247 (23.82) | | 122 (24.05) | 322 (24.00) | |
| Hypertension, n (%) | | | | 0.432 | | | 0.625 |
| No | 452 (24.26) | 199 (25.14) | 254 (23.60) | | 126 (25.04) | 327 (23.99) | |
| Yes | 1,406 (75.74) | 607 (74.86) | 798 (76.40) | | 383 (74.96) | 1,022 (76.01) | |
| CVD, n (%) | | | | 0.146 | | | 0.444 |
| No | 442 (23.72) | 201 (25.27) | 242 (22.19) | | 126 (24.87) | 317 (23.05) | |
| Yes | 1,416 (76.28) | 605 (74.73) | 810 (77.81) | | 383 (75.13) | 1,032 (76.95) | |
| Loud noise exposure in past 24-h, n (%) | | | | <0.001 | | | <0.001 |
| Yes | 228 (12.15) | 81 (10.31) | 147 (13.97) | | 40 (7.94) | 188 (14.02) | |
| No | 1,630 (87.85) | 725 (89.69) | 905 (86.03) | | 469 (92.06) | 1,161 (85.98) | |
| Loud noise exposure at work, n (%) | | | | 0.052 | | | 0.004 |
| Yes | 651 (34.95) | 254 (31.74) | 400 (37.67) | | 139 (27.75) | 515 (37.82) | |
| No | 1,207 (65.05) | 552 (68.26) | 652 (62.33) | | 370 (72.25) | 834 (62.18) | |
| Hear protection, n (%) | | | | <0.001 | | | <0.001 |
| Most of the time | 344 (18.58) | 124 (15.42) | 184 (17.55) | | 75 (14.39) | 233 (17.45) | |
| Sometimes | 141 (7.55) | 58 (7.31) | 69 (6.30) | | 33 (6.58) | 94 (6.81) | |
| Rarely/seldom | 1,130 (60.44) | 400 (48.75) | 603 (57.48) | | 249 (48.49) | 754 (55.58) | |
| Never | 243 (13.42) | 224 (28.51) | 196 (18.67) | | 152 (30.55) | 268 (20.16) | |
| Calcium intake, mg, Mean (S.E) | 825.84 (10.20) | 846.84 (17.04) | 817.59 (11.87) | 0.152 | 840.98 (20.15) | 826.52 (10.38) | 0.477 |
| Magnesium intake, mg, Mean (S.E) | 268.69 (3.23) | 279.39 (4.77) | 261.62 (4.01) | 0.004 | 280.11 (6.45) | 265.43 (3.29) | 0.029 |
| Vitamin C intake, mg, Mean (S.E) | 85.46 (2.21) | 86.03 (2.98) | 86.35 (2.38) | 0.907 | 86.03 (3.84) | 86.28 (2.20) | 0.941 |
| Vitamin E intake, mg, Mean (S.E) | 7.88 (0.16) | 8.16 (0.26) | 7.63 (0.23) | 0.159 | 7.92 (0.29) | 7.84 (0.19) | 0.812 |
| Total energy, kcal, Mean (S.E) | 1742.34 (18.22) | 1789.74 (26.35) | 1712.71 (23.11) | 0.014 | 1761.09 (27.05) | 1741.20 (20.71) | 0.456 |
| Dietary supplements, n (%) | | | | 0.088 | | | 0.034 |
| Yes | 1,503 (80.11) | 463 (59.75) | 557 (54.87) | | 300 (61.46) | 720 (55.34) | |
| No | 355 (19.89) | 343 (40.25) | 495 (45.13) | | 209 (38.54) | 629 (44.66) | |
| Ototoxic medication use, n (%) | | | | 0.043 | | | 0.018 |
| No | 1,573 (84.35) | 696 (86.19) | 877 (82.91) | | 450 (88.28) | 1,123 (82.87) | |
| Yes | 285 (15.65) | 110 (13.81) | 175 (17.09) | | 59 (11.72) | 226 (17.13) | |
## Comparison between the HL group and the non-HL group
Comparing the differences between low-frequency HL and non-low-frequency HL groups, the results showed that the two groups were significant in age ($p \leq 0.001$), race/ethnicity ($p \leq 0.001$), marital status ($p \leq 0.001$), education level ($p \leq 0.001$), PIR ($$p \leq 0.047$$), loud noise exposure in the past 24 h ($p \leq 0.001$), hear protection when noise exposure ($p \leq 0.001$), total energy intake ($$p \leq 0.014$$), and ototoxic medication use ($$p \leq 0.043$$; Table 1). In the speech-frequency HL and non-speech-frequency HL groups, there were significant differences in age ($p \leq 0.001$), race/ethnicity ($p \leq 0.001$), sex ($p \leq 0.001$), marital status ($$p \leq 0.022$$), educational level ($p \leq 0.001$), loud noise exposure in the past 24 h ($p \leq 0.001$), loud noise exposure at work ($$p \leq 0.004$$), hear protection when noise exposure ($p \leq 0.001$), dietary supplements ($$p \leq 0.034$$), and ototoxic medication use ($$p \leq 0.018$$).
## Association between dietary ca and mg intake and HL
Table 2 reports the effects of dietary Ca and Mg intake and their combination (Ca/Mg and Ca * Mg) on HL. Dietary Ca intakes [OR = 0.86, $95\%$CI: (0.75–0.99)] and Mg intakes [OR = 0.77, $95\%$CI: (0.67–0.90)] and Ca * Mg [OR = 0.22, $95\%$CI: (0.05–0.86)] were associated with lower odds of low-frequency HL. After adjusting for age, sex, race/ethnicity, marital status, education level, PIR, loud noise exposure in the past 24 h, loud noise exposure at work, total energy intake, vitamin C intake, vitamin E intake, physical activity, ototoxic medication, and dietary supplements (model 3), dietary Ca intakes [OR = 0.86, $95\%$CI: (0.74–0.99)] and Mg intakes [OR = 0.81, $95\%$CI: (0.68–0.95)] and Ca * Mg [OR = 0.12, $95\%$CI: (0.02–0.87)] were still associated with lower odds of low-frequency HL. In the analysis of speech-frequency HL, dietary Ca intakes [OR = 0.85, $95\%$CI: (0.77–0.95)] and Mg intakes [OR = 0.78, $95\%$CI: (0.68–0.90)] and Ca * Mg [OR = 0.23, $95\%$CI: (0.05–0.78)] were related to lower odds of speech-frequency HL after adjusting for all confounders. Among participants with both low- and speech-frequency HL, dietary Ca intakes [OR = 0.85, $95\%$CI: (0.77–0.95)] and Mg intakes [OR = 0.78, $95\%$CI: (0.68–0.90)] and Ca * Mg [OR = 0.23, $95\%$CI: (0.05–0.78)] were also related to lower odds of low- and speech-frequency HL after adjusting for all confounders. In the sensitivity analysis (Supplementary Table S1), there was no significant difference in the results before and after data imputation.
**Table 2**
| Outcomes | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Outcomes | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P |
| Low-frequency HL | | | | | | |
| Ca | 0.86 (0.75–0.99) | 0.032 | 0.85 (0.74–0.97) | 0.020 | 0.86 (0.74–0.99) | 0.035 |
| Mg | 0.77 (0.67–0.90) | <0.001 | 0.80 (0.69–0.94) | 0.008 | 0.81 (0.68–0.95) | 0.011 |
| Ca/Mg | 1.09 (0.93–1.28) | 0.258 | 1.03 (0.89–1.19) | 0.710 | 1.02 (0.88–1.18) | 0.809 |
| Ca * Mg | 0.22 (0.05–0.86) | 0.035 | 0.25 (0.04–0.93) | 0.044 | 0.12 (0.02–0.87) | 0.041 |
| Speech-frequency HL | | | | | | |
| Ca | 0.89 (0.77–1.03) | 0.120 | 0.91 (0.84–0.99) | 0.032 | 0.85 (0.77–0.95) | 0.004 |
| Mg | 0.81 (0.69–0.94) | 0.007 | 0.85 (0.77–0.95) | 0.003 | 0.78 (0.68–0.90) | 0.001 |
| Ca/Mg | 1.14 (0.99–1.32) | 0.073 | 1.06 (0.95–1.19) | 0.265 | 1.04 (0.93–1.16) | 0.470 |
| Ca * Mg | 0.11 (0.02–0.76) | 0.030 | 0.11 (0.03–0.75) | 0.036 | 0.23 (0.05–0.78) | 0.041 |
| Low- and speech-frequency HL | | | | | | |
| Ca | 0.89 (0.81–0.97) | 0.010 | 0.83 (0.73–0.95) | 0.006 | 0.83 (0.72–0.95) | 0.010 |
| Mg | 0.82 (0.75–0.91) | <0.001 | 0.82 (0.72–0.95) | 0.008 | 0.83 (0.76–0.91) | <0.001 |
| Ca/Mg | 1.06 (0.97–1.17) | 0.206 | 1.00 (0.85–1.19) | 0.973 | 0.96 (0.88–1.05) | 0.358 |
| Ca * Mg | 0.20 (0.05–0.89) | 0.040 | 0.26 (0.06–0.95) | 0.048 | 0.19 (0.04–0.79) | 0.027 |
## Association between different levels of ca and mg intake and HL
Compared with Ca intake <545 mg, only participants with Ca intake ≥1,044 mg [OR = 0.61, $95\%$CI: (0.46–0.81)] were related to lower odds of low-frequency HL after adjusting for all confounders. Compared with Mg intake <190 mg, only participants with Mg intake ≥330 mg [OR = 0.67, $95\%$CI: (0.46–0.99)] were related to lower odds of low-frequency HL after adjusting for all confounders (Supplement Table S2). Similar, only participants with Ca intake ≥1,044 mg [OR = 0.59, $95\%$CI: (0.38–0.90)] or Mg intake ≥330 mg [OR = 0.64, $95\%$CI: (0.41–0.99)] were associated with lower odds of speech-frequency HL after adjusting for all confounders. Table 3 shows the effect of combined intake of different levels of Ca and Mg on HL. Combined intake of Ca (545-756 mg) and Mg (≥330 mg) [OR = 0.14, $95\%$CI: (0.02–0.74)] or Ca (≥1,044 mg) and Mg (≥330 mg) [OR = 0.02, $95\%$CI: (0.00–0.27)] was related to lower odds of low-frequency HL. Combined intake of Ca (545-756 mg) and Mg (≥330 mg) [OR = 0.34, $95\%$CI: (0.12–1.00)] or Ca (≥1,044 mg) and Mg (252-330 mg) [OR = 0.37, $95\%$CI: (0.17–0.84)] or Ca (≥1,044 mg) and Mg (≥330 mg) [OR = 0.44, $95\%$CI: (0.21–0.89)] was associated with lower odds of speech-frequency HL. Similar results were observed in participants with both low- and speech-frequency HL.
**Table 3**
| Outcomes | Levels | Model 2 | Model 2.1 | Model 3 | Model 3.1 |
| --- | --- | --- | --- | --- | --- |
| Outcomes | Levels | OR (95%CI) | P | OR (95%CI) | P |
| Low-frequency HL | Ca (<545 mg) and Mg (<190 mg) | Ref | | Ref | |
| Low-frequency HL | Ca (545-756 mg) and Mg (190-252 mg) | 1.37 (0.89–2.10) | 0.149 | 0.66 (0.24–1.77) | 0.405 |
| Low-frequency HL | Ca (545-756 mg) and Mg (252-330 mg) | 0.92 (0.47–1.81) | 0.815 | 1.23 (0.37–4.13) | 0.734 |
| Low-frequency HL | Ca (545-756 mg) and Mg (≥330 mg) | 0.47 (0.20–1.10) | 0.082 | 0.14 (0.02–0.74) | 0.021 |
| Low-frequency HL | Ca (756-1,044 mg) and Mg (190-252 mg) | 0.92 (0.54–1.58) | 0.772 | 1.44 (0.48–4.30) | 0.510 |
| Low-frequency HL | Ca (756-1,044 mg) and Mg (252-330 mg) | 0.76 (0.48–1.21) | 0.245 | 3.37 (0.99–11.47) | 0.052 |
| Low-frequency HL | Ca (756-1,044 mg) and Mg (≥330 mg) | 0.69 (0.41–1.15) | 0.157 | 0.61 (0.11–3.33) | 0.571 |
| Low-frequency HL | Ca (≥1,044 mg) and Mg (190-252 mg) | 1.51 (0.54–4.17) | 0.432 | 0.07 (0.00–1.36) | 0.080 |
| Low-frequency HL | Ca (≥1,044 mg) and Mg (252-330 mg) | 0.59 (0.35–0.99) | 0.049 | 0.08 (0.00–1.44) | 0.087 |
| Low-frequency HL | Ca (≥1,044 mg) and Mg (≥330 mg) | 0.61 (0.40–0.93) | 0.020 | 0.02 (0.00–0.27) | 0.004 |
| Speech-frequency HL | Ca (<545 mg) and Mg (<190 mg) | Ref | | Ref | |
| Speech-frequency HL | Ca (545-756 mg) and Mg (190-252 mg) | 1.52 (0.68–3.40) | 0.303 | 1.31 (0.58–2.94) | 0.515 |
| Speech-frequency HL | Ca (545-756 mg) and Mg (252-330 mg) | 0.71 (0.28–1.79) | 0.466 | 0.67 (0.25–1.81) | 0.429 |
| Speech-frequency HL | Ca (545-756 mg) and Mg (≥330 mg) | 0.36 (0.13–0.98) | 0.045 | 0.34 (0.12–1.00) | 0.050 |
| Speech-frequency HL | Ca (756-1,044 mg) and Mg (190-252 mg) | 0.58 (0.27–1.23) | 0.156 | 0.62 (0.27–1.43) | 0.262 |
| Speech-frequency HL | Ca (756-1,044 mg) and Mg (252-330 mg) | 1.15 (0.52–2.55) | 0.739 | 1.10 (0.50–2.43) | 0.809 |
| Speech-frequency HL | Ca (756-1,044 mg) and Mg (≥330 mg) | 0.58 (0.22–1.54) | 0.285 | 0.52 (0.16–1.66) | 0.270 |
| Speech-frequency HL | Ca (≥1,044 mg) and Mg (190-252 mg) | 1.47 (0.40–5.34) | 0.561 | 1.37 (0.38–4.90) | 0.601 |
| Speech-frequency HL | Ca (≥1,044 mg) and Mg (252-330 mg) | 0.41 (0.19–0.88) | 0.023 | 0.37 (0.17–0.84) | 0.018 |
| Speech-frequency HL | Ca (≥1,044 mg) and Mg (≥330 mg) | 0.47 (0.28–0.80) | 0.005 | 0.44 (0.21–0.89) | 0.023 |
| Low- and speech-frequency HL | Ca (<545 mg) and Mg (<190 mg) | Ref | | Ref | |
| Low- and speech-frequency HL | Ca (545-756 mg) and Mg (190-252 mg) | 0.68 (0.27–1.74) | 0.425 | 0.59 (0.23–1.51) | 0.272 |
| Low- and speech-frequency HL | Ca (545-756 mg) and Mg (252-330 mg) | 1.33 (0.42–4.19) | 0.623 | 1.21 (0.37–3.95) | 0.753 |
| Low- and speech-frequency HL | Ca (545-756 mg) and Mg (≥330 mg) | 0.18 (0.03–1.02) | 0.053 | 0.16 (0.03–0.87) | 0.034 |
| Low- and speech-frequency HL | Ca (756-1,044 mg) and Mg (190-252 mg) | 1.64 (0.55–4.85) | 0.374 | 1.57 (0.53–4.69) | 0.418 |
| Low- and speech-frequency HL | Ca (756-1,044 mg) and Mg (252-330 mg) | 3.88 (1.25–12.11) | 0.019 | 3.63 (1.09–12.05) | 0.035 |
| Low- and speech-frequency HL | Ca (756-1,044 mg) and Mg (≥330 mg) | 0.92 (0.17–4.92) | 0.926 | 0.72 (0.13–3.93) | 0.707 |
| Low- and speech-frequency HL | Ca (≥1,044 mg) and Mg (190-252 mg) | 0.05 (0.00–0.92) | 0.438 | 0.06 (0.00–0.92) | 0.044 |
| Low- and speech-frequency HL | Ca (≥1,044 mg) and Mg (252-330 mg) | 0.08 (0.01–1.24) | 0.070 | 0.08 (0.01–1.22) | 0.069 |
| Low- and speech-frequency HL | Ca (≥1,044 mg) and Mg (≥330 mg) | 0.02 (0.00–0.31) | 0.005 | 0.02 (0.00–0.26) | 0.003 |
## Discussion
HL is one of the chronic non-communicable diseases that can affect people’s quality of life. HL not only has a negative impact on daily life and mental health, but its prevention and treatment costs also bring pressure to social and economic development. The present study found that dietary Mg and Ca intake and their combined intake were inversely associated with the odds of low- and speech-frequency HL. Among different levels of Mg and Ca intake, Mg levels ≥330 mg combined with Ca levels ≥1,044 mg were related to lower odds of low- and speech-frequency HL.
HL at different frequencies can be divided into low-frequency, speech-frequency, and high-frequency HL [29]. High-frequency audiometry is mainly used for early detection of hearing loss. Age-related HL experienced by older adults usually starts with high-frequency HL and gradually affects the middle and low frequencies and audiometry in the 2–4 kHz range is important for the assessment of speech comprehension [9]. Therefore, the current study focused on low- and speech-frequency HL in older adults aged ≥70 years. Our results showed that the prevalence of high-frequency hearing loss in participants aged ≥70 years was $95.07\%$, which was why we chose low- and speech-frequency HL as the outcome of our study. Many studies have reported the association between dietary Mg intake and HL. The cross-sectional study of Choi et al. showed that dietary Mg intake was associated with lower risks of HL [13], which was consistent with our results. Spankovich et al. suggested that higher Mg intake was significantly associated with better pure tone thresholds in the populations of Sydney Australia [30]. Several studies have pointed out that oxidative stress was related to HL [14]. The relationship between antioxidants and HL has been confirmed in many animal studies, the formation of free radicals in the inner ear is a key factor in HL, and Mg can reduce the vasoconstriction caused by the formation of free radicals, thereby protecting hearing [14, 15]. These studies provide a scientific basis to support our epidemiological findings. Weyh et al. reported the role of minerals such as Mg in immune system function and regulation of inflammation [31]. Therefore, dietary Mg intake is a promising intervention for elderly patients with HL and further studies are needed.
Currently, few studies were studied on the association between dietary Ca intakes and HL. The present study found an inversely associations between dietary Ca intake and low- and speech-frequency HL. Moreover, the product of dietary Ca and dietary Mg was found to be associated with lower odds of low-frequency and speech-frequency HL after adjusting for all confounders (including vitamin C, vitamin E, physical activity). To the best of our knowledge, this study was the first to analyze the association between combined intake of Ca and Mg and HL. The present study may provide epidemiological evidence for the relationship between Ca and Mg intake and HL in older adults. Dietary Ca and Mg intake is a promising intervention to be further explored in older adults with HL. However, the specific mechanism by which Mg and Ca affect HL is still unknown, and the mechanisms that can be speculated are as follows. Mg is a Ca antagonist, and Mg antagonizes Ca when absorbed in the small intestine, and chronic low levels of Ca may be related to underlying Mg deficiency [32]. Mg could block the excessive release of Ca in hair cells and the cochlear vasculature, limits cellular energy consumption and induces arteriole vasodilation [33]. Through this mechanism, Mg could inhibit ischemia caused by hearing damage [33]. Hypomagnesemia disrupted Ca homeostasis, thereby enhancing the pro-inflammatory effects of Mg deficiency [34]. Inflammatory effects are generally associated with free radicals and thus have an effect on HL [14, 35].
Several limitations of this study should be considered. First, we only used three surveys of 2005–2006, 2009–2010, and 2017–2018, which may lead to small sample size and reduced statistical power due to the different ages of participants who participated in the audiometry examination in different years in the NHANES database. Second, the cross-sectional study design of this study could not establish a causal relationship between dietary Ca and Mg intake and HL. Third, the dietary data was from a dietary recall survey, and there may be participants with cognitive impairments among the surveyed elderly, which may cause a certain recall bias. Prospective large-scale studies are needed to further explore the relationship between dietary Ca and Ma and HL.
## Conclusion
This study found that dietary Mg and Ca intake and their combination were inversely associated with low- and speech-frequency HL. Identification of modifiable factors affecting elderly patients with HL plays an important role in patient management and prevention. Dietary Mg and Ca intake is a promising intervention to be further explored in older adults with HL.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here NHANES, https://wwwn.cdc.gov/nchs/nhanes/.
## Ethics statement
Ethical approval was not provided for this study on human participants because NHANES is a publicly available data set. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XW designed the study, wrote the manuscript, collected, analyzed and interpreted the data, and critically reviewed, edited and approved the manuscript.
## Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1101764/full#supplementary-material
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|
---
title: Interaction between the intestinal flora and the severity of diversion colitis
after low anterior resection of rectal cancer
authors:
- Qiang Sun
- Yunjie Shi
- Xiaoben Liang
- Hao Lu
- Yu Huang
- Lin Zhu
- Wenqiang Wang
- Wei Zhang
- Zhiqian Hu
- Xinxing Li
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10043175
doi: 10.3389/fonc.2023.1001819
license: CC BY 4.0
---
# Interaction between the intestinal flora and the severity of diversion colitis after low anterior resection of rectal cancer
## Abstract
### Background
Diversion colitis (DC) is nonspecific inflammation of the distal intestinal mucosa following disruption of colonic continuity with colonic dysfunction. The colonscopic score is a good tool for differentiating the severity of patients with DC. At present, no studies have analyzed the pathogenesis of DC from the perspective of the diversity and and differences of intestinal flora.
### Methods
Retrospective study: Clinical information were collected from patients with low rectal cancer admitted to the Department of Anorectal Surgery, Changzheng Hospital, from April 2017 to April 2019. These patients underwent laparoscopic low anterior resection (LAR) combined with terminal ileum enterostomy (dual-chamber). We used chi-square test to comparethe clinical baseline information, clinical symptoms, and colonscopic characteristics between different severity of DC. Propsective oberservational study: We recruited 40 patients with laparoscopic anterior low resection combined with terminal ileum enterostomy and they were further classified into mild group and severe group according to the scores of colonscopic examinations for DC. 16s-rDNA sequencing was carried out to analyze the diversity and and differences of intestinal flora in the intestinal lavage fluid of the two groups.
### Results
In retrospective study, we found that age, BMI, history of diabetes, and symptoms associated with the stoma state were the independent risk factors that affect DC severity ($P \leq 0.05$). Meanwhile, age, BMI, history of diabetes and colonscopic score were found to be independent risk factors affecting the severity of diarrhea after ileostomy closure surgery($P \leq 0.05$), which was consistent with our results of differentiating the severity of DC under endoscopy; In propsective oberservational study, 40 patients with low rectal cancer recruited by sample size calculation, 23 were in the mild group and 17 in the severe group. The results of 16s-rDNA sequencing showed that intestinal flora with high enrichment values primarily consisted of Bifidobacteriales and Prevotella in mild group, whereas that in the severe group consisted of Providencia and Dorea. The functional predictions on such two types of intestinal flora were mainly focused on lipid synthesis, glycan synthesis, metabolism, and amino acid metabolism pathways.
### Conclusion
After ileostomy closure surgery, a series of severe clinical symptoms might appear in DC patients. There are significant differences in local and systemic inflammatory responses, composition of intestinal flora between DC patients with different colonscopic scores, which provide a basis for the clinical interventional treatment for DC in patients with permanent stoma.
## Introduction
According to 2020 cancer burden data, colorectal cancer is the third most common cancer and the second leading cause of cancer death worldwide, of which rectal cancer accounts for one-third [1]. Radical excision remains the major clinical treatment for rectal cancer at present, and low anterior resection (LAR) has been increasingly widely applied in patients with low rectal carcinoma [2]. Protective ileostomy is always applied to avoid clinically significant anastomotic leakage and other postoperative complications for patients receiving LAR surgery [3]. Although ileostomy plays a protective role in anastomotic stoma, it may artificially lead to an abnormal diversion of digestive tract contents and cause diversion colitis (DC). In specific, DC is a newly proposed non-specific inflammation in the intestinal mucosa with colonic dysfunction [4]. DC presents with erythema, diffuse granularity and indistinct vascular patterns under electronic colonoscop [5]. It is also associated, to varying degrees, with mucosal fragility ($80\%$), edema ($60\%$), aphthous ulcers, and bleeding. With the time prolongation of stoma state, the condition of DC may become increasingly severe. The pathological features of DC include lymphoid follicular hyperplasia, intestinal mucosal atrophy, muscularis mucosal hypertrophy, Paneth cell metaplasia, diffuse active mucosal inflammation with crypt abscess [6]. The pathogenesis of DC in patients with enterostomy status is unclear yet, although it may be related to intestinal bacteria disorder, insufficient short-chain fatty acids, and immune-inflammatory factors. In our clinical work, we found that the colonoscopic manifestations under the stoma were consistent with the diarrhea after ileostomy closure surgery. We were committed to verifying the accuracy of colonoscopic scores and explaining the differences in the severity of DC from the perspective of intestinal flora.
In this study, the clinical characteristics of patients with enterostomy status-related DC were observed and analyzed. We explored the correlation between intestinal flora imbalance and DC developmentand and elucidated the possible mechanisms. This study consists of two major parts: [1] Retrospectivly analyzed the clinical characteristics of patients with DC and verified the accuracy of colonoscopic scores; [2] Prospectively analyzed the relationship between intestinal flora and DC after ileum enterostomy. We are looking forward to the clinical interventional treatment of enterostomy-related DC from the perspective of regulating intestinal flora, with the focus on alleviating the clinical symptoms of such patients.
## Sample sources and methods
This study enrolled 305 patients with rectal cancer admitted in our center from April 2017 to April 2019. They all underwent laparoscopic LAR for rectal cancer. Among them, 167 patients were combined with concurrent terminal ileum enterostomy (dual-chamber) to prevent anastomotic leakage. Ethical review number was as follows: ChiECRCT-20180225.
Inclusion criteria: ①Diagnosed with rectal cancer through enteroscopy and pathology; ② Clinical stage III or below; ③ Complete medical data and receiving follow-up visits for at least 24 weeks.
Exclusion criteria: ① Combined with other infectious enteritis, autoimmune enteritis, radiation enteritis, or inflammatory bowel diseases; ② Follow-up was inconclusive or nonsurvivable; ③ Taking hormones, antibiotics, or immunosuppressors ≥1month in the course of the disease; ④ Postoperative C grade anastomotic leakage or infectious peritoneal effusion; ⑤ Other gastrointestinal tumors or surgical history; ⑥ Neoadjuvant or postoperative radiotherapy; ⑦ Vegetarians.
In this study, we performed a three-step retrospective comparative analysis of the risk factors of different severity of DC and diarrhea after ileostomy closure surgery. Step 1: According to colonscopic score (Table S1), 110 patients met the requirements of the study were divided into the mild group (52 cases) and the severe group (58 cases). The follow-up time was 3-6 months. *The* general data differences between the two groups were compared and analyzed. Step 2: Among 110 patients, 85 patients underwent ileostomy closure surgery. 25 patients were excluded because they could not be operated again due to age, underlying diseases and other reasons and became permanent stoma condition. 45 patients remained in the mild group and 40 patients remained in the severe group. *The* general data differences between the two groups after ileostomy closure surgery were compared and analyzed. Step 3: After ileostomy closure surgery, the most prominent clinical symptom is diarrhea. Then we wanted to examine differences in general data for patients with different degrees of diarrhea. We used the 20-point scoring method to distinguish the severity of diarrhea in 85 patients after ileostomy closure surgery within 6 month, and finally divided them into 41 cases of mild diarrhea group and 44 cases of severe diarrhea group. The differences of general data between the two groups were compared and analyzed.
We found that colonscopic score was an independent risk factor for diarrhea severity after ileostomy closure surgery. Therefore, in prospective studies, we continued to use colonscopic score to distinguish the severity of DC patients. According to the requirements of the case-control study, patients meeting the requirements of the study were prospectively selected in our center from August 2019 to August 2020. All patients underwent laparoscopic LAR combined with terminal ileum enterostomy and returned to our hospital 3 months after surgery. Colonoscopies were performed by the same investigator to assess the severity of DC. Mechanical bowel preparation was not performed before colonoscopy in order to reduce bias. The Olympus colonoscopy system was used to conduct full-layer examination of putting-aside colon and take images through anus. In this study, all patients presented with varying degrees of DC, and the colonscopic scoring requirements and methods were the same as before. *The* general data differences between the two groups were compared and analyzed. This study has been approved by the Ethics Committee of Changzheng Hospital, the Ethical review number was as follows:ChiECRCT-20190233, and the informed consent of patients was obtained.
Based on relevant literature and clinical practice, this study intended to include about 8 variables for data analysis. In multi-factor non-conditional logistic regression analysis, the sample size of dependent variables with a low incidence was required to be at least 5-10 times that of the included variables(5 times in this study). In addition, according to previous research results, the incidence of DC was $100\%$, so the total sample size of this study was 8×5÷$100\%$=40 cases.
## Relevant diagnostic criteria
Colonscopic scores are detailed as follows. Colonoscopic findings include ulcer, follicular hyperplasia, anastomotic stenosis, inflammatory polyp, and mucosa edema [7]. The colonoscopic rating [8, 9] consists of three elements of edema (0–3 points), mucosal hemorrhage (0–3 points), and contact hemorrhage (0–1 point). The total score was obtained by adding the points described above. On the basis of the total score, mild (0–3 points) or severe (4–7 points) symptoms were distinguished (Table S1 and Figure S1). On the basis of their colonscopic scores, the patients with DC were divided into a group of mild symptoms (the mild group) and a group of severe symptoms (the severe group).
For all patients, their clinical symptoms (e.g., abdominal pain, mucoid stool, and haematochezia) subsequent to LAR as well as those (e.g., tenesmus, abdominal pain, anal pain, diarrhea frequency, time for defecation return to normal, mucous stools and haematochezia) within 6 months after ileostomy closure surgeries were collected and conducted statistical analysis.
Diarrhea severity rating is described as follows. After ileostomy closure surgery, the most prominent clinical symptom is diarrhea [10]. This study selected 20-point scoring [11] to distinguish the severity of diarrhea after the ileostomy closure surgery, covering duration and frequency of diarrhea, vomiting duration, fever and dehydration state (estimated by a ratio of weight loss to the total weight). A higher score indicates more severe diarrhea, and more than 10 points suggests mild diarrhea, whereas that above 10 points indicates severe diarrhea (Table S2).
The severity rating system of the International Rectal Cancer Study Group classifies anastomotic leakage after rectal surgery into three grades: A, B and C. Crade A is relatively light, without special treatment; Grade B requires intervention but does not require open surgery; Grade C requirs reoperation, which is a relatively serious complication. Reoperation has a great impact on the intestinal environment. So the patients with grade C anastomotic leakage were excluded in this study.
## Calprotectin test in anal lavage fluid
Anal lavage fluid samples were selected and sampled prior to endoscopy to ensure that the results were not affected by endoscopy. Six hours before colonoscopy, 250ml normal saline was retained from the anus by infusion strip for enema, and 50-100ml of enema was retained and stored in the refrigerator at -80°C. The content of calprotectin in the anal lavage fluid of the two groups was standardized according to the requirements of the kit instruction (Shanghai Zeye Biotechnology Co., LTD.).
## Inflammatory factor level test
Under colonoscopy, biopsy forceps were used to take 3-4 pieces of mucosal tissue with the most severe inflammation as tissue samples. 10ml of fasting venous blood was extracted from the patients in the morning, heparin was treated with anticoagulation, and centrifuged at 5000r/min at 4°C for 5min. The serum was separated and frozen at -80°C for testing. After collection, sterile diluent was added for 10-fold dilution, and the tested solution was cultured in selective lactobacillus culture medium (LBS) at 37°C and anaerobic conditions for 72h. After the culture, the contents of TNF-α, IL-1β, IL-6 and IL-17 in tissues and plasma were detected by enzyma-linked immunosorbent assay (ELISA), and the operation was standardized according to the kit instruction (Shanghai Qiyi Biotechnology Co., LTD.).
## Lipopolysaccharide test
The plasma sampling method was the same as above. The content of LPS in plasma was detected by ELISA, and the standard operation was carried out according to the kit instruction (Shanghai Xinyu Biotechnology Co., LTD.).
## 16S rRNA sequencing and analysis for anal lavage fluid
Anal lavage fluid method see above. Samples are transported to the laboratory as soon as possible and all operations are carried out in the anaerobic chamber (atmospheric environment:Nitrogen ($75\%$), hydrogen ($10\%$) and carbon dioxide ($15\%$) were inoculated on two blood AGAR plates (BAP) for 24h at 35°C under anaerobic conditions in a $5\%$ CO2 incubator. Brucella culture medium was incubated in an anaerobic tank for 48h, and the species of cultured bacteria were identified qualitatively. Based on BGI, 16S rRNA sequencing of anal lavage fluid was performed. The main steps were as follows:DNA extraction, PCR amplification and library construction. HiSeq platform was selected for sequencing of qualified libraries according to the inserted fragment size.
Next, bioinformatics analysis, such as extraction level analysis, Operational Taxonomic Unit (OTU) clustering analysis, species diversity analysis, species composition analysis and species difference analysis, was carried out. Parallel function prediction:PICRUSt software was used to standardize OTU abundance table first, that is, to remove the influence of copy number of 16S marker gene in species genome. Then, KO corresponding to OTU was obtained by greengene ID corresponding to each OTU, and the abundance of KO was calculated by the sum of OTU abundance corresponding to KO.
## Statistical methods
SPSS (26.0, IBM) statistical software was selected to complete data entry and statistical analysis. General data of patients in both groups were descriptively analyzed. Quantitative data were denoted by (x¯±s), and inter-group comparison was conducted by t test for 2 independent samples. Relevant enumeration data were expressed in percentage (%), and χ2 test was carried out for comparison between the two groups. The severity of DC and general clinical data were analyzed by univariate analysis. The significant factors in univariate analysis were used as independent variables, and the severe DC was used as dependent variable for Logistic multiple analysis by stepwise method. Odds ratio (OR) was used to represent the strength of association between the two factors (OR > 1 was a risk factor, OR< 1 was a protective factor). The results of intestinal flora in both groups were subjected to Wilcoxon rank-sum tests on the HiSeq platform. Statistical significance was considered at $P \leq 0.05.$
## The result of retrospective study
In step 1, there were statistically significant differences in age, BMI, diabetes and incidence of DC-related clinical symptoms (including abdominal pain, mucous stools and haematochezia) between two groups ($P \leq 0.05$). The incidence of abnormal colonoscopic manifestations (including ulcers, follicular hyperplasia, inflammatory polyps, mucosal edema, anastomotic stenosis, etc.) in the severe group was significantly higher than that in the mild group ($P \leq 0.05$), which was consistent with the results of colonscopic score grouping. Age, BMI, diabetes, symptoms of stoma state were independent risk factors influence the severity of the DC (all $P \leq 0.05$) (Table 1).
**Table 1**
| Parameter | Characteristic | Mild group (n=52) | Severe group (n=58) | Univariate analysis | Univariate analysis.1 | Multiple analysis | Multiple analysis.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Parameter | Characteristic | Mild group (n=52) | Severe group (n=58) | Log rank χ2 test | P | OR (95%CI) | P |
| Gender | | | | 2.314 | 0.468 | – | NI |
| | Male | 32 (61.54) | 39 (67.24) | | | | |
| | Female | 20 (38.46) | 19 (32.76) | | | | |
| Age (years) | | | | 9.454 | 0.009 | | <0.0001 |
| | ≤60 | 35 (67.31) | 28 (48.28) | | | Reference | |
| | >60 | 17 (32.69) | 30 (51.72) | | | 0.708 (0.658~0.762) | |
| BMI (kg/m2) | | | | 5.356 | 0.019 | | <0.0001 |
| | <25 | 33 (63.46) | 30 (51.72) | | | Reference | |
| | ≥25 | 19 (36.54) | 28 (48.28) | | | 0.771 (0.644~0.923) | |
| Diabetes | | 5 (23.63) | 20 (34.48) | 13.234 | 0.004 | 0.895 (0.708~1.132) | <0.0001 |
| Pathological stage | | | | 1.564 | 0.472 | – | NI |
| | I~II | 34 (65.38) | 39 (67.24) | | | | |
| | III | 18 (34.62) | 19 (32.76) | | | | |
| Postoperative chemotherapy | | | | 1.378 | 0.482 | – | NI |
| | Yes | 16 (30.77) | 16 (27.57) | | | | |
| | | 36 (69.23) | 42 (72.41) | | | | |
| Symptom of stoma state | | | | 8.420 | 0.010 | | <0.0001 |
| | Abdominal pain | 6 (11.54) | 18 (31.03) | | | Reference | |
| | Mucoid stool | 3 (5.77) | 12 (20.69) | | | 1.029 (0.800~1.323) | |
| | Haematochezia | 1 (1.92) | 6 (10.34) | | | 1.288 (1.041~1.594) | |
| Colonoscopic findings | | | | 37.379 | <0.0001 | | <0.0001 |
| | Ulcer | 5 (9.62) | 17 (29.31) | | | Reference | |
| | Follicular hyperplasia | 10 (19.23) | 29 (50.00) | | | 0.864 (0.787–0.948) | |
| | Inflammatory polyp | 10 (19.23) | 28 (48.28) | | | 0.942 (0.832–1.065) | |
| | Mucosal edema | 42 (80.77) | 58 (100.00) | | | 1.087 (0.928–1.208) | |
| | Anastomotic stenosis | 1 (1.02) | 6 (15.52) | | | 1.180 (0.946–1.301) | |
In step 2, the incidence of postoperative clinical symptoms (including abdominal pain, haematochezia, tenesmus, anal pain, etc.) in severe group after ileostomy closure surgery was significantly higher than those in mild group ($P \leq 0.05$). CRP, diarrhea frequency per day and time for defecation return to normal in severe group were significantly higher than those in mild group ($P \leq 0.05$) (Table 2).
**Table 2**
| Indicators | Characteristic | Mild group (n=45) | Severe group (n=40) | P |
| --- | --- | --- | --- | --- |
| Clinical signs and symptoms | Clinical signs and symptoms | Clinical signs and symptoms | Clinical signs and symptoms | Clinical signs and symptoms |
| | Abdominal pain | 25 (55.56) | 35 (87.50) | 0.001 |
| | Haematochezia | 10 (22.22) | 18 (45.00) | 0.022 |
| | Tenesmus | 8 (17.78) | 20 (50.00) | 0.013 |
| | Anal pain | 4 (8.89) | 13 (32.50) | 0.005 |
| Diarrhea frequency per day | | 4.14±1.69 | 11.21±4.27 | 0.007 |
| Time for defecation return to normal (d) | | 15.67±4.78 | 30.92±9.09 | 0.003 |
| Complete blood count | Complete blood count | Complete blood count | Complete blood count | Complete blood count |
| | WBC (×109/L) | 6.44±3.42 | 7.08±1.34 | 0.188 |
| | NEUT% (%) | 68.43±6.32 | 70.4±5.13 | 0.101 |
| | CRP (mg/L) | 8.82±1.96 | 20.07±5.99 | 0.004 |
In step 3, the age and BMI of patients with severe diarrhea group were significantly higher than those with mild diarrhea group, and the incidence of diabetes, abdominal pain, mucus stool, haematochezia and other abnormal symptoms in patients with severe diarrhea were significantly higher than those with mild diarrhea ($P \leq 0.05$). Age, BMI, diabetes and colonscopic score were independent risk factors for diarrhea severity ($P \leq 0.05$) (Table 3).
**Table 3**
| Parameter | Characteristic | Mild diarrhea group (n=41) | Severe diarrhea group (n=44) | Univariate analysis | Univariate analysis.1 | Multiple analysis | Multiple analysis.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Parameter | Characteristic | Mild diarrhea group (n=41) | Severe diarrhea group (n=44) | Log rank χ2 test | P | OR (95%CI) | P |
| Gender | | | | 1.678 | 0.577 | – | NI |
| | Male | 26 (63.41) | 28 (63.64) | | | | |
| | Female | 15 (36.59) | 16 (36.36) | | | | |
| Age (years) | | | | 5.321 | 0.020 | | <0.0001 |
| | ≤60 | 16 (39.02) | 8 (18.18) | | | Reference | |
| | >60 | 25 (60.98) | 36 (81.82) | | | 0.802 (0.622–1.034) | |
| BMI (kg/m2) | | | | 35.435 | <0.0001 | | <0.0001 |
| | <25 | 25 (60.98) | 17 (38.64) | | | Reference | |
| | ≥25 | 16 (39.02) | 27 (61.36) | | | 1.224 (0.930–1.611) | |
| Diabetes | | 6 (14.63) | 18 (40.91) | 8.224 | 0.005 | 0.890 (0.626–1.263) | 0.005 |
| Symptom of stoma state | | | | 4.345 | 0.054 | – | NI |
| | Abdominal pain | 9 (21.95) | 14 (31.82) | | | | |
| | Mucoid stool | 6 (14.63) | 10 (22.73) | | | | |
| | Haematochezia | 5 (12.20) | 8 (18.18) | | | | |
| Postoperative chemotherapy | | | | 3.679 | 0.086 | – | NI |
| | Yes | 17 (41.46) | 13 (29.55) | | | | |
| | | 24 (58.54) | 31 (70.45) | | | | |
| colonscopic score | | | | 73.80 | <0.0001 | | <0.0001 |
| | Mild | 35 (85.37) | 10 (22.73) | | | Reference | |
| | Severe | 6 (14.63) | 34 (77.27) | | | 0.996 (0.840–1.182) | |
## Comparison of general data, calprotectin, inflammatory factors, and LPS between the two groups
There were no significant differences in baseline data such as gender, age, BMI, diabetes, pathological stage, chemotherapy regimens and duration of ostomy between two groups ($P \leq 0.05$). The patients were followed up after ileostomy closure surgery, and the severity of diarrhea in the two groups and the CRP results one month after ileostomy closure surgery were significantly different ($P \leq 0.05$)(Table 4). The content of calprotectin in anal lavage fluid in the severe group was significantly higher than that in the mild group, with significant difference between the two groups ($P \leq 0.05$)(Figure 1A), as shown in Figure 1B. The plasma LPS level of the severe group was significantly higher than that of the mild group, with significant difference between the two groups ($P \leq 0.05$)(Figure 1B). The results showed that the levels of TNF-α, IL-1β, IL-6 and IL-17 in the severe group were significantly higher than those in the mild group ($P \leq 0.05$)(Figure 2). The results showed that the levels of TNF-α, IL-1β, IL-6 and IL-17 in the severe group were significantly higher than those in the mild group ($P \leq 0.05$) (Figure 3).
## OTU clustering
The lowest sequence number in all samples was extracted to level, and the OTU number statistics of each sample were divided into $97\%$ sequence similarity level(Table S3). The sequence of original data after splicing and quality control was analyzed to figer out the overlapping of OTU clustering. The results showed that a total of 595 different OTUs were obtained, among which 415 otUs were shared by the two groups (Figure 4A).
**Figure 4:** *OTU cluster analysis and species diversity analysis of samples between the two groups. (A) OTU Venn diagram; (B) PLS-DA analysis; (C) Comparison of α-diversity.*
## Species diversity analysis for intestinal flora
PLS-DA analysis was performed on the OTU results of the two groups of samples, and the results showed that the distribution characteristics of the two groups were relatively concentrated within the group and relatively dispersed between the groups. There was no obvious overlap between the two groups, and there was not much overlap between the two groups, and the overall distribution was slightly dispersed. The two groups had good aggregation and differentiation respectively, indicating that there were significant differences in the bacterial community structure of the two groups (Figure 4B).
In this study, box plots were made for the six indices of the two groups of specimens respectively, which could intuitively evaluate the data distribution characteristics. The results showed that, on the whole, most of the median were distributed near the center of the box, indicating that the data distribution was relatively symmetrical, and the mean could be calculated for quantitative comparison. Observed species, Chao, Ace and Shannon indexes in the severe group were significantly lower than those in the mild group, while Simpson index was significantly higher than that in the mild group (all $P \leq 0.05$), suggesting that the intestinal microflora data in the severe group was more concentrated, but the diversity was significantly lower than that in the mild group. Good-coverage values of both groups were close to 1 (Figure 4C), suggesting that the results of this study were fairly representative of the actual situation.
## Species annotation analysis and composition differences of intestinal flora
[1] Species composition at phylum level:Bacteroidetes and Firmicutes were the dominant phyla with the highest overall abundance. Proteobacteria and Actinobacteria followed, and the total relative abundance of these four phyla exceeded $90\%$. Bacteroidetes and Firmicutes were the main components of intestinal flora. The Actinobacteria in the severe group was significantly lower than that in the mild group ($P \leq 0.05$), while the difference of Bacteroidetes and Firmicutes between the two groups was small ($P \leq 0.05$) (Figure 5A).
**Figure 5:** *Species annotation analysis and composition differences of samples between the two groups. (A) On phylum level; (B) On order level; (C) On family level; (D) Histogram of LDA value distribution.*
[2] Species composition at order level:Bacteroidales, Clostridiales and Enterobacteriales were the most abundant in the samples. Bifidobacteriales in the severe group were significantly lower than those in the mild group ($P \leq 0.05$) (Figure 5B).
[3] Species composition at genus level:Bacteroides, *Escherichia and* Porphyromonas were the dominant genera with the highest overall abundance. The Prevotella and *Escherichia levels* in the severe group were significantly lower than those in the mild group ($P \leq 0.05$) (Figure 5C).
[4] LefSe analysis of species composition differences:The histogram showed the results of LefSe analysis visually (all species shown in the figure were LDA score≥2, with statistical differences). Among them, Bifidobacteriales, Mollicutes, Atopobium, Prevotella and Actinobacteria were the most abundant in the mild group. In the severe group, Providencia and Dorea had higher abundance (Figure 5D).
## Function prediction
Function prediction of 16S-RDNA is to standardize OTU abundance table by PICRUSt, obtain KO information and abundance corresponding to OTU corresponding to Greengene ID, and predict genes for KEEG function classification prediction. There were significant differences between the two groups in genes enriched in glycan synthesis and metabolism, amino acid metabolism, immune system diseases, lipid synthesis and other metabolic pathways ($P \leq 0.05$) (Figure 6).
**Figure 6:** *Diagram of the difference in the Keeg Path Wilcox Test between the two groups.*
## Discussion
For patients with low rectal cancer (distance from inferior margin of the tumor to the anal edge≤5 cm), concurrent prophylactic terminal ileostomy is often considered during LAR surgery for rectal cancer. Terminal ileostomy could effectively protect rectal anastomosis from anastomotic leakage and reduce complications caused by poor healing of rectal anastomosis. However, studies in recent years has shown that DC is common in patients after terminal ileostomy. DC is also clinically known as disuse colitis [12]. DC was first mentioned by Basil Morson et al. [ 13] in 1974, when it was described as a segmental nonspecific inflammation confined to the colonic mucosa. David Glotzer et al. [ 14] published a case study of 10 patients in 1981, and defined this inflammation as DC. However, the exact mechanism of this disease has not yet been clarified.
Because enterostomy related-DC patient is in a stoma state, the typical clinical symptoms of DC are generally not obvious. For this reason, the actual incidence rate of DC might be underestimated. According to studies [15], the most common symptoms in DC patients are serous, bloody, or mucous stools ($40\%$), followed by abdominal pain and tenesmus ($15\%$). In this study, the incidence rate of abdominal pain, mucus stool, and bloody stool were lower than other studies. The possible reason is that our study was a retrospective study, and there might be a certain bias in the patient’s impression of previous symptoms. Although enterostomy closure surgery is the best method for the treatment of DC, the short-term clinical symptoms after restoration are usually obvious, which needs our attention. In addition, there are still a considerable number of patients who receive stomostomy, due to age, physical conditions and other reasons, cannot receive ileostomy closure surgery, forming permanent stomostomy, and the related symptoms will affect the quality of life, which is also the purpose of our next study. Our study not only focused on symptoms after ileostomy closure surgeries (e.g., abdominal pain, severe diarrhea, bloody stools, tenesmus, and anal pain), but also attached great importance to operation-related infections, enterocutaneous fistula, anastomotic leakage, anastomotic stenosis, and interstitial abscess. Nevertheless, no commonly used disease diagnosis standards are available globally [16]. In this study, the severity of DC was graded by colonscopic scores, and we found that age, BMI, history of diabetes, and enterostomy symptoms were independent risk factors for the severity of diarrhea, which provided data support for clinical prediction of the incidence of DC.
The pathogenesis of DC is not yet clear, which may be related to intestinal bacteria disorder, lack of SCFA and immune inflammatory factors [17]. Some studies have found that the lesions of the colonic mucosa might be caused by metabolites [18]. Metabolites in feces are an important source of energy for the body, and disruption of fecal flow may lead to colitis, but direct evidence is still few [19]. Glotzer et al. [ 12] speculated in a clinical report about 10 DC patients that DC might be the result of factors such as excessive proliferation of pathogenic bacteria, nutritional deficiencies, toxins, or disturbance of the symbiotic relationship between bacteria and the mucosal layer in gut. In an in vivo study [20], it was shown that the changes in gut microbiota were dominated by the increase of Bacteroides and Clostridium. It was speculated that bacteria were important environmental factors in the pathogenesis of DC, and the changes of gut microbiota may be involved in the pathogenesis of DC. One study showed [21, 22] decreased concentrations of carbohydrate-fermenting anaerobic and pathogenic bacteria in dysfunctional colon, which might suggest that the overproliferation of anaerobic or pathogenic bacteria is not important causative factors. Additional studies have shown that nitrate-reducing bacteria are increased in the gut of DC patients. Nitric oxide (NO) produced by nitrate-reducing bacteria has a dual effect on colon tissue, with a protective effect when NO concentration is low, but a toxic effect when the concentration is high [23]. Therefore, the increase of nitrate-reducing bacteria might increase the toxicity level of intestinal tissue, and then trigger DC [24]. In addition, current epidemiological studies [25] have not found specific pathogenic bacteria that cause DC, and the clinical effect of antibiotic therapy is poor. Therefore, DC may not be caused by direct infection by pathogenic bacteria, suggesting that changes in gut microbiota may only be part of environmental factors [26].
In this study, we used colonscopic scores to grade the severity of DC patients, and calprotectin, inflammatory factors, plasma lipopolysaccharide were tested to verify the severity of DC. Calprotectin was one kinds of calcium-binding protein whose expression levels were often abnormally increased during inflammatory responses. Therefore, calprotectin could be used as a marker of inflammatory cell activation and might be abnormally increased in a variety of enteritis [27]. In this study, the expression level of calprotectin in the intestinal enema fluid of the severe group was significantly higher than that of the mild group ($P \leq 0.05$), suggesting that calprotectin could be used as a detection method to evaluate whether DC was in the active phase. The chemical nature of LPS was endotoxin, the important component of the cell wall of Gram-negative (G-)bacteria. In the condition of severe infection, endogenous LPS in the intestine entered the blood through the intestinal wall, and excessive intestinal LPS might cause abnormal intestinal metabolism and immune response [28]. In our study, the plasma LPS level in the severe group was significantly higher than that in the mild group, suggesting that the intestinal wall permeability was increased and the intestinal barrier was damaged in patients with severe DC. The onset and progression of DC might be closely related to the inflammatory response. The abnormal expression of inflammatory factors such as TNF-α, IL-1β, IL-6 and IL-17 might affect the pathophysiological processes such as intestinal flora imbalance and immune dysfunction in DC patients [29]. In addition, inflammatory factors could invade the mucosal layer, damage the intestinal mucosal barrier, aggravate the damage of intestinal epithelial tissue, and lead to the recurrence of DC disease [30]. In this study, the expressions of inflammatory factors in the intestinal mucosal tissue and plasma of the severe group were significantly higher than those of the mild group. The possible reason was that the excessive immune response aggravated the intestinal mucosal damage in DC patients, which lead to the increase of the permeability of the intestinal mucosa and the decrease of the barrier function.
Our study was the first to evaluate the relationship between gut microbiota and DC using bacterial culture combined with 16S rDNA sequencing. Moreover, *In this* study, the traditional sampling method was improved, and the method of intestinal enema was selected, which had a lower chance of being exposed to oxygen during the operation, and the temperature and humidity environment was controllable, and had better accuracy. We found that comparing the two groups of flora at the phylum level, the *Actinomycete phylum* in the severe group was significantly lower than that in the mild group. However, there was no difference in Firmicutes and Bacteroidetes between the two groups. In the comparison of the two groups of bacteria at the order level, Bifidobacteriales in the severe group were significantly lower than that in the mild group. The comparison of the two groups of flora at the genus level showed that Prevotella and *Escherichia in* the severe group were significantly lower than that in the mild group. The results of LefSe analysis showed that the bacteria with higher abundance in the mild group were mainly Bifidobacteriales, Mollicutes, Atopobium, and Prevotella, Actinobacteria. The more abundant flora in the severe group were Providencia and Dorea. In addition, we compared and analyzed the 16S rDNA sequencing data of the intestinal flora of DC patients in two groups. The results showed that there were significant differences between the two groups in genes enriched in metabolic pathways such as glycan synthesis and metabolism, amino acid metabolism, immune system diseases and lipid synthesis. Our results suggested that the main reason for the occurrence of DC might be the decrease of beneficial bacteria or the increase of pathogenic bacteria.
Bifidobacterium, as representative gram-positive(G+) beneficial bacteria in Actinomyces, has functions including up-regulating systemic immune response, stimulating cellular immunity and preventing adverse infections, and is of great significance for participating in intestinal immunity and maintaining intestinal health [31]. Bifidobacterium on DC is not yet clear, the possible mechanisms lie in [32, 33]:1) Bifidobacterium can regulate the balance of intestinal flora, and by changing the pH value and substance metabolism of the intestinal environment, produce beneficial effects nutrients and antibacterial substances; 2) *Bifidobacterium is* beneficial to block the production of intestinal pathogenic bacteria (including Escherichia coli, Salmonella, etc.); 3) Bifidobacterium can be closely combined with intestinal epithelial cells through the adhesion effect to generate an effective biofilm barrier, which can effectively inhibit the invasion and transfer of intestinal pathogens;4) Bifidobacterium can secrete anti-inflammatory cytokines to regulate the process of immune system and inflammatory response. Bacteroidetes are the main components of the intestinal flora, and their presence plays a key role in the maintenance of intestinal health and material metabolism [34]. Prevotella is a common probiotic in the phylum Bacteroidetes, which is beneficial to regulate immune response and protect the structural integrity of intestinal mucosa. Russell TA [35] performed genomic analysis on the stool of DC patients, and the results showed that the abundance of Prevotella in the gut of patients with severe DC was reduced, similar to the results of our research. The possible reason is that *Prevotella is* an important component involved in the metabolism of various substances such as sugar and lipid, and lack of Prevotella affect the stability of substance metabolism. Dysregulation of Prevotella may lead to gastrointestinal motility disorders, and patients with DC may present with clinical symptoms such as abdominal pain, nausea, and vomiting. Providence can cause infection in multiple organs, mainly urinary tract and colon, which also can lead to an outbreak of nosocomial infections [36]. Dorella can affect the function of intestinal epithelial cells and increase the permeability of the intestinal epithelium, which leads to the entry of endotoxins into the blood and triggers chronic inflammation [37]. In addition, the study of Abegunde AT [38] showed that the composition of intestinal flora in DC patients was significantly different from that of negative controls; *Clostridium could* reduce the abundance of intestinal flora and have a greater correlation with the pathogenesis of inflammatory bowel disease. DC patients with symptoms had a higher proportion of Ruminococcus and *Clostridium in* gut microbiota. These results provide a basis for the clinical interventional treatment for enterostomy status-related DC in patients with permanent stoma.
There were still some problems to be improved in this study, including:[1] The sample size of this study was small, and the scope was limited to patients with rectal cancer who received terminal ileostomy;[2] we have found that specific flora is closely related to DC, but animal experiments are needed in the later stage to clarify the causal relationship and mechanism;[3] The DC grade used in this study was based on the colonscopic scores, and more precise grade needs to be combined with the pathological results of the intestinal inflammatory tissue;[4] In addition to bacterium, there are fungi and viruses in the gut, and we need to follow-up through metagenomic sequencing to clarify the mechanism.
## Conclusion
In a stoma state, DC patients show a few symptoms of the digestive tract but rather obvious colonscopic characteristics. Subsequent to colostomy closure surgeries, a series of serious clinical signs may appear. In addition to age, BMI, and diabetes influencing DC severity, colonscopic scores are also an independent risk factor related to diarrhea severity after the colostomy closure surgery. Moreover, DC patients with colonscopic scores at diverse grades are significantly different from each other in their local and systemic inflammatory responses, intestinal flora compositions, and diversity structures. In particular, obvious differences are found in the abundance of Bifidobacteriales, Prevotella, Providencia, and Dorea.
## Data availability statement
The data presented in the study are deposited in the GEO repository, accession number GSE226706.
## Ethics statement
The studies involving human participants were reviewed and approved by ChiECRCT-20180225. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
QS wrote the manuscript. YS and XiaL collected specimens. HL, LZ, WW and WZ sorted out relevant materials and literature. YH provided theoretical guidance. ZH and XinLi reviewed the paper. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1001819/full#supplementary-material
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|
---
title: The effect of transcutaneous electrical stimulation of the submental area on
the cardiorespiratory response in normal and awake subjects
authors:
- Abdulaziz Alsharifi
- Georgios Kaltsakas
- Martino F. Pengo
- Gianfranco Parati
- Miquel Serna-Pascual
- Gerrard Rafferty
- Joerg Steier
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10043176
doi: 10.3389/fphys.2023.1089837
license: CC BY 4.0
---
# The effect of transcutaneous electrical stimulation of the submental area on the cardiorespiratory response in normal and awake subjects
## Abstract
Background: Electrical stimulation has recently been introduced to treat patients with Obstructive sleep apnoea There are, however, few data on the effects of transcutaneous submental electrical stimulation (TES) on the cardiovascular system. We studied the effect of TES on cardiorespiratory variables in healthy volunteers during head-down-tilt (HDT) induced baroreceptor loading.
Method: Cardiorespiratory parameters (blood pressure, heart rate, respiratory rate, tidal volume, airflow/minute ventilation, oxygen saturation, and end-tidal CO2/O2 concentration) were recorded seated, supine, and during head-down-tilt [50] under normoxic, hypercapnic (FiCO2 $5\%$) and poikilocapnic hypoxic (FiO2 $12\%$) conditions. Blood pressure (BP) was measured non-invasively and continuously (Finapres). Gas conditions were applied in random order. All participants were studied twice on different days, once without and once with TES.
Results: We studied 13 healthy subjects (age 29 [12] years, six female, body mass index (BMI) 23.23 (1.6) kg·m−2). A three-way ANOVA indicated that BP decreased significantly with TES (systolic: $$p \leq 4.93$$E-06, diastolic: $$p \leq 3.48$$E-09, mean: $$p \leq 3.88$$E-08). Change in gas condition (systolic: $$p \leq 0.0402$$, diastolic: $$p \leq 0.0033$$, mean: $$p \leq 0.0034$$) and different postures (systolic: 8.49E-08, diastolic: $$p \leq 6.91$$E-04, mean: $$p \leq 5.47$$E-05) similarly impacted on BP control. When tested for interaction, there were no significant associations between the three different factors electrical stimulation, gas condition, or posture, except for an effect on minute ventilation (gas condition/posture $$p \leq 0.0369$$).
Conclusion: Transcutaneous electrical stimulation has a substantial impact on the blood pressure. Similarly, postural changes and variations in inspired gas impact on blood pressure control. Finally, there was an interaction between posture and inspired gases that affects minute ventilation. These observations have implications on our understanding of integrated cardiorespiratory control, and may prove beneficial for patients with SDB who are assessed for treatment with electrical stimulation.
## Introduction
Obstructive sleep apnoea (OSA) is a highly prevalent condition that affects about one billion people worldwide (Benjafield et al., 2019). In patients with OSA, intermittent and repeated upper airway collapse during sleep results in irregular breathing at night. Nocturnal apnoeas and hypopnoeas lead to an altered drive to breathe, high work of breathing, oxygen desaturations, and arousals from sleep (Hilton et al., 2001). These effects can cause daytime symptoms, such as sleepiness, and are associated with increased sympathetic tone activation and elevated blood pressure (Remmers et al., 1978). OSA is associated with co-morbidities, including hypertension (Marin et al., 2005; Parati et al., 2014), ischaemic heart disease (Martinez et al., 2012), stroke (Palomäki et al., 1989), congestive heart failure (Bradley et al., 1985), obesity and metabolic syndrome (Levy et al., 2009), and diabetes (Punjabi et al., 2002).
Treatment of OSA includes continuous positive airway pressure (CPAP), and mandibular advancement devices (MAD) (National Institute for Care Excellence, 2021). Primary airway therapies aim to maintain upper airway patency during sleep and lead to a normalisation of the work of breathing and prevention of apnoeas, hypopnoeas, and arousals from sleep that could cause the sympathetic response. Long-term therapy of OSA improves daytime symptoms and, potentially, long-term cardiovascular risks (Somers et al., 2008).
CPAP therapy remains the most common treatment for moderate-to-severe OSA, while for milder cases of OSA, MADs can also be effective (NICE (National Institute for Health and Care Excellence), 2008). However, long-term adherence to CPAP therapy is limited, with only $70\%$ adherence at 3-month (Benjafield et al., 2019b) and further reductions at later follow up (Benjafield et al., 2021). Non-CPAP therapies provide alternatives for patients who have difficulties with long-term compliance to CPAP (Randerath et al., 2021) and may be preferred over conventional treatment (Campbell et al., 2015).
Recently, electrical stimulation invasively applied using hypoglossal nerve stimulation (HNS) (Strollo et al., 2014) or transcutaneous electrical stimulation (TESLA) in the submental area to target the upper airway dilator muscles, particularly the genioglossus muscle, has been developed to treat OSA (Pengo et al., 2016). The randomised controlled trial using HNS (STAR-trial) reported modest improvements in the diastolic blood pressure with no significant changes in systolic blood pressure or heart rate over a 1-year period (Strollo et al., 2014). However, data on the acute cardiorespiratory responses to transcutaneous electrical stimulation of the upper airway dilator muscles, in both health and disease remain sparse (Pengo and Steier, 2015). This is a study to consider the physiological response to the use of electrical stimulation in direct proximity to the carotides, as hypoglossal nerve stimulation (HNS) and transcutaneous electrical stimulation is nowadays being used to treat obstructive sleep apnoea; the purpose of this study was to test the effects of the current has on the cardiorespiratory system in a cohort of normal subject (Strollo et al., 2014).
We hypothesize that acute application of transcutaneous electrical stimulation of the submental area will influence cardiovascular control in healthy, awake subjects. In the current study, we sought to describe the effect of transcutaneous electrical stimulation of the submental area on the cardiorespiratory control, for this particular purpose, we recorded beat-by-beat blood pressure with other cardiopulmonary variables when exposed to room air, hypoxic and hypercapnic gas mixtures (chemosensitivity) while in seated and supine postures, as well as with 50° HDT (baroreceptor response) while using electrical stimulation of the submental area (TES).
## Methods and subjects
The study was approved by the local research ethics committee (King’s College London; RESCM-$\frac{20}{21}$-8487) and performed in accordance with the Declaration of Helsinki. All participants received an information sheet and provided informed and written consent prior to participation.
## Subjects
We included healthy, normal- and slightly overweight subjects of both sexes over 16 years of age. All participants were non-smokers and free of cardiorespiratory and other significant acute or chronic illness and had normal blood pressure. Participants visited the respiratory physiology laboratory on two occasions at least 1 week apart, with one visit acting as control without electrical stimulation and the other during which TES was used during in all postures and gas conditions.
## Inclusion criteria
Subjects for the study met all the following criteria: age >16 years, body-mass index (BMI) > 18.5 and <30 kg/m2, non-smoker, and clinically stable in the last 28 days.
## Exclusion criteria
Subjects were excluded from the study if any of the following conditions were met: history of cardiovascular, respiratory, or neuromuscular disease, cardiac pacemaker, active seizures, current smokers, acute illness, allergy to skin patches, oobesity (BMI>30 kg/m2) or cachexia (BMI<18.5 kg/m2), and vertigo.
## Primary and secondary outcomes
The primary outcome of the study was the change in the diastolic blood pressure (BP) with electrical stimulation, affecting baro- and chemoreceptor response. Secondary outcomes were changes in other cardiovascular (systolic/mean BP, heart rate) and respiratory variables (respiratory rate, tidal volume, minute ventilation, modified Borg scale) during electrical stimulation.
## Equipment
Following the baseline visit without electrical stimulation the participants were continuously stimulated using electrical current in the submental area (4 × 4 cm dermal patches; Med-Fit Plus Ltd., Stockport, United Kingdom), at a frequency of 30 Hz and a pulse width of 250 microseconds during the second visit. Intensity of the electrical current was titrated according to individual comfort using a TENS machine (Premier Combo Plus, the TENS + Company Lets, Stockport, United Kingdom, placed in the submental area midway between angle of mandible and the chin (Figure 1) as previously described elsewhere (Steier et al., 2011). Continuous, beat-by-beat arterial blood pressure was measured continuously using digital artery photoplethysmography (Finapres, Ohmeda 2,300, BOC Healthcare, Englewood CO, United States of America). Heart rate was measured from the electrocardiogram (ECG) with electrodes positioned in the lead II configuration (ML132 bioamplifier, ADInstruments, Oxford, United Kingdom).
**FIGURE 1:** *Placed in the submental area midway between angle of mandible and the chin as described previously (Steier et al., 2011).*
Respiratory flow was measured via a mouthpiece, with the subject wearing a noseclip using a pneumotachograph (4,800 series, Hans Rudolph Inc., Shawnee Kansas, United States of America) and associated differential pressure transducer (Spirometer, ADInstruments, Oxford, United Kingdom). The distal end of the pneumotachograph was attached to a two way non-rebreathing valve (2,700 series, Hans Rudolph Inc., Shawnee, Kansas, United States of America, deadspace 77 ml) with inspired and expired gases measured continuously using a gas analyser (ML, 206, ADInstruments, Oxford, United Kingdom), connected to a side port on the pneumotachograph via a fine-bore catheter. Blood oxygen saturation (SpO2) was measured using a pulse oximeter (Sat 805 pulse oximeter, Charter Kontron, United Kingdom) attached to the subject’s finger. All data were acquired (PowerLab 16, ADInstruments, Oxford, United Kingdom) with 1 Khz sampling and displayed (LabChart ver 8, ADInstruments, Oxford, United Kingdom). Tidal volume was obtained by digital integration of flow by the acquisition software.
An open circuit (Figure 2) was used to deliver a continuous supply of medical air (wall outlet) to the inspiratory port of the two-way non-rebreathing valve via a low volume (2.5 L) reservoir bag. The inspired gas could be enriched with $100\%$ nitrogen or $100\%$ carbon dioxide from cylinders (BOC, Guildford, United Kingdom) to provide the appropriate inspired gas concentration. Three inspired gas mixtures were used; medical air ($21\%$ O2, balance N2), poikilokapnic hypoxia ($12\%$ O2, balance N2) and normoxic hypercapnia ($5\%$ CO2, balance N2). Symptoms of breathlessness were scored using the modified Borg scale in each posture (seated, supine, and 50°HDT).
**FIGURE 2:** *Experimental setup; and the inspiratory gas mixture could be enriched titrated (0%–100%) with 100% nitrogen or 100% carbon dioxide from cylinders (BOC, Guildford, United Kingdom).*
An electrically operated tilt table (Plinth2000 Ltd., Stowmarket, United Kingdom) which could be adjusted from 0° (flat) to 50°HDT was used to change posture.
## Short protocol
The following parameters were recorded at baseline: *Demographic data* (date of birth, height, weight, body mass index, ethnicity, and gender), clinical history, and medications. The neck, hips, and waist were measured along with vital signs (heart rate and blood pressure).
Measurements were first recorded in the seated position with the subject exposed to 5 min of each gas mixture, randomly assigned, before moving to the tilt table with measurements commencing in the supine position. Subjects were secured to the tilt table using a foam mattress and a foot strap across the ankles. Participants were familiarized with the 50°HDT procedure prior to the experiment commencing. After an initial period of stabilisation (at least 5 min) in the supine position, a period of 5 min resting breathing was recorded. The table was then tilted to the 50°HDT for 10 min. At the end of the 50°HDT, the subject was returned to the supine position for a further 5 min. Spontaneous ventilation and end-tidal gases (EtO2, EtCO2), and oxygen saturations were recorded throughout. The tilt table procedure was repeated three times with the subject breathing in random order (Figure 3). Participants were blinded to the identity of the gas being administered. For safety, the stop criterion for the hypoxic gas mixture was achieved if the arterial oxygen saturation (SpO2) dropped below $80\%$. To account for equilibration for change in posture and different gas mixtures, the final 2 min of recording for each posture and each gas mixture were analysed and an average reported for each variable.
**FIGURE 3:** *Schematic representation of study procedures. HDT: Head down Tilt. CO2: hypercapnic gas mixture (5% CO2). O2: hypoxic gas mixture (12% O2). Subjects were breathing room air, hypoxic, or hypercapnic gas mixtures in random order in seated, supine, and HDT (50) position. (off): electrical current off. (on): electrical current on (transcutaneous electrical nerve stimulation). Subjects were studied using each gas mixture for 5 min seated and supine, and for 10 min in HDT.*
## Data processing
All data were recorded in real-time using LabChart software (Chart V8, ADInstruments, Dunedin, New Zealand) with an analog-to-digital conversion at a sampling of 1 kHz. Data were exported and assigned key time periods for further analysis. Each variable was averaged over the last 2 min in seated, supine, and HDT positions. Respiratory variables (tidal volume (Vt) and respiratory rate (RR)) were extracted and multiplied to calculate minute ventilation (VE). Systolic (SBP), diastolic (DBP), and pulse blood pressure (pBP) was computed as pBP = SBP-DBP, and mean arterial pressure (MAP) was calculated as MAP = $\frac{1}{3}$ SBP + $\frac{2}{3}$ DBP. Heart Rate was derived from the 3-lead ECG, and SpO2 from the pulse oximeter.
## Sample size calculation
Based on the sample size of 13 subjects, the study detected a treatment difference at a two-sided significance level of 0.025 if the true mean difference in diastolic blood pressure (electrical stimulation on vs. off) was at least 8.049 mmHg (SD 10.6) with $80\%$ power. The variable calculated was the minimal detectable difference in mean diastolic blood pressure, based on previous data (Strollo et al., 2014).
## Statistical analysis
Following testing for normality, data were presented as mean (SD) unless otherwise indicated. Data were analysed using a three-way analysis of variance (ANOVA)) followed by a Tukey’s test using the ‘anovan’ and ‘multcompare’ function of MATLAB (Version 2022B, MathWorks Ltd, Natick/MA, United States of America) to evaluate overall effects of three factors: a) TES (on/off), b) posture (seated, supine, HDT), and c) inspired gas (RA, hypercapnia, and hypoxia); furthermore, baroreflex and chemoreflex interaction was tested with the combination of these three factors. A level of significance was defined as $p \leq 0.05.$
## Results
We studied 13 healthy subjects (age 29 [12] years, six female, BMI 23 (1.6) kg/m2, waist: hip (W: H) ratio 0.87 (0.05)) (Supplementary Table S1). Two more volunteers were unable to participate in the second visit and had incomplete datasets recorded for the primary outcome, these were not included in the analysis. Subjects used an electrical current of 8 [2] mA, which had been titrated to a comfortable and tolerable level of skin sensation. There were no adverse events, and no participant required electrical stimulation to be stopped.
## Systolic blood pressure
A marked reduction in systolic blood pressure during electrical stimulation was observed under hypoxic conditions in the HDT posture; there was also a trend towards reduction in other postures. ( Table 1).
**TABLE 1**
| Posture | Gas mixtures | Systolic blood pressure (mmHg) | Systolic blood pressure (mmHg).1 | Systolic blood pressure (mmHg).2 | Systolic blood pressure (mmHg).3 |
| --- | --- | --- | --- | --- | --- |
| Posture | Gas mixtures | Visit 1 (TENS-) | Visit 2 (TENS+) | Delta Δ | 95% CI |
| Seated | Room Air | 142.33 (20.34) | 127.56 (17.87) | -14.76 (22.90) | -36.10 to 6.572 |
| Seated | Hypercapnoea | 155.44 (20.85) | 139.42 (18.26) | -16.02 (23.99) | -38.37 to 6.324 |
| Seated | Hypoxia | 148.81 (23.28) | 134.69 (15.91) | -14.13 (24.52) | -36.96 to 8.713 |
| Supine | Room Air | 129.73 (20.55) | 123.14 (16.62) | - 6.59 (26.05) | -30.86 to 17.68 |
| Supine | Hypercapnoea | 137.42 (27.25) | 131.12 (20.69) | - 6.29 (26.92) | -31.37 to 18.78 |
| Supine | Hypoxia | 142.19 (20.32) | 125.64 (19.01) | -16.55 (26.68) | -41.65 to 8.552 |
| HDT 50° | Room Air | 121.47 (19.69) | 116.40 (21.85) | - 5.07 (23.13) | -26.62 to 16.48 |
| HDT 50° | Hypercapnoea | 130.61 (25.91) | 116.13 (23.47) | -14.48 (27.31) | -39.92 to 10.96 |
| HDT 50° | Hypoxia | 132.98 (20.68) | 112.03 (18.82) | -20.95 (19.97) | -39.55 to -2.349 |
## Diastolic and Mean Blood Pressure
A marked reduction in both diastolic and mean arterial blood pressures were also observed during electrical stimulation in supine and HDT postures. There was also a tendency towards a reduction in diastolic blood pressure during electrical stimulation when seated under hypoxic conditions, and during HDT both under hypercapnic and room air conditions. ( Tables 2, 3).
## Pulse pressure
There was no significant change to the pulse pressure when applying electrical stimulation, independent of different postures and gas mixtures. ( Table 4).
**TABLE 4**
| Posture | Gas mixtures | Pulse pressure (mmHg) | Pulse pressure (mmHg).1 | Pulse pressure (mmHg).2 | Pulse pressure (mmHg).3 |
| --- | --- | --- | --- | --- | --- |
| Posture | Gas mixtures | Visit 1 (TENS-) | Visit 2 (TENS+) | Delta Δ | 95% CI |
| Seated | Room Air | 53.50 (10.11) | 51.35 (13.01) | -2.15 (10.62) | -8.569 to 4.269 |
| Seated | Hypercapnoea | 62.43 (11.75) | 53.47 (13.61) | - 8.96 (10.31) | -18.58 to 0.6577 |
| Seated | Hypoxia | 58.72 (13.31) | 55.02 (11.51) | -3.69 (15.14) | -13.01 to 5.615 |
| Supine | Room Air | 49.85 (12.71) | 46.69 (11.58) | -3.16 (15.64) | -11.21 to 7.208 |
| Supine | Hypercapnoea | 48.03 (16.85) | 48.88 (14.01) | 0.84 (16.92) | -14.91 to 16.61 |
| Supine | Hypoxia | 50.33 (16.14) | 51.81 (15.80) | 1.48 (23.18) | -12.53 to 15.49 |
| HDT 50° | Room Air | 44.01 (13.01) | 47.40(16.36) | 3.39(21.82) | -9.107 to 16.58 |
| HDT 50° | Hypercapnoea | 42.36 (19.01) | 44.90(15.85) | 2.53(20.84) | -16.88 to 21.96 |
| HDT 50° | Hypoxia | 46.01 (15.72) | 47.07(16.61) | 1.05(21.25) | -11.79 to 13.90 |
## Heart rate
The heart rate did not significantly change with electrical stimulation, and this observation was independent of posture or gas mixture. ( Table 5).
**TABLE 5**
| Posture | Gas mixtures | Heart rate (BPM) | Heart rate (BPM).1 | Heart rate (BPM).2 | Heart rate (BPM).3 |
| --- | --- | --- | --- | --- | --- |
| Posture | Gas mixtures | Visit 1 (TENS-) | Visit 2(TENS+) | Delta Δ | 95% CI |
| Seated | Room Air | 69.65 (10.44) | 73.25 (11.19) | 3.60 (10.70) | -2.861 to 10.07 |
| Seated | Hypercapnoea | 73.91 (9.90) | 76.91 (12.79) | 2.92 (8.01) | -7.755 to 1.909 |
| Seated | Hypoxia | 77.71 (7.80) | 79.85 (13.59) | 2.14 (12.10) | -9.455 to 5.171 |
| Supine | Room Air | 62.49 (6.54) | 65.38 (12.23) | 2.89 (7.65) | -1.733 to 7.507 |
| Supine | Hypercapnoea | 64.33 (7.08) | 66.87 (11.71) | 2.53 (9.40) | -8.216 to 3.147 |
| Supine | Hypoxia | 72.53 (8.10) | 71.56 (13.69) | - 0.97 (8.57) | -4.210 to 6.151 |
| HDT 50° | Room Air | 62.74 (10.31) | 66.28 (13.15) | 3.54 (7.48) | -0.9742 to 8.060 |
| HDT 50° | Hypercapnoea | 70.86 (17.20) | 69.13 (13.78) | 1.51 (12.42) | -9.023 to 12.03 |
| HDT 50° | Hypoxia | 74.24 (7.24) | 72.51 (13.17) | -1.73 (13.17) | -3.680 to 7.151 |
## Respiratory variables
There was no change in the respiratory rate with electrical stimulation in any of the three postures studied. ( Table 6).
**TABLE 6**
| Posture | Gas mixtures | Respiratory rate (1 x min−1) | Respiratory rate (1 x min−1).1 | Respiratory rate (1 x min−1).2 | Respiratory rate (1 x min−1).3 |
| --- | --- | --- | --- | --- | --- |
| Posture | Gas mixtures | Visit 1 (TENS-) | Visit 2 (TENS+) | Delta Δ | 95% CI |
| Seated | Room Air | 17.14 (4.82) | 17.24 (3.72) | 0.10 (4.27) | -3.881 to 4.080 |
| Seated | Hypercapnoea | 18.78 (4.51) | 19.74 (4.65) | 0.96 (3.30) | -2.114 to 4.029 |
| Seated | Hypoxia | 18.06 (4.56) | 17.07 (3.59) | - 0.99 (3.50) | -4.253 to 2.273 |
| Supine | Room Air | 17.18 (3.86) | 17.25 (3.82) | 0.07 (3.52) | -3.211 to 3.345 |
| Supine | Hypercapnoea | 19.29 (3.22) | 19.09 (4.28) | - 0.19 (2.62) | -2.638 to 2.252 |
| Supine | Hypoxia | 17.83 (4.05) | 15.12 (4.36) | - 2.71 (4.74) | -7.118 to 1.705 |
| HDT 50° | Room Air | 18.64 (4.12) | 19.93 (4.32) | 1.28 (2.66) | -1.194 to 3.762 |
| HDT 50° | Hypercapnoea | 20.93 (3.27) | 20.18 (4.68) | - 0.75 (3.36) | -3.886 to 2.383 |
| HDT 50° | Hypoxia | 17.95 (4.65) | 18.83 (5.83) | 0.89 (5.83) | -4.373 to 6.148 |
## Tidal volume and minute ventilation
There was a trend towards increased tidal volume in the supine posture under hypercapnic conditions with electrical stimulation, although this did not reach significance. ( Table 7) Additionally, there was increased minute ventilation with electrical stimulation ($$p \leq 0.0443$$; Table 8;Figure 4; details is provided in Supplementary Table S3).
## End-tidal carbon dioxide (ETCO2) and oxygen saturation
No changes in EtCO2 during electrical stimulation in seated, supine or HDT posture were observed when breathing room air, under hypoxic or hypercapnic conditions. The SpO2 did not change when comparing electrical stimulation to baseline in any posture or gas condition studied.
## Modified borg scale
There was no significant change in the breathlessness scores when applying electrical stimulation, independent of posture and gas mixture. ( Supplementary Table S2).
## 3-Way ANOVA
In a 3-way ANOVA, BP decreased significantly with TES (systolic: $$p \leq 4.3$$E-06, diastolic: $$p \leq 3.01$$E-09, mean: $$p \leq 3.26$$E-08). Change in gas condition (systolic: $$p \leq 0.0386$$, diastolic: $$p \leq 0.0032$$, mean: 3.2E-03) and different postures (systolic: 6.91E-08, diastolic: $$p \leq 6.55$$E-04, mean: $$p \leq 4.91$$E-05) similarly impacted on BP control. There were no significant interactions between the three different factors: electrical stimulation, gas condition, or posture, except for an effect on minute ventilation (gas condition/posture $$p \leq 0.0348$$; Figure 4 for supplemental information on the analysis please refer to Supplementary Table S3).
## Discussion
The cardiorespiratory response to submental transcutaneous electrical stimulation, a novel therapeutic approach in OSA, applied during enhanced chemoreceptor (gas conditions) activation and baroreceptor (posture) loading demonstrates marked effects on cardiovascular control, with a modest effect on the respiratory control. Electrical stimulation appears to sensitise the arterial baroreceptor response resulting in decreased diastolic blood pressure, by $19\%$–$25\%$, under hypoxic conditions (chemoreceptor) supine and with HDT (baroreceptor). The effect of electrical current on the systolic blood pressure was slightly less consistent, albeit a reduction of $16\%$ was observed in HDT position under hypoxic conditions. There were no significant differences in the heart rate or the pulse pressure with electrical current; this was independent of posture or gas mixture used. Respiratory variables did not change significantly with electrical stimulation, except for the minute ventilation.
## Significance of findings
A number of pathways are involved in the cardiovascular responses to systemic hypoxia (Marshall, 1994), involving the primary effects of peripheral chemoreceptor stimulation, secondary effects of ventilation, and direct effects of hypoxia on the heart and peripheral vasculature leading to subsequent effects on the autonomic and the central nervous system (Marshall, 1994). The full effects of ventilation, mediated by carbon dioxide and oxygen, on the cardiovascular system remain to be fully elucidated (Heistad et al., 1974). Importantly, there is cardiorespiratory interaction which is mediated via hypoxia and that affects the baroreflex response, as suggested by our observations. It has been described previously that stimulation of the chemoreceptors can lead to an increased heart rate and a change in the blood pressure (cardiovagal baroreflex) in humans (Bristow et al., 1971); (Bristow et al., 1974). This is further supported by recent evidence showing that exposure to hypoxia can alter the arterial baroreflex and change heart rate and sympathetic nerve activity with a higher blood pressure. ( Heistad et al., 1974); (Halliwill and Minson, 2002); (Halliwill et al., 2003).
Electrical stimulation targets the upper airway dilator muscles, particularly the genioglossus muscle (GG), and counteracts their diminished neuromuscular state-dependent tone which promotes upper airway collapsibility (Mezzanotte et al., 1996). Both the invasive and transcutaneous approaches to stimulating the upper airway dilator muscles are beneficial for maintaining airway patency during sleep in patients with OSA (Strollo et al., 2014; Pengo et al., 2016) improving the AHI by a mean of 9.1 ($95\%$ confidence interval, CI 2.0, 16.2) events/hour and the $4\%$ ODI improved by a mean of 10.0 ($95\%$ CI 3.9, 16.0) events/hour (Pengo et al., 2016). Furthermore, the initial feasibility studies used TESLA with a current of 10.1 (3.7) mA (Steier et al., 2011). In the current study, electrical stimulation was well tolerated and had no adverse effects, underlining its safety for the use in the submental area and its efficacy in lowering diastolic blood pressure. In the context of potential long-term treatments for patients with OSA who have a high prevalence of cardiovascular comorbidities it is important to highlight that heart rate did not change significantly. A reduction in the blood pressure, systolic and diastolic, remains a favourable outcome for patients with sleep-disordered breathing, as the cardiovascular risk is typically raised and treatment resistant hypertension is of clinical relevance in this cohort (Antic et al., 2015). There are various interactions between different types of sleep apnoea and cardiovascular variables (e.g., blood pressure). On the one hand, central sleep apnoea is driven by heart failure (Javaheri and Javaheri, 2022). On the other hand, obstructive sleep apnoea leads to an increased sympathetic tone with may impact on the blood pressure contributing to hypertension (Antic et al., 2015); (Pengo et al., 2021); (Pengo et al., 2020).
## Limitations of the study
This prospective physiological study had a relatively small sample size and certain interactions could become more significant with a larger sample size, for example the effect of the three factors on minute ventilation. The effects of TES on the primary outcome variable, diastolic blood pressure, were highly consistently observed in all subjects with a large effect size. Longer steady state periods could have had further advantages over quasi-steady state achieved during the 5 min baseline periods used. The choice of this was pragmatic to allow for completion of what was a lengthy protocol and return of the healthy volunteers for a demanding second session. We were also limited with making causal inferences due to the observational nature of the design of the study. Despite a complex experimental setup, some parameters such as neural respiratory drive, blood gases, and perfusion could have provided helpful additional insights into the interaction between the cardiovascular, the respiratory, the peripheral autonomic and the central nervous system but were not measured on this occasion. In addition, this study focused on healthy subjects with normal blood pressure. Thus, further studies in subjects with hypertension and sleep-disordered breathing need to provide a comprehensive dataset on how electrical stimulation affects the chemo- and baroreceptor response in these clinically relevant cohorts. However, these points do not negate the insightful setup of a highly complex physiological experiment in human beings with a large effect size that enables to derive useful information for future clinical applications.
## Conclusion
Electrical stimulation of the submental area affects the chemo- and the baroreceptor response in normal healthy volunteers resulting in substantially lower levels of blood pressure. Similarly, inspired gas and posture impact on blood pressure regulation. Furthermore, electrical stimulation might modulate the cardiovascular risk in patients with hypertension and sleep-disordered breathing, a hypothesis that warrants further investigation in the respective clinical cohorts.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by The study was approved by the local research ethics committee (King’s College London; RESCM-$\frac{20}{21}$-8487). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AA, GR, and JS. Performed experiments AA, JS, and GR. Analyzed data. AA, GR, and JS. Interpreted results of experiments. AA. Prepared figures. AA and JS. Drafted manuscript. AA, GR, JS, MS-P, GK and GP. Edited and revised manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1089837/full#supplementary-material
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|
---
title: Expression of hypoxia-inducing factor-1α and matrix metalloproteinase-9 in
the recipient parasylvian cortical arteries with different hemodynamic sources in
adult moyamoya disease
authors:
- Mingrui Luo
- Jin Yu
- Can Xin
- Miao Hu
- Tianshu Tao
- Guiping Wan
- Jincao Chen
- Jianjian Zhang
journal: Frontiers in Surgery
year: 2023
pmcid: PMC10043197
doi: 10.3389/fsurg.2023.1080395
license: CC BY 4.0
---
# Expression of hypoxia-inducing factor-1α and matrix metalloproteinase-9 in the recipient parasylvian cortical arteries with different hemodynamic sources in adult moyamoya disease
## Abstract
### Objective
In our latest research, we have demonstrated that the recipient parasylvian cortical arteries (PSCAs) with hemodynamic sources from the middle cerebral artery (M-PSCAs) has a higher risk of postoperative cerebral hyperperfusion (CHP) syndrome than those from non-M-PSCAs in adult moyamoya disease (MMD) patient. However, whether there are differences between M-PSCAs and non-M-PSCAs in vascular specimens characteristics has not been studied. In this study, we further investigate the vascular specimen of recipient PSCAs by histological and immunohistochemical methods.
### Methods
50 vascular specimens of recipient PSCAs were obtained from 50 adult MMD patients during the combined bypass surgeries in our departments of Zhongnan hospital. 4 recipient PSCAs samples were also obtained in the same way from the middle cerebral artery occlusion patients. The samples were received the pathological sectioning, hematoxylin and eosin staining, and immunohistochemistry, then the vascular wall thickness, matrix metalloproteinase-9 (MMP-9) and hypoxia-inducing factor-1α (HIF-1α) were analyzed.
### Results
M-PSCAs adult MMD patients had a thinner intima than non-M-PSCAs in the recipient PSCAs specimens. In recipient non-M-PSCAs vascular specimens, the immunoreactivity indicating HIF-1α and matrix metalloproteinase-9 (MMP-9) was significantly higher than M-PSCAs groups. The logistic regression analyses showed that the M-PSCAs was an independent risk factor of postoperative cerebral hyperperfusion (CHP) syndrome (OR 6.235, $95\%$ CI1.018-38.170, $$P \leq 0.048$$) in MMD.
### Conclusion
Our results indicate that M-PSCAs adult MMD patients had thinner intima than non-MCAs adult MMD patients in the PSCAs. More importantly, HIF-1α and MMP-9 were overexpressed in non-M-PSCAs vascular specimens.
## Introduction
Moyamoya disease (MMD) is a chronic cerebrovascular disease that is characterized by bilateral steno-occlusive changes of distal part of internal carotid artery (ICA) and proximal part of middle and anterior cerebral arteries, with abnormal smoke-like vessels at the base of the brain [1]. The etiology of the disease is still unknown. MMD occurs around the world, with the most significant incidence in East Asian countries, but rare in Europe and the United States [2].
Cerebral revascularization, superficial temporal artery (STA)–middle cerebral artery (MCA) bypass, is recognized as one of the potent surgical approaches for the treatment of adult patients with MMD [3]. STA-MCA anastomosis not only prevents postoperative cerebral ischemic attacks but also reduces the risk of rebleeding in the postoperative period, whereas cerebral hyperperfusion (CHP) syndrome is one of the potential complications of this surgery [4]. CHP syndrome is defined as an increase in local cerebral blood flow (CBF) at the anastomotic site of more than $150\%$ over preoperative values, which may lead to intermittent aphasia, epilepsy and even intracranial hemorrhage [5]. At the same time, our latest study also found that the recipient PSCAs with hemodynamic source from MCA (M-PSCAs) had a higher risk factor of postoperative CHP syndrome compared to the recipient PSCAs with hemodynamic source from non-MCA (non-M-PSCAs) in MMD [6]. Although some studies have proposed differences in the recipient PSCAs (the distal MCA-M4) vascular specimens characteristics of MMD patients, such as, the differences in MMD and cerebrovascular occlusion, adults and children [7, 8], the difference between M-PSCAs and non-M-PSCAs in vascular specimens characteristics in adult MMD has rarely been studied.
In this study, we obtained vessel specimen of the recipient PSCAs (the distal MCA-M4) between M-PSCAs and non-M-PSCAs in adult MMD during surgery. Then, we treated them with histological and immunohistochemical methods. We will study its vascular histopathological and immunohistochemical characteristics to investigate their differences.
## Patients
Fifty patients with MMD were treated surgically at the Department of Neurosurgery, Zhongnan Hospital of Wuhan University, China, from November 2021 to April 2022. All recruited patients were diagnosed as MMD according to the Japanese research committee on MMD [9]. We obtained vessel specimens from the MMD patients during the bypass surgery (Figure 1). The grouping of patients with MMD in the preoperative period was blindly investigated by two senior neurosurgeons in preoperative digital subtraction angiography (DSA). According to the hemodynamic source of recipient PSCAs from MCA and non-MCA, the MMD patients were divided into M-PSCAs and non-M-PSCAs (Figure 2). There were 31 people in the M-PSCAs and 19 people in non-M-PSCAs. 4 patients with MCA occlusion were treated with STA-MCA bypass surgery; then four control PSCAs samples were also obtained in the same manner. The baseline data and clinical characteristics of patients were presented in Table 1. This study was conducted under the guidelines provided by Zhongnan Hospital Ethics Committee.
**Figure 1:** *Surgical view of superficial temporal artery (STA)–middle cerebral artery (MCA) bypass. White arrow indicates STA. Lower black arrow indicates MCA (PSCA) and upper black arrow indicates tiny piece of PSCA which is collected as specimen for further histological and immunohistochemical study.* **Figure 2:** *Analysis of the major blood sources of the PSCAs. Representative figures of M-PSCAs and non-M-PSCAs, which are defined as the PSCAs (white circles and ovals) with blood flow from the MCA and non-MCAs. Panels A1–4 indicate the major hemodynamic sources of PSCAs originating from the MCA; B1–4 indicate those from the posterior cerebral artery (PCA).* TABLE_PLACEHOLDER:Table 1
## Sample preparation
In the STA-MCA bypass procedure, a 10-0 nylon monofilament was passed around the wall of the recipient PSCAs (MCA-M4 portion, 0.5–1.0 mm in diameter). By lifting the monofilament with microforces to pull up the PSCAs, the operator carefully dissects the artery using a high-powered microscope. All samples of the PSCAs were obtained postoperatively without disrupting the recipient PSCAs. Then, STA-MCA anastomosis was performed. These samples were first fixed overnight in $10\%$ formalin for several hours and then embedded in paraffin the subsequent day. In each sample, we cut several 4-mm-thick tissue sections from the paraffin block in turn, then removed the paraffin in xylene, rehydrated, and finally received hematoxylin and eosin staining.
## Antibodies
A monoclonal antibody against human hypoxia-inducible factor-1α (HIF-1α; 1:200; Proteintech, Wuhan, China) and a monoclonal antibody against rabbit matrix metalloproteinase-9 (MMP-9;1:500; Servicebio, Wuhan, China) were performed as the primary antibodies in the present study, respectively. As for secondary antibodies, the horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-mouse immunoglobulin G antibody (1:200; Servicebio, Wuhan, China) were used.
## Immunohistochemical process
In this part, the sections are first washed with phosphate-buffered saline (PBS; PH7.4). After being blocked with $3\%$ H2O2 and incubating for 25 min at room temperature protected from light, the sections were preincubated with normal goat serum and then incubated overnight at 4 °C with the desired primary antibody. The sections were washed in PBS (pH 7.4). After the sections were slightly dried, the tissue was covered with a secondary antibody (HRP-labeled) of the corresponding species of primary antibody and incubated for several minutes at room temperature. Diaminobenzidine (DAB) chromogenic solution was added to the circles after the sections were slightly dried. The color development time of the sections needs to be controlled under the microscope. The sections were treated with hematoxylin stain solution, hematoxylin differentiation solution and hematoxylin returning blue solution, respectively. The nucleus of hematoxylin stained is blue, and the positive expression of DAB is brownish yellow. Visualize staining of tissue under a microscope (Nikon,Tokyo, Japan) and an image analysis software package (Image-Pro Plus 6.0).
## Immunohistochemical analysis
Under microscope (Nikon,Tokyo, Japan), the histological data were acquired with a computer. Next, the thickness of intima and media and the percentage of immunopositive cell area in the whole specimen were analyzed with Image-Pro Plus. Using Image-Pro Plus, the immunoreactivity was assessed by Luo and Yu blinded to baseline characteristics. They produced their results for the percentage of immunopositive cells in each sample three times and recorded the mean score.
## Statistical analysis
As for the results of the thickness of intima and media and the percentage of the area of immunopositive cells in the whole specimen, statistical analysis was performed using the Mann-Whitney U test and Fisher's exact test. The data are were represented as the mean ± standard deviation. The values $p \leq 0.05$ were considered.
## Postoperative cerebral hyperperfusion syndrome in moyamoya disease in relation with clinical characteristics
In unitivariate analysis, M-PSCAs, surgical side and hypertension were related to an increased risk of postoperative CHP syndrome, respectively (Table 2). After adjusting for potential covariables (Table 3), the logistic regression analyses showed that M-PSCAs (OR 6.235, $95\%$ CI1.018-38.170, $$p \leq 0.048$$) was independent risk factors of postoperative cerebral hyperperfusion syndrome.
## Thickness of intima and media
With the above sections, the thickness of the intima and media in hematoxylin and eosin-stained were measured (Figure 3). The vascular walls of PSCAs from MMD had thicker intima compared to the control recipient PSCAs (MCA occlusion). In contrast, media became thinner in MMD patients. The mean thickness of intima in MMD patients was significantly higher than that in MCA occlusion (Figure 4A–C, Table 4: MMD, 14.2 ± 4.2 μm; and MCA occlusion, 10.0 ± 1.4 μm, $$p \leq 0.024$$), but the mean thickness of the MMD media was obviously thinner (Figure 3A–C, Table 4: MMD, 27.6 ± 7.3 μm; and control, 40.5 ± 8.3 μm; $$p \leq 0.006$$). Subsequently, we performed an analysis of the thickness of intima and media between recipient M-PSCAs and non-M-PSCAs in MMD patients. In the present study, there was a significantly higher intimal thickening in non-M-PSCAs than M-PSCAs in MMD patients (Table 5: M-PSCAs, 12.8 ± 3.8 μm; non-M-PSCAs, 16.5 ± 4.0 μm, $$p \leq 0.004$$). However, there was no difference between M-PSCAs and non-M-PSCA in MMD in the media thickness. ( Table 5: M-PSCAs, 26.4 ± 7.2 μm; non-M-PSCAs, 29.5 ± 7.3 μm, $$p \leq 0.087$$).
**Figure 3:** *(A,B): Photomicrographs of specimens of the PSCAs (MCA) taken from patients with moyamoya disease between non-M-PSCAs (A) and M-PSCAs (B) disease showing intimal hyperplasia and curved internal elastic lamina (A, arrow), and specimens of the PSCA (C) taken from control subjects. Scale bar, 20 µm (A,B) and 50 µm (C); Hematoxylin and eosin stain, original magnification, ×40 (A,B) and ×20 (C).* **Figure 4:** **Immunohistochemical analysis* of HIF-1α and MMP-9.HIF-1α-immunopositive cells were detected in the PSCAs specimens from patients with non-M-PSCAs MMD (A). (B), in M-PSCAs MMD, a smaller number of such cells are detected. MMP-9-immunopositive cells were detected in the PSCAs specimens from patients with non-M-PSCAs MMD (C). (D), in M-PSCAs MMD, a smaller number of such cells are detected. Scale bar, 50 µm (A,B) and 20 µm (C,D); original magnification, ×20 (A,B) and ×40 (C,D).* TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5
## Expression of HIF-1α immunoreactivity
Subsequently, an immunohistochemical studies on the recipient PSCAs in MMD patients with HIF-1α were performed. The expression of HIF-1α in non-M-PSCAs is higher than M-PSCAs. For the M-PSCAs specimens, 37.1 ± $13.8\%$ of the area of immunopositive cells demonstrated anti-HIF-1α immunoreactivity in the whole specimen (Figure 4, Table 6). On the contrary, for non-M-PSCAs specimens, the values were 49.2 ± $16.4\%$ (Figure 4, Table 6). ( $$p \leq 0.041$$).
**Table 6**
| Unnamed: 0 | M-PSCAs (n = 50) | Non-M-PSCAs (n = 12) | p value |
| --- | --- | --- | --- |
| HIF-1α (%) | 37.1 ± 13.8 | 49.2 ± 16.4 | 0.041 |
| MMP-9 (%) | 18.2 ± 7.0 | 25.6 ± 7.7 | 0.02 |
## Expression of MMP-9 immunoreactivity
Finally, we also did an immunohistochemical studies on the recipient PSCAs in MMD patients with MMP-9. As regards the M-PSCAs MMD specimens, 18.2 ± $7.0\%$ of the area of immunopositive cells demonstrated anti-MMP-9 immunoreactivity in the whole specimens (Figure 4, Table 6). On the contrary, for non-M-PSCAs MMD specimens, the values were 25.6 ± $7.7\%$ (Figure 4, Table 6). ( $$p \leq 0.020.$$).
## Discussion
In this study, we have access to data on the recipient PSCAs specimens from the patients during surgery. With histopathological findings, MMD patients had thicker intima and thinner media than MCA occlusion, but M-PSCAs MMD patients had a thinner intima than non-M-PSCAs. In addition, the immunoreactivity of HIF-1α and MMP-9 in whole specimens was higher in non-M-PSCAs MMD than that in M-PSCAs MMD patients.
In the histological investigations of autopsy specimens, we have known that the MCA from MMD patients had fewer than normal smooth muscle cells both in the media and thick intima [10]. This study also found such results in the recipient PSCAs (M4 portion) when MMD was compared to MCA occlusion. It may indicate that the lesions of the whole cerebrovascular was similar in MMD. Meanwhile, the M-PSCAs had a thinner intima than non-M-PSCAs in adult MMD patients. During bypass surgery, our senior neurosurgeons found that the recipient M-PSCAs vascular walls in MMD are transparent and thinner, which meant that it was prone to tearing when anastomosing it, leading to longer operation times and narrowing of the anastomosed vessels. So, it's a challenge to some neurosurgeons. Combined with the study, M-PSCAs in MMD patients was independent risk factors of postoperative cerebral hyperperfusion syndrome (CHP), because it may lead to that intracranial hemodynamic distribution after surgery will be limited to an extremely fixed zones [11]. Furthermore, our recent work also found that M-PSCAs in MMD patients were significantly correlated with the onset of focal CHP because the blood supply of M-PSCAs usually came from a morbid MCA, which are severely stenotic or smoke-like vessels, but majority of the another group had normal hemodynamic source, leading to lower blood flow velocity, pressure and postoperative hemodynamic changes in M-PSCAs [6]. In addition, the formation of smoke-like vessels in M-PSCAs may be unstable and the cerebral cortex perfused by each artery may be more restricted in a certain area in M-PSCAs compared to that in non–M-PSCAs MMD patients [6]. Postoperative CHP syndrome is one of the serious complications after direct bypass that result in intermittent aphasia, epilepsy, and even intracerebral hemorrhage [12, 13]. Therefore, whether the recipient M-PSCAs in MMD patients are suitable for direct bypass should be further discussed. In future work, we may do a certain number of indirect bypasses and combined bypasses in M-PSCAs MMD patients, followed by follow-up to compare the efficacy of both procedures. Finally, we will identify the role of direct bypass in M-PSCAs MMD and determine whether it needs direct bypass.
Why there are significant differences in intimal thickness between M-PSCAs and non-M-PSCAs in MMD has confused us. According to that the blood supply of M-PSCAs usually came from non-normal MCA compared to non-M-PSCAs, we have some speculations. On the one hand, owing to the blood supply of it from stenotic MCA and the self-protective mechanism in the brain, PSCAs will increase cerebral blood flow by dilating the diameter of blood vessels. On the other hand, that the blood supply to M-PSCAs comes from severely stenosed or smoke-like MCAs leads to a change in blood flow volume through the stenosed vessels, which may increase the pressure on the vessels. Within the study, we also performed an immunohistochemical analysis of HIF-1α and MMP-9 expression in the recipient PSCAs vascular specimens. HIF-1α is a major factor participating in tissue-oxygen homeostasis and vascular endothelial growth factor (VEGF); MMP-9 also involves VEGF. Therefore, we chose HIF-1α and MMP-9 because they are genes related to VEGF. We all know that MMD is a chronic as well as ischemic cerebrovascular occlusive disease. HIF-1α, a transcriptional activator participating in tissue-oxygen homeostasis, is a heterodimeric protein formed by inducible HIF-1α and constitutively expressed HIF-1α proteins [14, 15]. In periods of cerebral ischemia, HIF-1α is activated due to insufficient oxygen supply and a decrease in the partial pressure of oxygen in the tissues [16]. In the assistance of co-activators, such as cyclic adenosine monophosphate response element-binding protein and acetyl-transferase, both HIF-1α and HIF-1β form a heterodimer [17] and HIF-1α is moved to the nucleus and binds to the target gene hypoxia response element (HRE), followed by induction of downstream gene expression. Its target genes encode molecules participating in many important physiological activities, such as vasomotor control, angiogenesis, erythropoiesis, cell proliferation, energy metabolism, and so on (18–20). VEGF is a renowned downstream target gene of HIF-1α and widely involved in the pathological process of cerebral ischemia, and the VEGF family is distinguished by its powerful angiogenic properties [21]. In our present study, we found that the expression of HIF-1α in non-M-PSCAs is higher than M-PSCAs in vascular specimens. That would explain why M-PSCAs MMD had a thinner intima compared to non-M-PSCAs. At the same time, the study also found that HIF-1α is expressed in the chronic hypoxic zone surrounding the infarcted area during cerebral ischemia [22]. Therefore, HIF-1α may be a valuable new therapeutic target, especially for ischemic MMD.
In present study, we also found that Suzuki stage 3 and 5 were an independent risk for M-PACSAs and non-M-PSCAs, respectively (Table 1). It indicates that there may be a temporal correlation between M-PACSAs and non-PSCAs in disease progression. In other words, M-PSCAs may transform into non-M-PSCAs with the time going by. In our study, we also analyzed the expression of MMP-9 in vascular specimens of non-M-PSCAs and M-PSCAs by immunohistochemistry. MMP-9 is named gelatinase B and its substrates include gelatins (denatured collagens), native type IV, V and XI collagens, laminin and so on [23]. MMP-9 has a dual function when involved in angiogenesis [24]. In the early phase of cerebral angiogenesis, the increased expression of MMP-9 under the regulation of vascular endothelial growth factor leads to the disruption of endothelial junctions and the release of hidden VEGF binding sites (25–27). In our study, the expression of MMP-9 in non-M-PSCAs is higher than M-PSCAs in vascular specimens, which further demonstrates why non-M-PSCA has a thicker intima compared to M-PSCAs and that M-PSCAs may transform into non-M-PSCAs in the future. In the later phase of cerebral angiogenesis, excessive activation of MMP-9 exhibits anti-angiogenic effects by degrading endothelial basal lamina and tight junction proteins, leading to endothelial instability and eventually causing blood-brain barrier (BBB) breakdown [23]. A study also revealed that melatonin inhibits MMP-9 expression through a related signaling pathway, which subsequently ensures the integrity of BBB function and reduces vascular permeability [28]. So, it also indicates that non-M-PSCAs may develop into hemorrhagic MMD in the future. We may also conclude that higher expression of MMP-9 in vessel specimens have lower risk in postoperative CHP in MMD patients. However, some previous studies have shown that increased expression of MMP-9 in serum specimens have higher risk in postoperative CHP in MMD patients (29–31). It seems to be contradictory. However, previous studies were all check the MMP-9 level in serum rather than in vascular wall. The relationship between the expression of MMP-9 in intracranial vascular wall and postoperative CHP in MMD needs more studies to explore. As we mentioned, Suzuki stage 3 is the independent risk for M-PSCAs MMD, which means M-PSCAs MMDs are in a rapidly progressive stage and its collateral vessels will increase. Since MMD is a progressive disease, the level of MMP-9 is dynamic rather than stable during the disease progress. The relatively lower expression of MMP-9 in vascular specimens in M-PSCAs MMD, may leads to relatively low vascular permeability and a relatively complete functioning BBB. In order to pass through BBB, more MMP-9 in peripheral blood needs to be produced to promote angiogenesis in M-PSCAs MMD. Therefore, it may lead to this phenomena that M-PSCAs MMD has higher expression of MMP-9 in serum but lower in PSCAs vessel specimens. This needs to be further confirmed. On the other hand, the expression of MMP-9 in intracerebral arterial serum and peripheral blood serum in the M-PSCAs MMD may be lower than non-M-PSCAs MMD patients in this study, but M-PSCAs MMD patients still have higher risk postoperative CHP, which revealed that the hemodynamic sources from PSCAs is more important for postoperative CHP compared to vascular permeability caused by MMP-9. In future studies, we will further confirm the relationship between MMP-9 and CHP, hoping to provide reliable evidence for the treatment of MMD as well as preventing CHP.
All in all, this study still has certain limitations. First, our study focused on the tissue of the recipient PSCAs (distal MCA vessels) in adult MMD, and we have made some new research progress on adult MMD. However, for the treatment of pediatric MMD, we usually perform indirect bypass surgery, and it is challenging to obtain vascular specimens. Therefore, for the time being, this study is still not progressing on pediatric MMD. Then, the expression of MMP-9 and HIF-1α in vessel specimens were compared, but not in the serum. Therefore, we are unsure if there is a difference between in vessel specimens and serum. If there is, it may provide new findings. It is also worth exploring whether its expression in serum is more stable and accurate because of the tedious vascular immunohistochemical process. Lastly, the mechanism of postoperative CHP syndrome and PSCA thickness in patients with MMD needs to be further studied.
## Conclusions
Our results indicate that M-PSCAs MMD patients had thinner intima than non-MCAs MMD patients in the PSCAs. More importantly, HIF-1α and MMP-9 were overexpressed in PSCAs specimens in non-MCAs MMD.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Ethics statement
Ethical approval was obtained from the Zhongnan Hospital Ethics Committee (approval number: Kelun-2017005).
## Author contributions
Conceived the project, designed the project, analyzed data, and drafted the manuscript: ML, CX. Extract and analyzed data: JY, MH. Designed the project: ML, TT. Edited the manuscript and approved the final version: GW, JC, JZ. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Association between the overall burden of comorbidity and Ct values among
the older patients with Omicron infection: Mediated by inflammation'
authors:
- Meixia Wang
- Hongfei Mi
- Na Li
- Qingfeng Shi
- Wei Sun
- Tingjuan He
- Jiabing Lin
- Wenting Jin
- Xiaodong Gao
- Bijie Hu
- Chenghao Su
- Jue Pan
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10043200
doi: 10.3389/fimmu.2023.1145044
license: CC BY 4.0
---
# Association between the overall burden of comorbidity and Ct values among the older patients with Omicron infection: Mediated by inflammation
## Abstract
### Objectives
To investigate the associations between the overall burden of comorbidity, inflammatory indicators in plasma and Ct values among the elderly with COVID-19.
### Methods
We conducted a retrospective observational study. The results of each nucleic acid test of during hospitalization were obtained. Linear regression models assessed the associations between the overall burden of comorbidity, inflammatory indicators in plasma and Ct values among the elderly. A causal mediation analysis was performed to assess the mediation effects of inflammatory indicators on the association between the overall burden of comorbidity and Ct values.
### Results
A total of 767 COVID-19 patients aged ≥ 60 years were included between April 2022 and May 2022. Patients with a high burden of comorbidity had significantly lower Ct values of the ORF gene than subjects with a low burden of comorbidity (median, 24.81 VS 26.58, $P \leq 0.05$). Linear regression models showed that a high burden of comorbidity was significantly associated with higher inflammatory responses, including white blood cell count, neutrophil count and C-reactive protein. Also, white blood cell count, neutrophil count, C-reactive protein and the overall burden of comorbidity assessed by age-adjusted Charlson comorbidity index were independent risk factors for the Ct values. A mediation analysis detected the mediation effect of white blood cells on the association between the burden of comorbidity and Ct values, with the indirect effect estimates of 0.381 ($95\%$ CI: 0.166, 0.632, $P \leq 0.001$). Similarly, the indirect effect of C-reactive protein was -0.307 ($95\%$ CI: -0.645, -0.064, $$P \leq 0.034$$). White blood cells and C-reactive protein significantly mediated the relationship between the burden of comorbidity and Ct values by $29.56\%$ and $18.13\%$ of the total effect size, respectively.
### Conclusions
Inflammation mediated the association between the overall burden of comorbidity and Ct values among elderly with COVID-19, which suggests that combined immunomodulatory therapies could reduce the Ct values for such patients with a high burden of comorbidity.
## Introduction
The Omicron variant has spread rapidly worldwide, inducing a pandemic in Shanghai in March 2022 [1]. Thus far, at least 32 mutations have been identified in the spike protein, resulting in high transmissibility and immune escape (2–4). On the other hand, Omicron infection was associated with a lower rate of hospital admission and mortality [5, 6]. However, the elderly were more likely to be infected with Omicron due to immunosenescence and high comorbidity burden [7, 8]. The rate of severe Omicron infections were increased in the older patients and in individuals with comorbidities such as hypertension, diabetes, cardiovascular disease, and chronic respiratory disease [9, 10]. Moreover, it was reported that the severe illness was related to high viral copies [11, 12]. Therefore, understanding the underlying mechanism of Ct values, representing the degree of SARS-CoV-2 viral loads among the elderly with COVID-19, may be helpful for the early implementation of therapy.
As we know, viral loads are associated with infectiousness, transmissibility, disease severity, and mortality [11]. Previous studies reported that the elderly and chronic medical diseases might influence the viral loads/Ct values. Patients’ age was found to be positively correlated with the viral loads [13, 14], partly because of immunosenescence [15]. Additionally, the elderly were more likely to have comorbidities. Several studies have identified that congestive heart failure, hypertension, diabetes, chronic kidney disease, and coronary artery disease are associated with higher SARS-CoV-2 copies of the viral genome or lower Ct values [9, 16, 17]. Most of these studies mainly focused on the simple relationship between a single chronic medical disease and Ct values/viral loads. However, a person might suffer a high burden of comorbidity with multiple chronic medical diseases. The overall comorbidity burden is of important as it considers multiple chronic medical conditions. The effect of the overall burden of comorbidity on Ct values remains unclear and needs to be further elucidated.
The inflammatory response has recently emerged as an essential factor in COVID-19 patients. Numerous studies reported a direct association between preexisting comorbidities and inflammation, which might impact the immune response to COVID-19. For example, the experimental model showed that COVID-19 with elevated glucose levels directly promoted viral replication, cytokine production, and subsequent T cell dysfunction [18, 19]. It was also reported that COVID-19 with hypertension delayed viral clearance and exacerbated airway hyper inflammation [20, 21]. Cancer patients with COVID-19 have impaired lymphocyte function, neutropenia, and decreased in white cell count [22, 23]. This suggests that the delayed viral clearance and hyper inflammation are involved in COVID-19 patients with comorbidities, possibly contributing to severe illness. Furthermore, the correlation of respiratory viral loads were found to be correlated with inflammatory indicators in the plasma of elderly patients [24]. White blood cells, neutrophils, and lymphocytes were significantly lower in patients with a high viral load (Ct ≤ 25) [25]. Considering these results, the associations between Ct values and the preexisting comorbidities might be affected by inflammation. However, most previous the epidemiological reports focused on evaluating the simple association between comorbidity and Ct values. The effect of inflammation on the relationship between preexisting comorbidities and Ct values needs to be further elucidated in real-world data. Due to the importance of viral loads in disease severity, assessing the effect of inflammation on the association between Ct values and comorbidities might inform proper therapeutic strategies, especially for the elderly with comorbidities.
The aim of this study was to investigate the associations between the overall burden of comorbidity, inflammatory indicators in plasma, and Ct values among the elderly with COVID-19. A mediation analysis was conducted to explore the mediation effects of inflammatory indicators on the relationship between the overall burden of comorbidity and Ct values.
## Study population
This retrospective observational study was conducted at Zhongshan Hospital, Fudan University (Geriatrics Medicine Center), between April 2022 and May 2022. The Geriatrics Center was designated as a temporary COVID-19 hospital during the Omicron outbreak in Shanghai, mainly receiving older patients. Patients with positive nucleic acid testing for SARS-CoV-2 were included. Only COVID-19 patients aged ≥60 years old were eligible in this study. Patients without complete medical history were excluded from this study. Finally, 767 COVID-19 patients were included. There demographic information (age, gender, COVID-19 vaccination, time of hospital admission and discharge), COVID-19 diagnosis, chronic medical conditions, laboratory results of inflammatory indicators in plasma before treatment (white blood cell, neutrophil count, lymphocyte count, C-reactive protein and procalcitonin), and the results of each nucleic acid test of during hospitalization (Ct values) were retrospectively collected. This study was approved by the Ethical Committee of Zhongshan Hospital, Fudan University.
Patients were divided into a non-severe group and a severe group according to disease severity. Patients were allocated to the severe group if they satisfied any of the following requirements proposed by the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition):1) shortness of breath, RR≥30 times/min; 2) in the resting state, oxygen saturation ≤$93\%$ during air inhalation; 3) arterial partial pressure of oxygen (PaO2)/inspired oxygen concentration (FiO2) ≤300 MMHG (1mmHg=0.133kPa); 4) the clinical symptoms were progressively aggravated, and the chest imaging showed that the lesions significantly progressed > $50\%$ within 24-48 hours; 5) respiratory failure requiring mechanical ventilation; 6) shock; and 7) complicated with other organ failure requiring ICU care.
## Nucleic acid testing
A real-time reverse transcription-polymerase chain reaction (RT-PCR) assay was performed to detect the SARS-CoV-2 of nasal swab samples with AutraMic mini4800 Plus equipment. Liferiver (Shanghai ZJ Bio-Tech Co., Ltd.) A novel coronavirus 2019-nCoV nucleic acid detection kit was used. The Ct values of ORF1ab, the nucleocapsid protein (N) and the E gene were obtained. Most inpatients underwent nucleic acid testing every 2 days until meeting discharge criteria, i.e., the Ct values of N gene and ORF1ab gene >35 in two consecutive nucleic acid tests or two consecutive negative results on the nucleic acid test. The minimum Ct values of the ORF gene, N gene and E gene during hospitalization were obtained. Lower Ct values indicated higher SARS-CoV-2 virus copies [26]. We applied Ct values to represent the degree of SARS-CoV-2 viral loads.
## Burden of comorbidity
The overall burden of comorbidity was assessed by the modified form age-adjusted Charlson comorbidity index (aCCI), which accounts for multiple chronic medical conditions [27, 28]. The CCI is the most extensively studied and widely used comorbidity index [29]. Data on multiple chronic medical conditions, including myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular accident or transient ischemic attack, dementia, COPD, donnective tissue disease, peptic ulcer disease, liver disease, diabetes mellitus, hemiplegia, moderate to severe chronic kidney disease, solid tumor, leukemia, lymphoma and AIDS were retrospectively collected from electronic medical record. ACCI score was calculated for each patient by using a freely accessible online calculator (https://www.mdcalc.com/charlson-comorbidity-index-cci#use-cases). The mean value of the aCCI score was 4 points. Patients were divided into two groups according to the mean value of the aCCI score. ACCI score >4 point was defined as high burden of comorbidity, and others were defined as low burden of comorbidity.
## Statistical analysis
Categorical variables were presented as n (%).Ct values were described by median (IQR). Mean ± SD was calculated for inflammatory indicators. Pearson’s Chi-squared test was performed for categorical variables. The differences in inflammatory indicators among different groups were analyzed by t-test. Wilcoxon Mann-Whitney rank test was applied to compare the Ct values among different groups. The linear regression models were performed to investigate the associations between inflammatory indicators, the overall burden of comorbidity, and Ct values of ORF gene (the minimum Ct values of the ORF gene during hospitalization). Coefficient values (β) and $95\%$ confidence intervals ($95\%$ CIs) were calculated.
In order to assess the mediation effects of inflammatory indicators on the associations between the overall burden of comorbidity and Ct values (the minimum Ct values of the ORF gene during hospitalization), a PROCESS model of mediation analysis with R/bruceR package was conducted. The confidence intervals (CIs) of effect estimates were calculated with the bootstrap method. Variations in Ct value over time were visualized by fitting smooth lines using a loess method. Statistical analyses were performed using the R-4.1.2 software. Two-sided test with $P \leq 0.05$ indicating statistical significance was used.
## Differential associations of Ct values with the overall burden of comorbidity
A total of 767 COVID-19 patients with a mean age of 78.5 years old were included. Among these, $57.2\%$ were female patients, and < $30\%$ received the COVID-19 vaccine. There were 35 severe COVID-19 patients ($4.6\%$). The distributions of different characteristics according to the overall burden of comorbidity were shown in Table 1. The proportion of patients with a high burden of comorbidity ($9.2\%$) in the severe group was significantly higher than that of patients with a low burden of comorbidity ($1.9\%$), and the difference was statistically significant ($P \leq 0.05$). In addition, patients with a high burden of comorbidity had a lower rate of COVID-19 vaccine compared with the low comorbidity burden group ($P \leq 0.05$). It was also found that patients with a high burden of comorbidity had significantly higher levels of white blood cells, neutrophil count, C-reactive protein, and procalcitonin compared to patients with a low burden of comorbidity (all $P \leq 0.05$). Also, a significantly lower lymphocyte count was observed among patients with a low burden of comorbidity ($$P \leq 0.022$$).
**Table 1**
| Variables | Overall(n=767) | Low burden of comorbidity (n=285) | High burden of comorbidity (n=482) | P value |
| --- | --- | --- | --- | --- |
| Gender | Gender | Gender | Gender | Gender |
| Male | 328 (42.8) | 199 (41.3) | 129 (45.3) | 0.317 |
| Female | 439 (57.2) | 283 (58.7) | 156 (54.7) | 0.317 |
| Vaccination | Vaccination | Vaccination | Vaccination | Vaccination |
| No | 499 (70.2) | 272 (59.9) | 227 (88.3) | <0.001 |
| Yes | 212 (29.8) | 182 (40.1) | 30 (11.7) | <0.001 |
| Group | Group | Group | Group | Group |
| Non-severe group | 730 (95.4) | 472 (98.1) | 258 (90.8) | <0.001 |
| Severe group | 35 (4.6) | 9 (1.9) | 26 (9.2) | |
| Minimum Ct value of ORF gene | 25.73 (11.25) | 26.58 (11.5) | 24.81 (10.49) | 0.005 |
| Minimum Ct value of N gene | 25.39 (12.43) | 26.14 (12.7) | 24.39 (11.77) | 0.013 |
| Minimum Ct value of E gene | 25.71 (11.24) | 26.10 (11.34) | 24.89 (11.05) | 0.065 |
| WBC(x 109/L) | 6.15 (3.32) | 5.61 (2.07) | 6.95 (4.47) | <0.001 |
| N(x 109/L) | 4.11 (4.08) | 3.67 (4.23) | 4.76 (3.76) | 0.002 |
| L(x 109/L) | 1.49 (1.19) | 1.59 (1.00) | 1.35 (1.41) | 0.022 |
| CRP(mg/L) | 26.21 (47.56) | 14.18 (30.71) | 43.36 (60.46) | <0.001 |
| PCT(ng/mL) | 0.74 (4.08) | 0.15 (0.59) | 1.39 (5.82) | 0.025 |
The median, minimum Ct value of the ORF gene during hospitalization in patients with a high burden of comorbidity was 24.81, which was significantly lower than that in subjects with a low burden of comorbidity (median, 26.58, $P \leq 0.05$). Similarly, the Ct values of the N gene and E gene were lower in the high burden of the comorbidity group compared to the low burden of the comorbidity group. Figures 1A–C showed the distributions of Ct values according to the overall burden of comorbidity. COVID-19 patients with a high burden of comorbidity were more likely to have lower Ct values ($P \leq 0.05$), indicating higher viral loads. It was also found that severe COVID-19 patients had lower Ct values (Figures 1D–F).
**Figure 1:** *Boxviolin plots of Ct values. (A–C): Distributions of Ct values for target genes according to the burden of comorbidity. (D–F): Distributions of Ct values for target genes according to disease severity. P values were calculated by a two-tailed Mann–Whitney test between groups.*
## Differential association of Ct values with disease severity depending on patient’s overall comorbidity burden
A subgroup analysis was conducted to confirm whether the lower Ct values in patients with a high burden of comorbidity might be due to the increased prevalence of severe COVID-19 patients compared to patients with a low burden of comorbidity (Figure 2). Our results showed that the associations between Ct values and disease severity depended on the burden of comorbidity, wherein disease severity was significantly related to lower Ct values only in patients with a high burden of comorbidity ($P \leq 0.05$) but not in patients with a low burden of comorbidity. Ct values dynamics of patients also showed severe patients had lower Ct values during the first 20 days recovery process compared with non-severe patients in the subgroup with the high comorbidity burden (Figures 2D–F). Also, this phenomenon was not obvious in the subgroup with a low burden of comorbidity. These results suggested that viral clearance in COVID-19 patients might be differently regulated according to the burden of comorbidity.
**Figure 2:** *Kinetic changes of Ct values according to disease severity. (A–C): Distributions between disease severity and Ct values for target genes in high and low comorbidity burden subgroups. (C–E): Kinetic changes of Ct values for target genes in patients with a high burden of comorbidity from severe and non-severe groups. (F–I): Kinetic changes of Ct values for target genes in patients with a low burden of comorbidity from severe and non-severe groups. The blue and red lines showed the trend in viral loads of severe and non-severe groups, respectively, using curve fit with non-linear regression with 95% confidence intervals (shaded color) from the regression line. TTD: Times to detection of nucleic acid testing.*
## Mediation effects of inflammatory markers on the association between the overall burden of comorbidity and Ct values
Our results showed that a high burden of comorbidity was significantly associated with higher inflammatory response, including white blood cell count (β: 1.23, $95\%$ CI: 0.59, 1.86), neutrophil count (β: 1.21, $95\%$ CI: 0.66, 1.76), and C-reactive protein (β: 20.32, $95\%$ CI: 11.04, 29.6) (Table 2). Furthermore, a linear regression model was applied to explore associations between inflammatory indicators and Ct values (Table 3). Higher C-reactive protein was associated with lower Ct values with a coefficient value of -0.02 ($95\%$CI: -0.03,-0.01, $$P \leq 0.0223$$). White blood cell and neutrophil count were positively associated with Ct values (β [$95\%$ CI]: 0.28[0.12-0.45] and 0.20[0.07,0.33], respectively). Meanwhile, a high burden of comorbidity was significantly associated with lower Ct values (β [$95\%$ CI]: -1.08 [-2.09,-0.06]).
After a detailed exploration of the associations among the overall burden of comorbidity, Ct values, and inflammatory indicators, we assumed that the inflammatory indicators mediate the association between the burden of comorbidity and Ct values, which was subsequently confirmed through a mediation analysis (Table 4). The total effect estimates of white blood cell and c-reactive protein on Ct values were -1.289 ($95\%$ CI: -2.393, -0.069) and -1.630 ($95\%$ CI:-2.832, -0.299), respectively. The mediation effect of white blood cells on the association between the comorbidity burden group and Ct values was found, with the indirect effect estimates of 0.381 ($29.56\%$ of the total effect size, $95\%$ CI: 0.166, 0.632, $P \leq 0.001$). Similarly, C-reactive protein was found to significantly mediate the relationship between the burden of comorbidity and Ct values ($18.13\%$ of the total effect size, β= -0.307, $95\%$ CI: -0.645, -0.064, $$P \leq 0.034$$). No mediation effects were observed for the neutrophil count, lymphocyte count, and procalcitonin.
**Table 4**
| Inflammatory Indicators | Effect estimates | Effect | 95% CI | P value | Prop.Mediated |
| --- | --- | --- | --- | --- | --- |
| WBC | Indirect | 0.381 | 0.166, 0.632 | 0.001 | 29.56% |
| WBC | Direct | -1.67 | -2.765, -0.450 | 0.007 | 29.56% |
| WBC | Total | -1.289 | -2.393, -0.069 | 0.037 | 29.56% |
| N | Indirect | 0.192 | -0.017, 0.497 | 0.125 | 14.84% |
| N | Direct | -1.486 | -2.769, -0.270 | 0.015 | 14.84% |
| N | Total | -1.294 | -2.530, -0.069 | 0.036 | 14.84% |
| L | Indirect | -0.053 | -0.662, 0.020 | 0.752 | 4.12% |
| L | Direct | -1.232 | -2.407, 0.076 | 0.056 | 4.12% |
| L | Total | -1.285 | -2.470, -0.004 | 0.039 | 4.12% |
| CRP | Indirect | -0.307 | -0.645, -0.064 | 0.034 | 18.83% |
| CRP | Direct | -1.324 | -2.577, 0.024 | 0.047 | 18.83% |
| CRP | Total | -1.63 | -2.832, -0.299 | 0.013 | 18.83% |
| PCT | Indirect | -0.094 | -0.256, 0.075 | 0.259 | 6.20% |
| PCT | Direct | -1.422 | -3.248, 0.398 | 0.12 | 6.20% |
| PCT | Total | -1.516 | -3.360, 0.299 | 0.094 | 6.20% |
## Discussion
The present study aimed to explore the effect of the overall burden of comorbidity and inflammatory indicators on Ct values among elderly patients with COVID-19. Our findings highlighted that inflammation-mediated the relationship of comorbidity burden with Ct values. It was also found that Ct values were associated with disease severity depending on patients’ comorbidity burden. To the best of our knowledge, this is the first study that specifically aimed to identify the mediation effect of inflammatory indicators on the relationship between comorbidity burden and Ct values among older patients with COVID-19. Most previous studies solely focused on the associations between the single medical chronic disease and inflammatory indicators with Ct values [16, 21, 24].
The Omicron variant is characterized by immune evasion [30]. In the present study, a high burden of comorbidity was associated with lower Ct values (high viral loads) in older patients with Omicron infection. This relationship was also detected in patients with earlier variant infection [9, 17]. It is worth noting that compared to the earlier variant, the rate of severe Omicron infections increased among the elderly [31]. The low Ct values in the elderly with a high burden of comorbidity may contribute to the disease severity. Additionally, the results highlighted that the overall burden of comorbidity assessed by the age-adjusted Charlson comorbidity index was an independent risk factor for the Ct values. It has been reported that the Charlson comorbidity index predicted poor clinical outcomes and mortality in COVID-19 patients [7, 32]. Therefore the Charlson Comorbidity index might contribute to the management of the older patients with COVID-19.
The underlying mechanism of low Ct values in older COVID-19 patients with a high burden of comorbidity remains unclear. We found that a high burden of comorbidity in older patients was associated with enhanced inflammatory responses in plasma with elevated white blood cell count, neutrophil count, and C-reactive protein compared with subjects with a low burden of comorbidity. Furthermore, serum levels of inflammatory indicators were related to Ct values, which was consistent with previous studies [24, 25, 33]. Based on current results, it could be hypothesized that inflammatory indicators might contribute to the association between the burden of comorbidity and Ct values. Therefore, we conducted a causal mediation analysis. Finally, we confirmed that white blood cells and C-reactive protein significantly mediated the relationship between the burden of comorbidity and Ct values by $29.56\%$ and $18.13\%$ of the total effect size, respectively. These results were unsurprising as age-related diseases share inflammatory pathogenesis and age-related decline and dysregulation of immune function (34–36). Also, the degree of immune dysfunction correlates with disease severity [37, 38]. Previous studies have reported that white blood cells and C-reactive protein are early indicators of progression to serious disease and in-hospital mortality in COVID-19 patients (39–42). Chen et al. also proposed an immune hypothesis for the COVID-19 vulnerability of older adults [34], which was further supported by our findings. In addition, Padilla et al. reported that remdesivir combined with immunomodulatory therapy had a better effect in patients with Ct values < 25 [43]. Also, our findings might contribute to the elucidation of the underlying mechanism. Antiviral treatments combined with immunomodulatory therapy might be particularly helpful for the elderly patients with COVID-19 and a high burden of comorbidity.
The presence of a mediator involves a causal pathway between exposure and outcome [44]. Mediation analysis is widely used to explore and evaluate biological mechanisms and unknown biological pathways (45–47). The criteria for certain factors to be regarded as a mediator is that exposure should have a statistically significant association with mediator, and that mediator should also have a statistically significant association with outcome [48]. In the current study, we found that the burden of comorbidity was significantly associated with inflammatory indicators. Also, we observed a significant relationship between serum levels of inflammatory indicators and Ct values. Thereby, we conducted a causal mediation analysis, where Ct values, used as an outcome variable, were regressed on inflammatory indicators (mediator variable) and burden of comorbidity (independent variable). Our results supported the partial mediation of inflammation on the association between the burden of comorbidity and Ct values among the elderly.
The latest systematic review reported an inconclusive relationship between COVID‐19 severity and viral loads [11]. We observed that the associations between Ct values and disease severity depended on the overall burden of comorbidity, wherein disease severity was significantly related to lower Ct values only in patients with a high burden of comorbidity but not in patients with a low burden of comorbidity. Moreover, we noted that viral clearance was delayed in patients with COVID-19 and a high burden of comorbidity compared to patients with a low burden of comorbidity. The low Ct values (higher viral loads) in older patients with a high burden of comorbidity may explain a potential mechanism underlying the relationship between COVID‐19 viral loads and disease severity.
The present study has some limitations. First, we did not have quantitative viral loads. However, previous studies reported that Ct values were positively associated with viral loads [26]. Lower Ct values indicate higher viral loads. Second, some inflammatory markers associated with critical cases of COVID-19, such as IL-6, interleukin (IL)-1β, and TNF-α, were not included [39]. Third, we did not have the information about treatments that would allow us to assess the effect of antiviral treatments and immunomodulatory therapy on COVID-19 viral loads. These issues should be addressed by further studies.
In China, there were about 264 million individuals aged≥60 years old in 2020, accounting for $18.70\%$ of the total population [49]. If a large number of elderly became infected with SARS-CoV-2 in the future, this would pose a substantial challenge; thus, greater focus should be placed on the elderly with a high burden of comorbidity. In this study, we revealed that a high overall comorbidity burden in older patients with COVID-19 was associated with lower Ct values, partly mediated by inflammation. Moreover, we found that the differential association of Ct values with disease severity among the elderly depended on patient’s overall comorbidity burden. These conclusions have relevant implications for combined immunomodulatory therapies for older patients with COVID-19, which might contribute to effectively reducing the progression to serious disease, especially for the elderly with high burden of comorbidity.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/wangmeixia223542/original-data.git.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethical Committee of Zhongshan Hospital, Fudan University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
MW, HM, CS and JP contributed to the study conception and design. Material preparation, data collection and analysis were performed by MW, NL, QS, WS, TH, JL and WJ. MW, HM, XG and BH drafted manuscript. All authors critically reviewed the manuscript and approved the final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer
and unbalanced classification model
authors:
- Zhi-Wen Liu
- Gang Chen
- Chao-Fan Dong
- Wang-Ren Qiu
- Shou-Hua Zhang
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10043203
doi: 10.3389/fphys.2023.1105891
license: CC BY 4.0
---
# Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model
## Abstract
As one of the most common diseases in pediatric surgery, an inguinal hernia is usually diagnosed by medical experts based on clinical data collected from magnetic resonance imaging (MRI), computed tomography (CT), or B-ultrasound. The parameters of blood routine examination, such as white blood cell count and platelet count, are often used as diagnostic indicators of intestinal necrosis. Based on the medical numerical data on blood routine examination parameters and liver and kidney function parameters, this paper used machine learning algorithm to assist the diagnosis of intestinal necrosis in children with inguinal hernia before operation. In the work, we used clinical data consisting of 3,807 children with inguinal hernia symptoms and 170 children with intestinal necrosis and perforation caused by the disease. Three different models were constructed according to the blood routine examination and liver and kidney function. Some missing values were replaced by using the RIN-3M (median, mean, or mode region random interpolation) method according to the actual necessity, and the ensemble learning based on the voting principle was used to deal with the imbalanced datasets. The model trained after feature selection yielded satisfactory results with an accuracy of $86.43\%$, sensitivity of $84.34\%$, specificity of $96.89\%$, and AUC value of 0.91. Therefore, the proposed methods may be a potential idea for auxiliary diagnosis of inguinal hernia in children.
## 1 Introduction
The incidence of inguinal hernia in children is common. Galinier et al. [ 2007] pointed out that the incidence of inguinal hernia in children of any age is about $0.8\%$–$4.4\%$, and in premature babies, it is even as high as $30\%$. Generally, inguinal hernia in pediatric patients is caused by their congenital abnormalities. Although some new methods are studied in this issue (Chowdhury et al., 2019; Molinaro et al., 2022; Zhao et al., 2022), for patients with different conditions, treatment methods also differ. If there is only hernia and no serious diseases such as intestinal necrosis, conservative treatment will be adopted. If serious diseases such as incarcerated necrosis of the intestines occur, surgical treatment will be adopted to prevent the risk of internal damage to the renal organs of pediatric patients. Usually, the diagnosis of intestinal necrosis of inguinal hernia is determined by medical imaging equipment, doctor’s clinical experience, or symptoms after surgery. Because medical imaging examinations have a greater radiation impact on children than on adults, m any parents disagree with children’s medical imaging examinations. At this time, medical expertise is very important for the diagnosis of intestinal necrosis in pediatric patients, and it is also a test for experts.
With the continuous advancement of concepts in the area of precision medicine, the application of intelligent algorithms in medical diagnosis has become increasingly extensive. By constructing predictors on clinical data, the purpose of assisting diagnosis is achieved. Common MRI imaging data (Gurses et al., 2019; Wadhwa et al., 2019; Wang et al., 2022), CT imaging data (Mohakud et al., 2019; Masselli et al., 2020; Singh et al., 2020; Zhuang et al., 2021), and EEG imaging data (Prucnal and Polak, 2019; Quintero-Rincón et al., 2020) are helpful in the work. However, there are a few auxiliary diagnosis models based on medical digital and textual data. However, some researchers introduced special cases in more detail or performed simple analysis on the current patient’s condition (Shiqi et al., 2018; János et al., 2020; Sabra et al., 2020; Abdulrahman et al., 2021; Beau et al., 2021; Karhade et al., 2021; Radhakrishnan et al., 2021; Hyun et al., 2022; Lin et al., 2022; Oh et al., 2022). No corresponding auxiliary diagnosis model was constructed based on these data because the data information that can be mined by case analysis or statistical analysis is very limited. Scrutinio et al. [ 2020] used machine learning algorithms to build a decision-making model for the prognosis of stroke survivors, providing better guidance for doctors in clinical diagnosis. We can combine machine learning algorithms to collect more information from digital and textual clinical data (Ricciardi, 2019; Onan, 2020; Onan and Tocoglu, 2021), such as some important examination parameters or making diagnosis decisions for patients, which is extremely significant to the doctor’s accurate diagnosis. Some doctors had conducted a retrospective bicentric study in this point (Bouassida et al., 2022).
In this work, we used the clinical data of pediatric patients with inguinal hernia and non-inguinal hernia. However, clinical data are different from other data. From the actual examination items performed by the patient to the collection of clinical data, some vacancies can easily occur in the examination parameters. In order to better collect more information from limited data and build a corresponding model, the nature of the data needs to be followed in the research process. If researchers blindly pursue the complexity and diversity of sample parameters, the possible consequence is that there are too few samples that can be used in the experiment, which is not conducive to experimental research. Selecting appropriate characteristic values from some common examinations of patients is a data mining method worth exploring. Therefore, in this study, we defined a model using blood routine test parameters as M1, a model using liver and kidney function test parameters as M2, and a model using blood routine test parameters and liver and kidney function test parameters as M3.
We first used statistical analysis methods to preprocess the original data and used the RIN-3M (median, mean, and mode region random interpolation) method to fill in the vacancy in the data. Second, the importance of features was compared according to the Gini coefficient (Fang et al., 2012), and the combination of features with the best performance was selected in an iterative manner. Third, we used an ensemble learning method (Onan, 2017) to deal with the problem of sample imbalance. Finally, the samples after feature selection and the original samples were put into the RF algorithm to train them as predictors. Comparing the performance of each model, we found that the model after feature selection had better performance.
The analysis process of these data is shown in Figure 1, where Smajority and Sminority represent the processed majority class sample and minority class sample, respectively; Smaj1,Smaj2,...,Smaj3 represent the sub-samples equally divided in the training set of the samples of the majority class; and Smin represents the samples used for training in the samples of the minority class. The specific profile of most samples divided into sub-samples under different parameters is shown in Table 1.
**FIGURE 1:** *Flow chart of data analysis.* TABLE_PLACEHOLDER:TABLE 1
## 2.1 Logistic regression (LR)
The logistic regression algorithm (Abu-Hanna and de Keizer, 2003; Zhu and Fang, 2016; Li et al., 2019) plays an important role in aiding decision-making in clinical medicine, where researchers construct linear regression functions using clinical examination parameters as characteristic input parameters and map the values obtained from the linear regression functions between 0 and 1 by means of a sigmoid function, thus achieving classification. It is commonly used to construct equations for the relationship between input vectors and categories. The principle of this study is shown in Eqs 1, 2. y=c0+c1x1+c2x2+c3x3+…+cnxn, [1] L=sigmoidy=11+e−y. [ 2] *In formula* [1], c0,c1,…,cn are the parameters of the polynomial fitting curve, x1,x2,…,xn represent the n-dimensional input eigenvectors, and y represents the output value of the fitting equation. The output value L of the sigmoid function is 1 when y is greater than 0; otherwise, it is 0.
## 2.2 Support vector machine (SVM)
When dealing with classification problems, SVM maps samples to higher dimensions if they are indistinguishable in the current dimension, so that the samples are linearly separable in the higher dimensional space. Also, a segmentation hyperplane is constructed in the samples in the high-dimensional space to maximize the distance between the sample points and the hyperplane for the purpose of classification. Because of its good learning ability, the SVM algorithm is widely used in clinical disease diagnosis, and the algorithm has strong processing performance in the face of complex clinical medical data (Zhu et al., 2013; Recenti et al., 2019; Reynolds et al., 2019; Chen and Lin, 2020).
## 2.3 Random forest (RF)
Random forest (Zhao et al., 2020) is itself a swarm policy algorithm. It constructs optimal decision trees by releasably drawing n samples at random from the sample and constructing the optimal decision tree for each drawn dataset. Many optimal decision trees are combined to form a random forest. Due to the relatively stable performance of the models constructed by the RF algorithm, many researchers often apply such algorithm to disease analysis (Asadi et al., 2021; Quist et al., 2021).
## 3.1 Datasets
The data of this study were derived from the diagnostic data on children with incarcerated inguinal hernia in Jiangxi Children’s Hospital. The study was approved by the Ethics Committee of Jiangxi Children’s Hospital with ethics approval number JXSETYY-YXKY-20210016. Because the subjects were all under 18 years of age, informed consent from their guardian or legal close relatives was obtained. In order to protect the patients’ private information, we used digital codes to replace the names and other private information.
We selected 3,807 children with incarcerated inguinal hernia but no intestinal necrosis as the positive sample set, denoted as S1, and 170 children with incarcerated inguinal hernia caused by intestinal necrosis as the negative sample set, denoted as S2. The clinical parameters used in this study are blood routine examination parameters and liver and kidney function examination parameters, and the patient discharge diagnosis results are the basis for the category label of the study.
However, it is quite often that the dimensions of the examination parameters are inconsistent in the diagnostic data, which may be due to the lack of certain examination items in the hospital. In other words, some children only have a single test item such as that of blood or liver and kidney function. Of course, there are also patients who have multiple test items at the same time. Therefore, based on the characteristics of the clinical data on children with incarcerated inguinal hernia, the blood routine single clinical examination data, the single clinical examination data on liver and kidney function, and the combination of these two examination parameters served for modeling and analysis in this work.
## 3.2.1 Statistical magnitude
The original data have been analyzed with statistical theory. Based on the number of features and samples, some clinical examination parameters with low sample size were excluded. The clinical examination parameters after preliminary screening are shown in Table 2
**TABLE 2**
| Inspection method | Parameter name |
| --- | --- |
| Blood routine examination (f1–f20) | Basophils, basophil ratio, eosinophils, eosinophil ratio, hematocrit, hemoglobin, large platelet ratio, lymphocyte count, lymphocyte ratio, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, mean platelet volume, monocyte count, monocyte ratio, neutrophil count, neutrophil ratio, platelet, platelet crit, and platelet distribution width |
| Liver and kidney function (f21–f50) | Alanine aminotransferase, albumin, albumin to globulin ratio, alkaline phosphatase, aspartate aminotransferase, calcium, chloride, creatine isoenzyme, creatine kinase, C-reactive protein, creatinine, direct bilirubin, globulin, glutamyl transpeptidase, indirect bilirubin, lactate dehydrogenase, myoglobin, potassium, prealbumin, retinol-binding protein, sodium, total bilirubin, troponin I, total protein, transaminase ratio, urea, uric acid, urea: creatinine, 5′-nucleotidase, and β2 microglobulin |
| Others (f0) | Age |
Unfortunately, there were still some null values in the parameters of a certain examination because the patient did not undergo a certain examination or there are some deviations in the information input or information collection. For those samples with missing values, the usual processing method used is deletion or interpolation. Because there is an imbalance between positive and negative samples, it may be more severely imbalanced when some samples with null or missing values of the minority are deleted. Thus, in this work, the selection of samples depends on the missing rate of the inspection parameters in the samples. The selected sample should be the positive sample without any missing value or the negative sample with less than $40\%$ of the whole features missing. In this way, some sparse feature samples can be eliminated, and the integrity of negative sample information can be preserved to a large extent to avoid further expansion of sample imbalance.
## 3.2.2 Data interpolation
For those samples with missing values, the commonly used processing methods are interpolation (Liu et al., 1997; Rababah Msc et al., 2019), mean, mode, and median, and nearest neighbor imputation (Beretta and Santaniello, 2016). Although nearest neighbor imputation has good performance in image processing, there are some limitations to this experiment because of the number of samples. If the mode interpolation is used, because the number of negative samples is only 170, the mode of some selected features may not be representative. Second, because the clinical examination parameters are discrete, some features may have multiple modes.
Therefore, choosing an appropriate mode is also a difficult task. In this study, we combined the actual situation of the data, in order to make the interpolation closer to reality, comprehensively considered the different characteristics of the mode, mean, and median, and adopted a new interpolation method for these vacant data. This is the regional random interpolation (RIN-3M) method of the median, mode, and mean. The principle of this method is shown in formula [3]. Ii=maxmi,ni,pi−minmi,ni,pi*rv+minmi,ni,pi. [ 3] *In formula* [3], *Ii is* the value which needs to be inserted for the ith feature; mi, ni, and pi are the mode, mean, and median of the ith feature, respectively; and rv is a random number between 0 and 1.
## 3.3 Feature selection
Random forests have relatively stable performance at the time of dealing with heterogenetic parameters because the constructed decision trees could randomly extract some feature values and avoid the influence of too many redundant features in the process of training models (Onan et al., 2016a; Onam and Serdar, 2016; Onan, 2016; Onan and Korukoğlu, 2016; Onan, 2019; Toolu and Onan, 2021). Thus, the random forest algorithm classifier was selected as a sub-model. To analyze the contribution of the involved parameters to different models, the importance of features was evaluated by using the feature_importance method based on the Gini coefficient theory in the sklearn library of Python. The importance score of each feature was obtained by calculating the sum of the degree of impurity reduction of each feature, and then the importance of the parameter depends on the importance of the sub-model. The average of five-fold cross-validation test results served as the importance score of the model. The histograms in Figures 2–4 show the importance of parameters in the M1, M2, and M3 models.
**FIGURE 2:** *Histogram showing the importance of parameters in model M1.*
Figure 2 shows that the age marked as f0 is the most important feature. In Figures 3, 4, the C-reactive protein labeled f22 is the most important parameter. Since the level of C-reactive protein reflects the degree of infection, it is an important indicator of certain diseases. In fact, the feature importance histograms of the models M2 and M3 also fully indicated that the feature importance of C-reactive protein is relatively high, so C-reactive protein can be used as a reference for the diagnosis of inguinal hernia. Second, the feature importance of direct bilirubin, albumin, and troponin I in the second gradient was high, as shown in Figure 3, which are labeled as f35, f34, and f41, respectively. The feature importance of troponin I and direct bilirubin in the second gradient was also high, as shown in Figure 4; it can be seen that these two examination parameters can also provide doctors with reference values.
**FIGURE 3:** *Histogram showing the importance of parameters in model M2.* **FIGURE 4:** *Histogram showing the importance of parameters in model M3.*
In order to better compare the features that have significant contributions to these three models, Table 3 lists the top 10 important parameter names in the M1 and M2 models, the top 15 important parameter names in the M3 model, and their importance score. The feature intersection between M1 and M3 in Table 3 and the feature intersection between M2 and M3 are analyzed in the Discussion sub-section.
**TABLE 3**
| M1 | M1.1 | M2 | M2.1 | M3 | M3.1 |
| --- | --- | --- | --- | --- | --- |
| Feature | Importance | Feature | Importance | Feature | Importance |
| Age | 0.08476063 | C-reactive protein | 0.19369296 | C-reactive protein | 0.13330727 |
| Mean corpuscular volume | 0.07512136 | Direct bilirubin | 0.06635172 | Troponin I | 0.06787233 |
| Hematocrit | 0.07433977 | Albumin | 0.06205365 | Direct bilirubin | 0.04879934 |
| Monocyte count | 0.06902576 | Troponin I | 0.05554238 | Retinol-binding protein | 0.03912257 |
| Monocyte ratio | 0.06892273 | Prealbumin | 0.04254516 | Basophil ratio | 0.03847966 |
| Lymphocyte count | 0.06348745 | Albumin to globulin ratio | 0.0390976 | Total bilirubin | 0.03825586 |
| Lymphocyte ratio | 0.05230277 | Total bilirubin | 0.03887077 | Albumin | 0.03810347 |
| Basophil ratio | 0.04958729 | Retinol binding protein | 0.03805697 | Calcium | F |
| Basophils | 0.04446896 | Creatine isoenzyme | 0.03481849 | Monocyte count | 0.03749601 |
| Neutrophil ratio | 0.04412248 | Creatine kinase | 0.034006 | Prealbumin | 0.02736976 |
| | | | | Indirect bilirubin | 0.0244206 |
| | | | | Basophils | 0.02433038 |
| | | | | Neutrophil ratio | 0.0238246 |
| | | | | Mean corpuscular volume | 0.0209632 |
| | | | | Mean hemoglobin | 0.01883659 |
## 3.4 Classification model design and training
To solve the problem of imbalanced forecasting and improve the prediction accuracy, some researchers have recently tried to use the combination of up-sampling and down-sampling methods (Rubin et al., 2009). In the procedure of balancing the training datasets, some synthetically generated data points are injected into the minority class dataset in the up-sampling method, while the down-sampling method would train on a disproportionately low sub-set of the majority class examples by adding a weight to the down-sampled class. Both methods have advantages and disadvantages. This study uses a voting-based integrated learning method to solve data imbalance. The model is trained by dividing most samples into multiple sub-sets to improve the training effect of the model.
In this study, $20\%$ of the positive and negative samples are selected as test sets, and the remaining $80\%$ served as the training sets. Subsequently, the positive sample set with majority categories was divided into multiple sub-sets, and the numbers of samples in all sub-sets are almost equal to the number of negative samples. Every dataset of the sub-sets and the negative set form a sub-balance training dataset, denoted as Smaji∪Smin, for training the ith sub-model. Then, the ensemble classifier (Onan et al., 2016b; Onan et al., 2017; Onan and Mansur, 2020) is formed by fusing the sub-models trained by the sub-balance dataset, and the final prediction results would serve for doctors’ decision-making.
As shown in Table 2, the parameters of this research issue comprise blood routine examination and liver and kidney function; the classification models should be trained according to the parameters. Actually, the model should be separately trained with the blood routine examination parameters and the liver and kidney function parameters because the dimensionality of the data is very heterogenetic and it is not appropriate or practical for diagnosis. The inspection is often based on the process. Some basic examinations should be performed on the patient, and a more in-depth examination will be performed when a doctor makes a diagnosis. As basic checks, some features of routine blood tests, for example, white blood cell count and red blood cell ratio, are usually used as diagnostic reference indicators for doctors. However, liver and kidney function tests are relatively stricter than routine blood tests. The liver and kidney function tests are different from the routine blood test because the patient’s venous blood needs to be taken for testing and the patient also needs to fast before the blood is drawn. However, compared with B-ultrasound and other medical imaging diagnostic methods, the results of blood routine and liver and kidney function tests are superior to the former in terms of economy and operation process. Then, a specific model based on parameters would reduce the medical resources and patients’ financial expenditure. Moreover, in many hospitals, the parameters of routine blood tests and liver and kidney function tests are easily available since the tests are cheap and convenient.
To make the auxiliary diagnosis models in line with the actual situation of the diagnosis process and simplify the complexity, the proposed models are trained with different inhomogeneous features. Based on the aforementioned data explanation, the blood routine parameters are used to train the first kind of model named M1, the liver and kidney function parameters served for training the second model denoted as M2, and all of the parameters for the third model marked as M3. Table 1 shows the profiles of the datasets.
## 4.1 Performance evaluations
Accuracy (ACC), sensitivity (SN), specificity (SP), and area under the curve (AUC) (Wang et al., 2019) are often used to judge the quality of the proposed models. The accuracy rate represents the proportion of the sample that can be accurately predicted in the overall test sample. The larger the value of ACC, the higher the accuracy of the model’s prediction of the sample. However, it usually reflects only the overall situation of the sample. When evaluating models for imbalanced datasets, the ACC value can obscure some truth. In other words, the model’s prediction accuracy for samples from most categories may neutralize the low prediction accuracy of samples from a few categories. Therefore, we need to use other parameters for further analysis. Sensitivity represents the proportion of samples that are correctly predicted in positive samples, and specificity represents the proportion of samples that are correctly predicted in negative samples. The specificity and sensitivity reflect the actual predictions of the model for positive and negative samples. These two values will not change much due to the imbalance of the sample. Therefore, accuracy combined with sensitivity and specificity can objectively reflect the prediction of the model. The AUC value is based on the area enclosed by the receiver operating characteristic (ROC) curve and the coordinate axis. The ROC curve takes the sensitivity of the model as the ordinate and 1 minus the specificity as the abscissa. According to different classification thresholds, the relationship between sensitivity and specificity can be accurately analyzed. The AUC value is between 0 and 1. The larger the AUC value, the better the performance of the model.
## 4.2 Results
In this study, aiming to resolve the imbalance of children’s inguinal hernia text data, an integrated learning method based on the voting mechanism is used to reduce the impact of data imbalance. According to the characteristics of the sample data, blood routine and liver and kidney functions were used to establish different comprehensive classifying models. In this experiment, five-fold cross-validation (Mou et al., 2014) is used to further analyze the stability of the model. The ensemble of SVM and LR algorithms was compared with the current auxiliary diagnosis system. Table 4 lists the performance of different algorithms. M1 indicates that the model is constructed only from blood routine parameters, M2 indicates that the model is constructed only from liver and kidney function parameters, and M3 indicates that the model is constructed from all of the aforementioned parameters.
**TABLE 4**
| Feature set | Algorithm | ACC (%) | SN (%) | SP (%) | AUC |
| --- | --- | --- | --- | --- | --- |
| M1 | RF | 72.28 | 71.93 | 79.3 | 0.72 |
| M1 | SVM | 88.22 | 91.9 | 15.67 | 0.53 |
| M1 | LR | 74.28 | 74.44 | 71.27 | 0.71 |
| M2 | RF | 86.15 | 85.93 | 88.73 | 0.87 |
| M2 | SVM | 86.46 | 87.01 | 79.94 | 0.83 |
| M2 | LR | 86.51 | 86.67 | 84.59 | 0.84 |
| M3 | RF | 82.47 | 79.35 | 98.14 | 0.89 |
| M3 | SVM | 77.87 | 77.5 | 79.72 | 0.76 |
| M3 | LR | 78.59 | 76.04 | 91.42 | 0.83 |
To optimize the performance of the RF algorithm trained with the M3 parameter, we further analyzed feature importance ranking, as shown in Table 3. Two items of research have been performed for the in-depth study. The first one is to screen out the intersection of the first 15 features of M3 and the first 10 features of M1 and M2 and then find the union of these two intersections. The union of these 11 features was denoted as FI and applied to train the enhanced model. The other one is to select the best feature combination from the aforementioned 15 features of M3. Since RF does not have a clear threshold for the feature importance, we continuously adjust the number of features in this experiment to achieve the goal of optimization, and the best combination feature set was denoted as FC_15. Figures 5, 6 show the performance of different feature sets, and the detailed results are listed in Table 5.
**FIGURE 5:** *Trend of model performance under different quantitative characteristics.* **FIGURE 6:** *AUC values of different quantitative characteristics.* TABLE_PLACEHOLDER:TABLE 5
## 4.3 Discussion
Table 4shows that the performances of the three models using the M1 parameters alone are not good, and the SVM algorithm even obtained a low specificity value ($15.67\%$). The accuracies of models trained with M2 are all over $86\%$, and their sensitivity and specificity are prospective. As far as the overall performance is concerned, the performance of the RF algorithm under the M2 parameter is better than those of the models with M1 or M2 parameters. The values of ACC, SN, and SP of RF trained with M2 are $86.17\%$, $85.93\%$, and $88.73\%$, respectively. The reason why the performance of the models trained with M1 is inferior to that of the models with M2 may be that it is not effective enough for mining the information of children with intestinal necrosis from the blood routine features. The performance of the models with M3 is lower than that with M2, as shown in Table 4, and there are many interfering features which affect the involved models.
Figures 5, 6 show that when the feature number is 5, the performance of the model is better than the model under the M3 parameter. At this time, the five feature parameters are C-reactive protein, calcium, direct bilirubin, average hemoglobin, and the ratio of basophils. The AUC values of the model trained with M2 and M3 parameters are all larger than 0.87 (see Table 3), and the AUC value of the filtered characteristic FC_15 model can reach 0.91. Therefore, the author believes that the model constructed using medical text data can be used for a doctor’s auxiliary diagnosis. This work proved that the performance of a model can be further improved by selecting proper features with good priority.
To calculate the performance of the different models, the parameters used in this study can be summarized as follows: the random forest algorithm has n_estimators parameter of 200, criterion parameter of gini, min_samples_split parameter of 2, min_samples_leaf parameter of 1, and max_features parameter of “auto.” The support vector machine algorithm has C parameter of 1 and gamma of “scale.” The parameter in the logistic regression algorithm is 1e-4, the C parameter is 1, and the max_iter parameter is 100. All of these have been added in the MS.
## 5 Conclusion
The purpose of this study is to find the relationship between patients with intestinal necrosis and patients with inguinal hernia through blood routine and liver and kidney function test parameters, so as to provide auxiliary recommendations for children’s next treatment. Some constructive models were established on the heterogenetic feature sets and offer helpful answers to doctors’ diagnosis. Furthermore, our work highlighted many patient features that are predictive for making a diagnosis on the relevant diseases. For example, C-reactive protein parameters, troponin I, albumin, and total bilirubin are remarkably important for the issue. The vital sign parameters and image-type medical data would be helpful for the improved models.
Actually, routine blood tests and liver and kidney function tests are often overlooked by researchers because of their basic and common data. This study was conducted to explore the potential association of these basic clinical data with inguinal hernia disease and to construct a model to assist physicians in decision-making. Due to the imbalance of the clinical data and the sparsity of the features, the current study only attempts to use some conventional algorithms to train the samples for analysis. Therefore, this study is more of a trial, guided experiment. In the future, the authors aim to introduce more medical data and intelligent assistance.
However, there are a couple of limitations to this study. On the one hand, the obtained clinical data such as blood routine data and liver and kidney function test parameters are easily affected by the data collection process. If the clinical data parameters used in each study cannot be unified, some important parameters may be missed, which will increase some uncertain risks. On the other hand, due to the influence of research methods, clinical data are different from the characteristic values of some other sample data, and the normal value is usually given in a certain interval range. If one pays too much attention to the numerical weight of the parameters, the generalization ability of the model may be limited. Therefore, based on these limitations, in the future, the author will strictly use data standards and convert some parameters into codes by encoding, thereby weakening the individuality of parameter values.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statements
The studies involving human participants were reviewed and approved by the Ethics Committee of Jiangxi Children’s Hospital (approval no. JXSETYY-YXKY-20210016). Written informed consent from the patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
S-HZ and W-RQ devised and designed the experiments. GC and C-FD were in charge of feature extraction, model construction, model training, and model evaluation. Z-WL and GC analyzed the data and carried out the evaluation. Z-WL wrote the manuscript. S-HZ supervised the project and revised the manuscript. The figures were created by GC. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: The effect of high oral loading dose of cholecalciferol in non-alcoholic fatty
liver disease patients. A randomized placebo controlled trial
authors:
- Amal Ahmed Mohamed
- Ahmed Abdel Halim
- Sahar Mohamed
- Seham Mohamed Mahmoud
- Eman Mohamed Bahgat Eldemiry
- Rasha Sobh Mohamed
- Mahmoud Maamoun Shaheen
- Gina G. Naguib
- Nashwa M. Muharram
- Mona G. Khalil
- Salma Saed
- Randa Ibrahim
- Ahmed Salah Seif
- Noha Kamal
- Karima Nasraldin
- Ali Elsaid Abdelrahman
- Radwa El Borolossy
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10043211
doi: 10.3389/fphar.2023.1149967
license: CC BY 4.0
---
# The effect of high oral loading dose of cholecalciferol in non-alcoholic fatty liver disease patients. A randomized placebo controlled trial
## Abstract
Background and Aim: Non-alcoholic fatty liver (NAFLD) is one of the most common progressive metabolic disorders worldwide. There are increasing scientific interests nowadays for the association between vitamin D status and Non-alcoholic fatty liver. Earlier studies have revealed that vitamin D deficiency is highly prevalent in Non-alcoholic fatty liver patients that contributes to poor outcomes. Hence, the present study aimed to assess the efficacy and safety of oral cholecalciferol on Non-alcoholic fatty liver patients.
Subjects and Methods: This study was conducted on 140 patients that were randomized either to group 1 that received the standard conventional therapy in addition to placebo or group 2 that received the standard conventional therapy in addition to cholecalciferol during the 4 months study period.
Results: At the end of the study group 2 revealed significant decrease ($p \leq 0.05$) in the mean serum level of TG, LDL-C, TC, hsCRP as compared to their baseline results and group 1 results. Additionally, a significant improvement in the serum levels of ALT ($$p \leq 0.001$$) was seen in group 2 at the end of the study when compared to group 1. Whereas group 1 did not show any change in these parameters when compared to group 2 and their baseline results.
Conclusion: Cholecalciferol was shown to have beneficial effects on serum ALT levels, hsCRP levels and lipid profile of NAFLD patients.
Clinical Trial Registration: https://prsinfo.clinicaltrials.gov/prs-users-guide.html, identifier NCT05613192
## 1 Introduction
Non-alcoholic fatty liver disease (NAFLD) is a metabolic disorder with high prevalence in patients suffering from chronic liver diseases (Chalasani et al., 2012). NAFLD is defined as the presence of more than $5\%$ of fat deposits in the hepatocytes (hepatic steatosis) with no known other reasons of steatosis as high alcohol intake (Benedict and Zuhang, 2017).
The global prevalence of NAFLD differ according to the population reaching $13\%$ in Africa, $32\%$ in middle East and $30\%$ in the United States (Ofosu et al., 2018). More than $70\%$ of patients with metabolic syndrome suffer from NAFLD due to the excessive fat accumulation in this syndrome (Tolman et al., 2007). This disease usually begins as hepatic steatosis that may progress to steatohepatitis (NASH) with hepatic cells inflammation and may finally end with Chronic liver disease with fibrosis and cirrhosis (Agrawal and Duseja, 2014).
The main aetiology of NAFLD is associated with interaction between different factors as: environmental, genetic, hormonal and nutritional factors. Obesity and metabolic syndrome (MS) are considered the most common risk factors for NAFLD initiation, also they are linked to greater progression of the disease (Younossi et al., 2018). As visceral obesity constitutes a major health problem, it is now important for hepatologists to weigh risk factors that lead to insulin resistance and hepatic steatosis (Finelli and Tarantino, 2012). Visceral obesity and its adipose-tissue-resident macrophages produce many inflammatory cytokines that induce insulin resistance and play a great role in hepatic steatosis and fibrosis pathogenesis (Larter and Farrell, 2006).
Till now, the standard treatment for NAFLD is weigh reduction with life style modification (European Association for the Study of the Liver EASLEuropean Association for the Study of Diabetes EASDEuropean Association for the Study of Obesity EASO, 2016; Liyanagedera et al., 2017). However, there is no pharmacological management has been approved yet by guidelines. Accordingly, several therapies with different modes of action for treatment of NAFLD are gaining significant interests and are currently under clinical evaluation (Del Ben et al., 2014). Various pharmacological approaches using existing drugs have also been considered in the management of NAFLD and NASH. These attempts mainly focus on antidiabetics, anti-obesity drugs, antioxidants, and cytoprotective agents, including insulin sensitizers (e.g., metformin), thiazolidinediones (e.g., pioglitazone), glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., liraglutide), a natural dihydroxy bile acid (e.g., ursodeoxycholic acid) or antioxidants (vitamin E) (Harrison et al., 2020; Negi et al., 2022).
One of these interesting therapies is cholecalciferol (native vitamin D) which is a fat-soluble vitamin that is endogenously produced in the skin, it exerts many beneficial effects other than its primary role in bone homeostasis (Barchetta et al., 2017). Vitamin D has been demonstrated by many animal and clinical studies to induce anti-inflammatory and anti-fibro genic activity in the liver through inhibiting proinflammatory cytokines, profibrotic mediators and oxidative stress (Abramovitch et al., 2011; Ding et al., 2013; Abramovitch et al., 2015; Beilfuss et al., 2015). Moreover, Vitamin D has been shown in several experimental studies to be an effective modulator of insulin sensitivity and metabolism of free fatty acids (FFAs). Hence, vitamin D deficiency (VDD) increase the percentage of FFAs circulating in the blood stream which promote fat deposition into the hepatocytes causing NAFLD (Barchetta et al., 2011).
Several clinical studies have proved the association between low vitamin D levels and NAFLD, also it correlates with the spectrum of inflammation and fibrosis that occur in the course of NAFLD (Targher et al., 2007; Manco et al., 2010; Nobili et al., 2014; Zhai et al., 2016).
VDD is defined as a serum 25-hydroxyvitamin D levels ≤20 ng/mL and it is very common in adults over 20 years. It can be attributed to many factors as: poor sunlight exposure, insufficient intake of food containing vitamin and malabsorption syndromes (Matthias and Micheal, 2013).
Patients with NAFLD have $26\%$ additional risk to VDD as compared to controls owing to the impairment of 25 (OH)D synthesis due to the presence of steatosis (Eliades et al., 2013), in addition vitamin D receptor (VDR) expression in the hepatocytes decreases as the extent of the disease increase (Barchetta et al., 2012).
Sunlight therapy and vitamin D have shown clinical benefit in experimental animal models with fatty liver. Hence vitamin D supplementation can represent a simple and cheap therapy for the management of NAFLD (Geier, 2011).
In 2016, the first pilot prospective clinical trial was conducted to assess the effect of 24 weeks high-dose (25,000 IU/Week) oral cholecalciferol supplementation on the liver histological findings of 12 non-cirrhotic NASH patients, no beneficial effects of this treatment were found on the laboratory parameters of hepatic damage and insulin sensitivity (Kitson et al., 2016). After this study, other clinical trials were conducted evaluating the effect of oral Vitamin D with different dosing regimens in NAFLD, however results from these clinical studies are debatable (Sharifi et al., 2014; Barchetta et al., 2016; Lorvand Amiri et al., 2017).
Hence, our study aimed to determine the effect and safety of high oral loading dose of cholecalciferol supplementation on the clinical parameters related to liver steatosis, glycaemic control, insulin resistance and metabolic profile in NAFLD patients. According to our knowledge this is the first randomized placebo-controlled trial to investigate the impact of high oral loading dose of vitamin D in NAFLD patients.
## 2.1 Study design
The present study was prospective, simply randomized (via computer generated sequence), placebo controlled double blinded study (patients, physicians, radiologist remained blinded from randomization) conducted on NAFLD patients in the outpatient liver clinics of the National Hepatology and Tropical Medicine Research institute, Cairo, Egypt. From March 2022 to August 2022.
## 2.2 Study population
Patients had to fulfil the following inclusion criteria to be included in the study: either male or female adult patients (>19 years) with fatty liver diagnosis by using upper abdominal ultrasound echography (US) and with T2D diagnosed according to ADA 2022 criteria (American Diabetes Association, 2022) and treated with metformin.
The main exclusion criteria from the study were as follows: Pregnant and/or lactating women, excessive alcohol use (as defined by an average daily consumption of alcohol >30 g/day in men and >20 g/day women), patients with other causes of chronic liver disease as viral hepatitis, drug induced hepatitis, autoimmune hepatitis, patients suffering of chronic kidney disease, hyper/hypoparathyroidism, hypersensitivity to cholecalciferol, hypercalcemia, patients taking supplementation with vitamin D, calcium and medications affecting calcium/vitamin D metabolism (as: anticonvulsants, glucocorticoids, antacids).
## 2.3 Study intervention
Hundred and forty eligible patients were included into the study and randomized by simple randomization into either of the two groups (Figure 1):
**FIGURE 1:** *CONSORT flow diagram of patient allocation.*
Group 1: 70 patients received the standard conventional therapy in addition to placebo for 4 months.
Group 2: 70 patients received the standard conventional therapy in addition to a single oral dose of cholecalciferol 200,000 IU (Devarol- S®, manufactured by Memphis company, Egypt) followed by oral cholecalciferol 800 IU (Vidrop®, manufactured by Medical Union Pharmaceuticals company, Egypt) daily for 4 months.
The standard conventional therapy in both groups included regular exercise in the form of any physical activity as: walking, cycling, etc., For 30–45 min at least 5 days per week in addition to calorie restriction in overweight and obese patients (1,000–1,200 kcal/day for women, 1,200–1,500 kcal/day for men).
Study medications were given to the patients by an unblinded pharmacist to ensure the right treatment assignment, however this pharmacist was not included in the outcome assessment.
All patients were diagnosed with NAFLD depending on abdominal ultrasonography performed by a radiologist where the liver brightness and liver parenchyma with diffuse echogenicity in confirm the diagnosis.
## 2.4 Study procedures
The following information was collected from the patients including: age, sex, history of smoking or alcohol use, medications use, Sun exposure and vitamin D containing food consumption.
The grade of fatty liver (Kim et al., 2019) was classified as none [0], mild [1], moderate [2], or severe [3] according to the findings of liver brightness, hepatorenal echo contrast, deep attenuation of the ultrasound signal, and the blurring of vessels. The grading was recorded two times; the first at the beginning of the study and the second time after 4 months of cholecalciferol administration.
## 2.5 Laboratory measurements and clinical assessments
All patients were subjected to anthropometric measurements at baseline and at the end of the study including: height (meters), weight (kilograms), waist circumference (WC) (measured midway between the 12th rib and the iliac crest in inches). Body mass index (BMI) (measured as weight in kg divided by height in m2). According to WHO criteria: overweight is defined as BMI ≥25 kg/m2, obesity is defined as BMI ≥30 kg/m2 (Garvey et al., 2016).
Moreover, 10 mL Blood samples were collected from all patients at the beginning and at the end of the study after an overnight fasting, then blood samples were centrifuged at 3,000 rpm for sera separation for 10 min, and then sera were kept frozen at −80°C for analysis.
The following laboratory tests were measureda- Glycaemic control: Fasting blood glucose (FBG mg/dl), glycated haemoglobin (HbA1C%), Fasting insulin (mU/L), Insulin resistance index calculated by the homeostasis model assessment insulin resistance (HOMA-IR) method using the product of fasting insulin and fasting plasma glucose divided by 405. The cut off value of HOMA-IR is more than 1.64.b- Liver function tests: Alanine transaminase (ALT U/L), Aspartate transaminase (AST U/L), Albumin (g/dL), Gamma glutamyl transferase (GGT U/L), Alkaline phosphatase (ALP U/L).c- Lipid Profile: Low density lipoprotein (LDL-C mg/dl), High density lipoprotein (HDL-C mg/dl), Triglycerides (TG mg/dL), Total cholesterol (TC mg/dL).d- Other markers: High sensitivity C reactive protein (hsCRP mg/dl), Alfa fetoprotein (AFP ng/ml), serum 25-hydroxy vitamin D (25(OH) D ng/ml).
Analysis of FBG, ALT, AST, GGT, ALP, HDL-C, TG, TC was performed by enzymatic colorimetric methods, while analysis of serum 25(OH) D, fasting insulin, hsCRP and AFP was performed by enzyme linked immunosorbent assay technique (ELISA) (EIA-5240; DRG International Inc., Springfield., United States). Patients were considered to be vitamin D deficient when the level of 25(OH)D is less than 20 ng/mL, insufficient when the level is less than 21–29 ng/mL and when the level is 30 ng/mL or more, patients were considered to be sufficient or normal.
All Patients were followed up every 2 weeks by the clinical pharmacist in charge through patient encounter to ensure the compliance to the treatment regimen and to assess any adverse side effects.
## 2.6.1 Primary outcomes
The improvement in the glycaemic control parameters, liver function tests, lipid profile and serum 25-hydroxy vitamin D of the patients at the end of the study
## 2.6.2 Secondary outcomes
The Decrease in degree of steatosis on US with the improvement in CRP, AFP at the end of the 4 months.
## 2.7 Ethical consideration
The study protocol was revised and approved for the scientific and ethical issues by the institution review board of the ethical committee of National Hepatology and Tropical Medicine Research institute, Cairo, Egypt (serial number:10–22). The study was registered in Clinical trial. Gov (Identifier: NCT05613192). The study procedures were carried out in accordance with Good Clinical Practice guidelines, and the ethical principals in 2013 Helsinki Declaration. This study also applied CONSORT guidelines and ICMJE recommendations. All patients included in the study were informed and educated about the study protocol before their participation and requested to sign a written informed consent without any obligation to withdraw if they want to.
## 2.8 Statistical analysis
Sample size calculation was done depending on data from a previous study (Barchetta et al., 2016) by considering serum LDL-C as a key dependent variable, type I error of 0.05, and study power of $90\%$. Based on the suggested formula for parallel clinical trials, we reached the sample size of 50 patients in each group. Taking into account a possible drop-out rate of $30\%$, 70 patients will be enrolled in each group.
Statistical analysis was performed using the SPSS statistical program (v.22; SPSS, Chicago, IL). Mean and standard deviation (SD) were used to express the parametric data, and the categorical data were expressed as numbers and percentage. Data analysis were performed by the Paired Student’s t-test, Unpaired Student’s t-test and Chi-square test. The probability of error of 0.05 was considered to be significant, and 0.001 to be highly significant.
## 3.1 Patients characteristics
All the 140 patients who started the study, completed till the end and there were no dropouts (Figure 1). At baseline no significant differences were found between both groups regarding the demographic data, anthropometric measures, laboratory measurements and degree of liver steatosis on US (Table 1; Figure 2). Totally 112 out of 140 patients had mean serum 25(OH) D < 30 ng/mL, in group 1 30 ($43\%$) patients were vitamin D deficient and 25 ($36\%$) were vitamin D insufficient while in group 2 40 ($57\%$) patients were vitamin D deficient and 17 ($24\%$) were vitamin D insufficient. Concerning vitamin D containing food consumption (fish beef liver. etc.), all patients in both groups stated that they have little amount due to financial burden.
## 3.2 Laboratory measurements and clinical assessments
After 4 months of cholecalciferol supplementation, group 2 revealed significant decline in the mean serum level of TG (152 ± 25 vs. 175 ± 34), LDL-C (135 ± 15 vs. 155 ± 65), TC (150 ± 17.2 vs. 175 ± 20.1) as compared to their baseline results and group 1 results (Table 2). In addition, hsCRP mean serum level showed significant decrease in group 2 compared to the baseline results (7.9 ± 0.899 vs. 11 ± 0.9) and compared to group 1 (Table2). Moreover, a highly significant improvement in the serum levels of ALT (45 ± 5.1 vs. 66 ± 10) was seen in group 2 at the end of the study when compared to group 1 (Table 2). Whereas group 1 did not show any change in these parameters at the end of the study when compared to their baseline results.
**TABLE 2**
| Unnamed: 0 | Group 1 (n = 70) | Group 2 (n = 70) | p-Value |
| --- | --- | --- | --- |
| Weight (kg) | 75 ± 11 | 80 ± 9.5 | 0.078 |
| BMI (kg/m2) | 30.3 ± 4 | 30.8 ± 5.2 | 0.92 |
| WC (inches) | 110 ± 3.9 | 112 ± 4.8 | 0.76 |
| Male | 111 ± 4.1 | 111 ± 3.8 | 0.91 |
| Female | 115 ± 11 | 119 ± 8.9 | 0.092 |
| FBG (mg/dL) | 10 ± 4 | 9 ± 2 | 0.76 |
| Fasting insulin (mU/L) | 2.8 ± 0.3 | 2.6 ± 0.1 | 0.85 |
| HOMA-IR | 180 ± 20.1 | 150 ± 17.2 | 0.03* |
| TC (mg/dL) | 166 ± 24.2 | 152 ± 25 | 0.05* |
| TG (mg/dL) | 149 ± 20 | 135 ± 15 | 0.043* |
| LDL-C (mg/dL) | 43 ± 2.6 | 47 ± 10 | 0.06 |
| HDL-C (mg/dL) | 54 ± 7.9 | 59 ± 8.2 | 0.078 |
| AST (U/L) | 59 ± 6.2 | 45 ± 5.1 | 0.001** |
| ALT (U/L) | 47 ± 4 | 45 ± 3.7 | 0.51 |
| ALP (U/L) | 55 ± 3.9 | 56 ± 2.1 | 0.36 |
| GGT (U/L) | 4 ± 0.6 | 4 ± 0.4 | 0.9 |
| Albumin (g/dL) | 19 ± 2.1 | 25 ± 3.1 | 0.05* |
| 25(OH) D (ng/mL) | 18 ± 2.4 | 24 ± 2.9 | 0.05* |
| Male | 19 ± 1.9 | 25 ± 3.1 | 0.05* |
| Female | 17 ± 1.87 | 24.5 ± 2.6 | 0.05* |
| Age (19–30) | 18 ± 2.1 | 26 ± 2.4 | 0.05* |
| Age (30–60) hsCRP (mg/dL) | 9.9 ± 0.8 | 7.9 ± 0.899 | 0.001* |
| AFP (ng/mL) | 16 ± 2.4 | 14.5 ± 2.2 | 0.09 |
There was no change observed in the auxological parameters, BMI, glycemic control markers, AFP, AST, ALP, GGT in both groups at the end of the study (Table 2), also no significant difference was reported regarding the degree of liver steatosis on US in both groups (Figure 2).
Regarding serum 25(OH) group 2 reported significant increase as compared to their baseline results (25 ± 3.1 vs. 15 ± 1.23) and when compared to group 1 (19 ± 2.1 vs. 17 ± 1.4) after 4 months of cholecalciferol administration. 50 patients in group 2 became Vitamin D sufficient, 20 patients were found to be vitamin D insufficient and no patient was found in vitamin D deficient category at the end of the study. However, there were no change observed in group1 as compared to their baseline values.
Neither of the patients in group 2 reported any side effects after 4 months of daily cholecalciferol administration.
## 4 Discussion
NAFLD is one of the most common and prevalent progressive metabolic disorder worldwide (Charlton et al., 2011). It is manifested in different clinical spectrum that can start with simple fatty liver and ends with cirrhosis (Wong et al., 2015). Meanwhile, there are increasing scientific interests in the association between NAFLD and vitamin D levels (Pacifico et al., 2019). Vitamin D is a fat-soluble vitamin and an important component in many tissues and metabolic process with several functions extending beyond the skeletal homeostasis (Liu et al., 2019). Findings of earlier studies have revealed that vitamin D deficiency is highly prevalent in NAFLD patients and this contributes to poor outcomes and progression to liver cirrhosis (Targher et al., 2007; Manco et al., 2010; Nobili et al., 2014; Zhai et al., 2016). Also, several clinical trials evaluated the impact of cholecalciferol in NAFLD patients but till now there is no clear evidence on its beneficial effect in NAFLD patients (Sharifi et al., 2014; Barchetta et al., 2016; Kitson et al., 2016; Lorvand Amiri et al., 2017). Different vitamin D dosing regimens were investigated in these trials but none of these trials evaluated the impact of high oral loading dose followed by daily dose supplementation although the high oral vitamin D dosing was demonstrated to be superior to the daily regimen in treating hypovitaminosis in patients with different inflammatory diseases (Sainaghi et al., 2013; Wong et al., 2015; Mak et al., 2016). Hence our aim in the current study was to evaluate the impact of high oral loading dose vitamin D supplementation in NAFLD patients. The present study showed that vitamin D dosing significantly ($p \leq 0.05$) decreased serum ALT at the end of the study but there were no changes seen in other liver enzymes biomarkers and this was also shown in Sakpal et al. [ 2017] were the serum level of ALT decreased ($p \leq 0.001$) from 87 ± 48 to 59 ± 32 IU/mL after 6 months of vitamin D supplementation and in Lorvand Amiri et al. [ 2017] where ALT significantly decreased at the end of the study, whereas other studies reported no significant effects on ALT (Sharifi et al., 2014; Barchetta et al., 2016; Foroughi et al., 2016; Kitson et al., 2016). The discrepancy between our study results and other studies maybe due to the differences in the study population and the dosing regimen. Moreover, our study showed beneficial effect of vitamin D on the lipid profile including significant reduction in serum TG, LDL-C, TC. In accordance, previously Lorvand Amiri et al. [ 2017] and Sharifi et al. [ 2014] reported the reduced effect of vitamin D on TG, TC, LDL-C serum levels. On the other hand, other studies (Barchetta et al., 2016; Foroughi et al., 2016; Kitson et al., 2016) failed to show significant effect of vitamin D on the lipid profile. In addition, we reported significant decrease in the serum level of hsCRP from 11 ± 0.9 to 7.9 ± 0.89 mg/dL which was also reported in Foroughi et al. [ 2016], Sharifi et al. [ 2014] In contrast Barchetta et al. [ 2016] and Sakpal et al. [ 2017] did not show significant effect on CRP after daily vitamin D. Furthermore, concerning the glycemic index and anthropometric parameters, our trial did not reveal any beneficial effect of vitamin D on all these parameters at the end of the study. Likewise, all previous trials (Sharifi et al., 2014; Barchetta et al., 2016; Foroughi et al., 2016; Kitson et al., 2016; Mak et al., 2016; Lorvand Amiri et al., 2017) did not report any significant changes in the anthropometric measures. Conversely, two older studies (Foroughi et al., 2016; Lorvand Amiri et al., 2017) revealed a significant reduction in FBG, HOMA-IR at the end of vitamin D supplementation duration.
Further, no change in the degree of liver steatosis on US was found in both groups at the end of the study, similarly Barchetta et al. [ 2016] did not report significant difference in the hepatic fat fraction measured by magnetic resonance after 24 weeks of oral high-dose vitamin D supplementation in T2D patients with NAFLD.
Hypovitaminosis D was found in $80\%$ of our NAFLD patients and this was supported by earlier trials (Targher et al., 2007; Manco et al., 2010; Nobili et al., 2014; Zhai et al., 2016) that demonstrated the association between low serum 25(OH) and NAFLD.
There are two limitations in our study; the lack of liver biopsy due to financial constraints so we cannot follow the histological changes in the liver and the short duration of the study. Further studies with longer duration evaluating the histological changes that occur in the liver with vitamin D supplementation are required.
## 5 Conclusion
In conclusion, our randomized placebo-controlled trial demonstrated that hypovitaminosis D is common in NAFLD patient and high oral loading dose followed by daily oral doses of vitamin D had beneficial effects on serum ALT levels, hsCRP levels and lipid profile of NAFLD patients.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by institution review board of the ethical committee of National Hepatology and Tropical Medicine Research institute, Cairo, Egypt (serial number:10–22). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
All authors contributed to the study conception and design. Material presentation, data collection were performed by AM, AH, SAM, SEM, EB, RM, MS, GN, NM, MK, SS, RI, AS, NK, KN, and AA. Data Analysis was performed by RE. The first draft of the manuscript was written by RE and all authors commented on versions of the manuscript. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Identification and validation of IgG N-glycosylation biomarkers of esophageal
carcinoma
authors:
- Huiying Pan
- Zhiyuan Wu
- Haiping Zhang
- Jie Zhang
- Yue Liu
- Zhiwei Li
- Wei Feng
- Guiqi Wang
- Yong Liu
- Deli Zhao
- Zhiyi Zhang
- Yuqin Liu
- Zhe Zhang
- Xiangtong Liu
- Lixin Tao
- Yanxia Luo
- Xiaonan Wang
- Xinghua Yang
- Feng Zhang
- Xia Li
- Xiuhua Guo
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10043232
doi: 10.3389/fimmu.2023.981861
license: CC BY 4.0
---
# Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma
## Abstract
### Introduction
Altered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC.
### Methods
In total, 496 individuals of ESCC ($$n = 114$$), precancerosis ($$n = 187$$) and controls ($$n = 195$$) from the discovery population ($$n = 348$$) and validation population ($$n = 148$$) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score.
### Results
In the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 ($95\%$ CI: 3.03-5.36, $P \leq 0.001$), 0.69 ($95\%$ CI: 0.55-0.87, $P \leq 0.001$), 0.56 ($95\%$ CI: 0.45-0.69, $P \leq 0.001$), 0.52 ($95\%$ CI: 0.41-0.65, $P \leq 0.001$), 7.17 ($95\%$ CI: 4.77-10.79, $P \leq 0.001$), and 2.86 ($95\%$ CI: 2.33-3.53, $P \leq 0.001$), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 ($95\%$ CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 ($95\%$ CI: 0.758-0.864).
### Discussion
Our study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression.
## Introduction
Esophageal cancer (EC) is the seventh most common cancer type worldwide and ranks sixth in the cause of cancer-related death [1]. In China, there have been an amount of estimated 0.25 million new cases of esophageal cancer and 0.19 million related deaths as of 2018, accounting for $43\%$ and $37\%$ of the global morbidity and mortality [2]. The 5-year relative survival rate of the localized esophageal cancer at the point of confirmed diagnosis is $47\%$, while the rate declines to only $20\%$ for all esophageal cancer patients [3]. In addition, esophageal squamous cell carcinoma (ESCC) predominates sub-type of esophageal cancer and is among the most aggressive forms of squamous cell carcinoma. ESCC belongs to the most deadly malignancy with late stage diagnosis, metastasis, therapy resistance and frequent recurrence [4].
Most patients of ESCC lack obvious symptoms at the early stage and progress insidiously to a relatively advanced stage when detected [5]. Therefore, exploring the reliable biomarkers associated with early stage of ESCC is critical for improving the prognosis and life quality of patients, which fits with in the paradigm of predictive medicine. Esophagogastroduodenoscopy (EGD) is the main method for screening EC in the clinical practice, and it is of high cost, discomfortable and invasive. In addition, there are some serum biomarkers recommended for the assistant screening of EC, such as carcinoembryonic antigen (CEA), P53-Ab, Cytokeration fragment antigen21-1 (CYFRA21-1), squamous cell carcinoma antigen (SCC), protein kinase D1 (PRKD1), matrix metalloproteinase 2 (MMP-2), tissue inhibitor of metalloproteinases-2 (TIMP-2) and serum macrophage colony-stimulating factor (M-CSF). However, these tumor markers could alter in various tumor types, and even relate with the acute infection (6–8). Therefore, it is of great significance to identify novel biomarkers of high specificity and sensitivity for the early detection of ESCC, contributing to the early diagnosis and prevention of ESCC.
The glycomics analysis is a promising ‘omics’ technology [9], providing novel biomarkers for diseases diagnosis and prognosis, which could advance the personalized medicine and intervention strategy [10]. Immunoglobulin G (IgG), as the most abundant immunoglobulin in blood, constitutes approximately $75\%$ of the serum immunoglobulin proteins [11]. IgG activates a series of effector pathways, such as complement-dependent cytotoxicity (CDC), antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) [12, 13], which are regulated by the N-linked glycosylation process at the Fc segment of IgG. N-glycosylation is one of the most common post-translational modifications of membrane and secretory proteins, with an important role in the biological processes, such as intercellular recognition, adhesion, communication and mutual interactions [14, 15]. It plays an important role in the antibody functions and almost all the tumor markers approved by FDA are modified through glycosylation [16]. The attached N-glycans on IgG are essential for the proper functional activity of the immune system. IgG N-glycosylation has been reported to be affected by the pathophysiological conditions, and thus associated with various diseases, such as the metabolic diseases (17–22), aging [23, 24], inflammatory and autoimmune diseases [25, 26] The profile of IgG N-glycans could alter its effector functions on tumor cells, and the variability of IgG N-glycosylation has also been identified in some tumor types (27–30).
Our previous study found that the IgG N-glycosylation profiles were independently associated with the esophageal precancerosis for squamous cell carcinoma beyond inflammation [31]. However, the association of IgG N-glycosylation pattern with ESCC remains unknown to date. In this study, we investigated the variation of IgG N-glycans in the stages of normal, precancerosis and early ESCC. We aimed to develop a predictive score using IgG N-glycans data to improve the risk stratification and management of ESCC.
## Study design and population
In total, 516 subjects voluntarily participated in this study and 496 individuals were finally recruited in the analysis according to the inclusion and exclusion criteria as shown in Figure 1. In 2018, 80 cases of early ESCC, 125 cases of precancerosis and 143 controls were enrolled from Feicheng People’s Hospital (Feicheng City, Shandong Province). Meanwhile, data of 34 early ESCC patients, 62 precancerosis patients and 52 controls were collected as validation group from Gansu Wuwei Tumor Hospital (Wuwei City, Gansu Province). This two-center respective case control study umbrellaed under a national screening project, aiming at the early screening and diagnosis of ESCC and other gastrointestinal cancers as described previously [31]. Before the endoscopic screening, the demographic information, dietary habit, lifestyle, history of gastrointestinal disease and family history of gastrointestinal cancer were surveyed through a standardized questionnaire (Supplementary Table S1). The blood samples were collected and stored at -80°C for the subsequent experiment.
**Figure 1:** *A schematic diagram of the study participants.*
The following were the inclusion criteria: [1] providing informed consent prior to enrollment; [2] initial confirmed diagnosis of precancerosis or ESCC, or neither; [3] the required information and data of IgG glycosylation profile eligible. The exclusion criteria were as follows: [1] diagnosis of other gastrointestinal cancer (gastric cancer or intestinal cancer) before or at the screening; [2] history of mental illness, infectious disease, autoimmune diseases or and other malignant cancers; [3] women in pregnancy or lactation; [4] post-operation or post-radiochemotherapy.
The study was approved by the independent ethics committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (grant number: 17–$\frac{124}{1380}$) and ethics committee of Capital Medical University (grant number: Z2019SY012). All participants provided their written informed consents before taking part in this study.
## Outcome definition
The diagnosis of precancerous esophageal lesions and early ESCC was according to the endoscopic screening and biopsy examination, while the judgment of the controls was only based on the endoscopic diagnosis. In a previous article we described the procedures of routine endoscopy examination [31]. The controls in this study were defined as oesophagitis or normal, while esophageal precancerosis were defined as mild or moderate atypical hyperplasia, and the early ESCC included severe atypical hyperplasia, mucosal and submucosal carcinomas.
## Covariates
The body mass index (BMI) was defined as weight (in kilograms)/height2 (in meters squared) and the participants were grouped into <24 kg/m2 and ≥24 kg/m2. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured twice on the right arm using a standard mercury sphygmomanometer in a sitting position after the subjects had rested at least 10 minutes, and the mean value was used for the present analysis. Hypertension was defined as a self-reported history of hypertension, a mean SBP ≥140 mmHg or DBP ≥90 mmHg or taking antihypertensive medications. Education level was classified into illiteracy, primary school, middle or high school, bachelor degree or above. Marriage status was defined as married status or others. Family income was divided into less than and more than 50,000 yuan per capita per year. Smoke was defined as at least one cigarette per day in the past year, while drink was defined as at least 100 ml consumption of alcohol (content ≥$50\%$) per day in the past year. Dietary frequency of pickled food, fried food, hot food and mildew food were grouped into never, seldom and often. History of gastrointestinal disease involved gastroenteritis and peptic ulcer. Family history of gastrointestinal cancer included esophageal cancer, gastric cancer and intestinal cancer.
## IgG N-glycosylation experiment
The glycosylation experiment and analysis involved four key processes: IgG isolation and purification from plasma, glycans enzyme digestion and release, fluorescence labeling and quantitative detection, as described previously [32, 33]. In brief, IgG was isolated in a high-throughput manner, using 96-well protein G monolithic plates (BIA Separations, Slovenia), starting from 100 μl of plasma. Plasma was diluted 7× with phosphate buffered saline (PBS), applied to the protein G plate and washed. IgG was eluted with 1 ml of 0.1 M formic acid and immediately neutralized with 1 M ammonium bicarbonate. Then, the N-linked glycans were released by incubating at 37°C for 18-20 hours with 1.5 units of PNGase F. The released glycans were fluorescent labeled using 2-aminobenzamide at 65°C for 3 hours. After incubation samples were brought to $96\%$ of acetonitrile (ACN) by adding 700 μl of $100\%$ ACN and applied to each well of a 0.2 μm GHP filter plate. Solvent was removed by application of vacuum using a vacuum manifold. Loaded samples were subsequently washed 5× with $96\%$ ACN. Fluorescently labelled N-glycans were separated by hydrophilic interaction chromatography on Acquity UPLC H-Class instrument (Waters, USA). Labelled N-glycans were separated on a Waters BEH Glycan chromatography column at 60°C, with 100 mM ammonium formate, pH 4.4, as solvent A and ACN as solvent B. Separation method used linear gradient of 75–$62\%$ acetonitrile at flow rate of 0.4 ml/min in a 27-min analytical run. Detect N-glycan fluorescence at excitation and emission wave lengths of 330 nm and 420 nm, respectively.
Finally, 24 direct glycan peaks (GPs) were quantitatively expressed with the percentage of the total integrated peak area, as presented in Supplementary Figure S1. In addition, 54 derived traits (IGPs) were derived to reflect the relative abundance of the specific structure, such as galactosylation, sialylation, bisecting N-acetylglucosamine (GlcNAc), core fucosylation and mannose. The amounts of GP and IGP were normalized followed by log transformation and batch-effect was considered and corrected. The detailed structural and biological information of each GP and IGP was shown in Supplementary Table S2.
## Statistical analysis
Continuous variables adhering to the normal distribution were represented as mean and standard deviation (SD), and the differences between groups were tested by the independent ANOVA tests; otherwise, the median and interquartile range (P25, P75) were used, and the differences were explored by Kruskal-Wallis H tests. Categorical variables were presented as n (%), and the differences were tested by the chi-square tests. The box plots were used to show the differences of IgG GPs and IGPs among the controls, precancerosis and early ESCC.
The false discovery rate (FDR) correction was used to primarily identify the substantially increased or decreased IgG glycans and traits associated with ESCC. Then, the candidate glycans and traits selected above were finally confirmed using the stepwise ordinal logistics regression according to Akaike information criterion (AIC), which composed of an ESCC-related glycan score by the regression coefficients. The glycan score and its components were tested both in the discovery and validation population after the confounding covariates adjusted in three models: model 1 was unadjusted; model 2 was adjusted for age and sex; model 3 was further adjusted for BMI, hypertension, smoke, drink, education, income, marriage status and dietary habits. Formula of the ESCC-related glycan score was listed below: Score = ∑(βn × amounts of each IgG GP and IGP n), where β is the ordinal logistics coefficient.
The discriminative capacity of the proposed ESCC-related glycan score was illustrated using multi-class receiver operating characteristic (ROC) curve, and the average area under-the curve (AUC) value was provided. Significant differences in the proposed ESCC-related glycan score between different groups in the discovery and validation populations were subsequently assessed using DeLong’s test. The robustness of the ESCC-related glycan score was assessed using a bootstrap procedure ($k = 100$). The bootstrap method was used to resample distinct data sets 100 times from the original data set, and the number of subjects in each resampled data set was set to be the same number as the sample size of the original data set. SNPs associated with the proposed ESCC-related glycan score were found out by Meta-analysis of the IgG N-glycosylation GWAS and were annotated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis were carried as well as protein–protein interaction (PPI) network analysis to find potential hub genes. Finally, we validated the potential hub genes on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) based on RNA sequence data. Detailed statistical methods are provided in the Supplementary material online. All statistical tests were two-sided at a significant level of 0.05, and the Benjamini-Hochberg method was applied to control the FDR for multiple hypothesis tests [34]. All the analyses presented above were performed using the packages of ‘MASS’, ‘forestplot’, ‘multiROC’ in R software (version 4.0.0).
## Characteristics
In the discovery population, the median (P25, P75) age was 58.50 (54.00, 63.00), and 163 ($46.84\%$) were males. In the validation population, the median (P25, P75) age was 60.00 (56.00, 64.00), and 65 ($43.92\%$) were males. The characteristics were similar between the discovery and validation populations, except age as shown in Supplementary Table S3. There were no significant differences in sex, education level, marriage status, household income, BMI, hypertension, history, family history, dietary habits among the controls, precancerosis and early ESCC groups both in the discovery and validation populations, apart from age, smoking and drinking. The detailed distributions of the characteristics were shown in Table 1. In addition, the dietary habits, including the frequency of having pickled food, fried food, hot food, and mildew food, were similar among the controls, precancerosis and ESCC groups both discovery and validation populations (Table 2).
## Different IgG N-glycosylation patterns in ESCC, esophageal precancerosis, and controls
The detailed distribution of IgG glycans and traits among the controls, precancerous and early ESCC groups were shown in Supplementary Table S4. A total of 7 GPs (GP3, GP6, GP12, GP13, GP17, GP20, GP23) and 11 IGPs (IGP30, IGP36, IGP37, IGP38, IGP46, IGP51, IGP52, IGP57, IGP73, IGP75, IGP77) substantially increased in the carcinogenesis progression of ESCC (Supplementary Figure S2A), while GP5 and 14 IGPs (IGP31, IGP33, IGP34, IGP43, IGP44, IGP47, IGP55, IGP56, IGP58, IGP60, IGP61, IGP62, IGP63, IGP76) showed negative association (Supplementary Figure S2B). After stepwise ordinal logistics regression, GP20 and 4 IGPs (IGP33, IGP44, IGP58, IGP75) retained in the final model and the AIC declined from 749.56 to 531.73. The distribution of these GP and IGPs were presented in Figure 2. In both the discovery and validation populations, compared with the control group, GP20 and IGP75 were elevated ($P \leq 0.05$); whereas IGP33, IGP44, and IGP58 were decreased in the early ESCC group. Similarly, GP20, IGP33, IGP44, IGP58 and IGP75 differed statistically between the early ESCC group and the precancerosis group. Table 3 summarized the association of IgG glycans and traits with ESCC. In the discovery population, the adjusted ORs of GP20, IGP33, IGP44, IGP58, IGP75 were 4.03 ($95\%$ CI: 3.03-5.36, $P \leq 0.001$), 0.69 ($95\%$ CI: 0.55-0.87, $P \leq 0.001$), 0.56 ($95\%$ CI: 0.45-0.69, $P \leq 0.001$), 0.52 ($95\%$ CI: 0.41-0.65, $P \leq 0.001$), and 7.17 ($95\%$ CI: 4.77-10.79, $P \leq 0.001$) respectively, while in the validation population, the adjusted OR were 7.41 ($95\%$ CI: 4.17-13.17, $P \leq 0.001$), 0.66 ($95\%$ CI: 0.45-0.99, $P \leq 0.045$), 0.60 ($95\%$ CI: 0.39-0.92, $P \leq 0.020$), 0.48 ($95\%$ CI: 0.32-0.71, $P \leq 0.001$), and 14.88 ($95\%$ CI: 5.75-38.47, $P \leq 0.001$).
**Figure 2:** *Distribution boxplot of differential GP and IGPs among the controls, precancerosis, and early ESCC groups. (A) The distribution boxplot of the IgG GP and IGPs in the discovery population; (B) The distribution boxplot of the IgG GP and IGPs in the validation population. The vertical position of each histogram represents the relative amount level of GP and IGPs.* TABLE_PLACEHOLDER:Table 3
## Construction and assessment of a glycan score for differentiating ESCC from esophageal precancerosis and controls
We screened ESCC-related N-glycan alterations based on ordinal logistic regression analysis. Regression coefficients were used to estimate odds ratios for each of the independent variables. The mathematic formula named ESCC-related glycan score was constructed to differentiate ESCC from esophageal precancerosis and controls (ESCC-related glycan score = 0.612×GP20 - 0.357×IGP33 - 0.623×IGP44 - 0.439×IGP58 + 1.333×IGP75). The distinct distribution of the ESCC-related glycan score was shown in Figure 3. In both the discovery and validation populations, compared with precancerosis and controls, ESCC-related glycan score was elevated ($P \leq 0.001$) in the early ESCC group. In the discovery population, compared with the controls, ESCC-related glycan score was slightly increased ($P \leq 0.05$) while there was no difference in the validation population. After adjusting confounders including age, sex, BMI, hypertension, smoke, drink, education, income, marriage status and dietary habits (model 3), the ESCC-related glycan score showed significant association with the carcinogenesis progression of ESCC, and the adjusted ORs were 2.86 ($95\%$ CI: 2.33-3.53, $P \leq 0.001$) in the discovery population, and 3.43 ($95\%$ CI: 2.32-5.05, $P \leq 0.001$) in the validation population. Individuals in the highest tertile of the glycan score owned a higher risk compared with those in the lowest, and the adjusted ORs were 11.41 ($95\%$ CI: 6.30-20.69, $P \leq 0.001$) and 14.79 ($95\%$ CI: 5.40-40.51, $P \leq 0.001$), respectively, (Figure 4). Figure 5 illustrated the multi-class ROC curves were of the ESCC-related glycan score for discriminating the controls, esophageal precancerosis and ESCC patients. Accordingly, the AUC value in the discrimination of the controls, esophageal precancerosis and early ESCC patients were 0.710 ($95\%$ CI: 0.656-0.775), 0.672 ($95\%$ CI: 0.625-0.735) and 0.913 ($95\%$ CI: 0.868-0.969) in the discovery population, and 0.692 ($95\%$ CI: 0.589-0.788), 0.677 ($95\%$ CI: 0.597-0.781) and 0.902 ($95\%$ CI: 0.824-0.982) in the validation population. The AUC value of early ESCC patients was significantly different from the controls (<0.01) and esophageal precancerosis (<0.001) in both discovery and validation populations. However, no statistically significant difference was found between the ROC curves of the controls and esophageal precancerosis in the validation populations ($p \leq 0.05$) (Supplementary Table S5). The ESCC-related glycan score achieved an average AUC of 0.822 ($95\%$ CI: 0.786-0.849) and 0.807 ($95\%$ CI: 0.758-0.864), respectively. The results after combining the two populations were similar to each single population.
**Figure 3:** *Distribution violin plot of the ESCC-related glycan score among the controls, precancerosis, and early ESCC groups. (A) The violin plot of the glycan score in the discovery population; (B) The violin plot of the glycan score in the validation population.* **Figure 4:** *Forest plot for the association of the ESCC-related glycan score and progression of ESCC in the discovery population and the validation population. The ESCC-related glycan score estimates the magnitude of the effect as a continuous variable and tertile. The vertical line indicates no effect (odds ratio 1.0); horizontal lines indicate 95% confidence interval. Model 1: unadjusted; Model 2: adjusted for age, sex; Model 3: adjusted for age, sex, BMI, hypertension, smoke, drink, education level, income, marriage status, dietary habits; OR, odds ratio; CI, confidence interval; ref, reference.* **Figure 5:** *The discriminative capacity of the ESCC-related glycan score among the controls, precancerous, and early ESCC populations. (A) The ROC plot of the ESCC-related glycan score in the discovery population; (B) The ROC plot of the ESCC-related glycan score in the validation population.*
After linkage disequilibrium, we found 27 SNPs were associated with the proposed ESCC-related glycan score and 15 of them could be annotated to functional genes (Supplementary Table S6). In total, the genes were significantly enriched in 12 different GO gene sets and 2 different KEGG gene sets (Supplementary Figure S3), and construct a PPI network topology includes 595 nodes and 723 edges (Supplementary Figure S4). Based on the node degree score, the top 6 genes, including SMARCB1, IKZF1, RUNX1, TAB1, RUNX3 and B4GALT1 were considered as potential hub genes. After validation on RNA sequence data in the database online, these 6 genes were differently expressed in ESCC and normal tissues (Supplementary Figure S5), which may be the corroborative evidence of our study.
## Discussion
In this current study, we investigated the association of IgG N-glycosylation profiles and the carcinogenesis progression of ESCC. IgG N-glycans (GP20) and the derived traits (IGP33, IGP44, IGP58, IGP75) were primarily selected and validated to be associated with different stages of ESCC. Specific IgG N-glycosylation pattern participates in the carcinogenesis progression of ESCC, and the proposed ESCC-related glycan score could be a novel indicator. Variation in the fucosylated glycans and the suppressed mannose level, reflected by the altered glycans and traits, could be potential intervention target for ESCC. In addition, an ESCC-related glycan score was composed in this study, which achieved a high AUC value to discriminate different stages of ESCC. Besides, SMARCB1, IKZF1, RUNX1, TAB1, RUNX3, B4GALT1 were considered as potential hub genes of the proposed ESCC-related glycan score.
In our study, we found that GP20 and IGP75 was positively associated with ESCC progression, while IGP33, IGP44 and IGP58 was negatively associated (GP20: digalactosylated monosialylated biantennary with core and antennary fucose; IGP33: the ratio of all fucosylated monosyalilated and disialylated structures; IGP44: the proportion of high mannose glycan structures in total neutral IgG glycans; IGP58: the percentage of all fucosylated structures in total neutral IgG glycans; IGP75: the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans). These results above revealed a glycosylation pattern of increased digalactosylated biantennary glycan, the incidence of bisecting GlcNAc in all fucosylated digalactosylated glycans, and decreased high mannose glycan, fucosylated glycan, the ratio of all fucosylated monosyalilated and disialylated glycan among ESCC.
These finding were largely in consistent with previous studies. Liu et al. reported a significantly decreased of mannose glycan in patients with colorectal cancer [35] and we found a decrease of glycans with mannosylation in precancerous lesions and early esophageal cancer. It was also observed that, mannose glycan was distinctively decreased in breast cancer relative to control in total mouse serum proteins, demonstrating that mannosylation may play an important role in cancer progression not only in human but also in other animals [36]. Removal of mannose sugar residues resulting in conformational changes in Cgamma2 domain affected the structure and function of IgG-Fc fragments [37], showing the importance of mannosylation. Gornik et al. found that IgG would activate complement and ADCC, and promote anti-inflammatory activity according to the extent of galactosylation and fucosylation of its glycans [38]. Sialylation plays a crucial role in the inflammatory potential of IgG. Addition of sialic acid to IgG would decrease its binding to Fcγ receptors, and converts the function from pro- to anti-inflammatory [39]. Sethi et al. reported that the expression levels of disialylation was higher in mid-and late-stage colorectal tumors than in early tumors [29] and we found the ratio of all fucosylated monosyalilated and disialylated structures was negatively associated with ESCC progression. Similar to the critical role of sialylated glycan in the regulation of inflammatory action, the fucosylated glycan can also enhance or inhibit IgG-mediated ADCC [40]. Liu et al. reported that fucosylation and sialylation were associated with lung tumor cell growth and malignancy [41]. Some previous studies pointed out that the decrease of fucosylated glycan was probably associated with colorectal cancer progression [35, 42], and we found a decrease of glycans with fucosylation in ESCC progression. Therefore, it is of significance to reveal the changes of IgG N-glycans abundance, and to explore the profiling of IgG N-glycans as potential biomarker for early detection of ESCC.
Our study found SMARCB1, IKZF1, RUNX1, TAB1, RUNX3 and B4GALT1 as potential hub genes for the proposed ESCC-related glycan score, which were in agreement with previous studies. B4GALT1, IKZF1, TAB1 and SMARCB1 are reported to associate with IgG N-glycosylation show pleiotropy with autoimmune diseases and haematological cancers [43], while Shen et al. used multivariate methods in a genome-wide association study certified B4GALT1 and SMARCB1 are related to IgG N-glycosylation [44]. TAB1 has also been reported associated with the progression and prognosis of esophageal cancer [45]. RUNX3 encodes for a transcription factor of the runt domain-containing family. Methylation of RUNX3 promoters has an impact on cancers (46–49) and B-cell maturation [50]. By influencing T-cell differentiation, RUNX3 is likely to indirectly affect the glycosylation of antibodies produced by B-cells. IKZF1, attributed to the enzymes of the Ikaros family, can also alter the differentiation process of T-cells [51, 52]. Klarić et al. confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3 [53].
In this study we explored the significant differences in IgG N-glycosylation profile among early ESCC, esophageal precancerosis, and the controls. To our knowledge, this is the first attempt aiming at the association of IgG N-glycans biomarkers with the carcinogenesis progression of ESCC. The identified glycans and proposed glycan score were validated in another population. However, the limitations should be addressed. First, the sample size was relatively small causing an inadequate statistical power. Second, this was a population-based cross-sectional study, hence, no causal relationships or pathophysiological inferences were available, basic experiments in vivo or in vitro will be conducted to confirm the association of IgG N-glycans biomarkers with the carcinogenesis progression of ESCC. Third, our study was based on two Chinese populations, more collaborations are needed to validate the generalizability of the observed results for other ethnic groups. Fourth, the identification and quantification of glycans were by HPLC in our study, although glycan standards were used, additional cross validation with other techniques, e.g. mass spectrometry, lectin array will be performed in our further research.
## Conclusions and perspectives
In summary, we have performed the first analysis so far to identify the association of IgG N-glycans biomarkers with the carcinogenesis progression of ESCC. In this study, GP20, IGP33, IGP44, IGP58, IGP75 are significantly associated with the carcinogenesis progression of ESCC, and the proposed glycan score is a novel indicator for different progressive stages. In addition, the variation of fucosylation level and the suppressed mannose level could provide potential therapeutic intervention targets. These findings support the potential utility of glycomics in the ESCC related personalized therapy. The mechanism studies about the biological or pathological function of the fucosylated protein and mannosed protein in the carcinogenesis of ESCC and other cancers are of paramount importance. The experiment on mice after knocking out the corresponding genes of glycosyltransferase and glycosylhydrolase regulating the fucosylation and mannose levels are the next step for our study to validate the effect of IgG N-glycan patterns in the carcinogenesis of ESCC. Future studies on larger cohorts from diverse populations are expected for the validation of these observed associations.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Independent Ethics Committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College and the Ethics Committees of Capital Medical University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XG and GW contributed to conception and design of the study. DZ, ZYZ, YQL, ZZ, ZW, JZ, YuL, YoL, and HZ collected the data. HP, ZL, WF, XTL, and YXL performed the statistical analysis. HP and ZW wrote the first draft of the manuscript. LT, XW, XY, and FZ wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.981861/full#supplementary-material
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|
---
title: 'The value of the MIND diet in the primary and secondary prevention of hypertension:
A cross-sectional and longitudinal cohort study from NHANES analysis'
authors:
- Yanjun Song
- Zhen'ge Chang
- Kongyong Cui
- Chenxi Song
- Zhongxing Cai
- Boqun Shi
- Qiuting Dong
- Kefei Dou
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043250
doi: 10.3389/fnut.2023.1129667
license: CC BY 4.0
---
# The value of the MIND diet in the primary and secondary prevention of hypertension: A cross-sectional and longitudinal cohort study from NHANES analysis
## Abstract
### Background
The Mediterranean-Dietary Approaches to Stop Hypertension for neurodegenerative delay (MIND) has been regarded as a novel healthy dietary pattern with huge benefits. However, its value in preventing and treating hypertension has not been investigated. The objective of this study is to investigate the impact of adhering to the MIND diet on the prevalence of hypertension in the entire population and long-term mortality in hypertensive patients.
### Methods
In this cross-sectional and longitudinal study, 6,887 participants consisting of 2,984 hypertensive patients in the National Health and Nutritional Examination Surveys were analyzed and divided into 3 groups according to the MIND diet scores (MDS; groups of MDS-low [<7.5], MDS-medium [7.5–8.0] and MDS-high [≥8.5]). In the longitudinal analysis, the primary outcome was all-cause death and the secondary outcome was cardiovascular (CV) death. Hypertensive patients received a follow-up with a mean time of 9.25 years (median time: 111.1 months, range 2 to 120 months). Multivariate logistics regression models and Cox proportional hazards models were applicated to estimate the association between MDS and outcomes. Restricted cubic spline (RCS) was used to estimate the dose–response relationship.
### Results
Compared with the MDS-low group, participants in the MDS-high group presented a significantly lower prevalence of hypertension (odds ratio [OR] 0.76, $95\%$ confidence interval [CI] 0.58, 0.97, $$p \leq 0.040$$) and decreased levels of systolic blood pressure (β = −0.41, $$p \leq 0.033$$). Among hypertensive patients, 787 ($26.4\%$) all-cause death consisting of 293 ($9.8\%$) CV deaths were recorded during a 10-year follow-up. Hypertensive patients in the MDS-high group presented a significantly lower prevalence of ASCVD (OR = 0.71, $95\%$ CI, 0.51, 0.97, $$p \leq 0.043$$), and lower risk of all-cause death (hazard ratio [HR] = 0.69, $95\%$ CI, 0.58, 0.81, $p \leq 0.001$) and CV death (HR = 0.62, $95\%$ CI, 0.46, 0.85, p for trend = 0.001) when compared with those in the MDS-low group.
### Conclusion
For the first time, this study revealed the values of the MIND diet in the primary and secondary prevention of hypertension, suggesting the MIND diet as a novel anti-hypertensive dietary pattern.
## Introduction
Hypertension, known as one of the standard modifiable cardiovascular risk factors, greatly contributes to atherosclerotic cardiovascular disease (ASCVD) development and health burden worldwide [1]. Investigations exploring dietary approaches with anti-hypertensive value have been extensively performed. In previous studies, the Dietary Approaches to Stop Hypertension (DASH) diet and the Mediterranean (MED) diets have been widely demonstrated to confer huge benefits in preventing hypertension [2, 3]. Recently, the MED-DASH Intervention for Neurodegenerative Delay (MIND) diet, a promising dietary pattern designed from most of the components in the MED and DASH diets, attached great attention for its great protective values in cognitive performance [4]. As for its components, the MIND diet emphasizes the consumption of whole grains, green leafy vegetables, olive oil, nuts, beans, berries, poultry, and seafood, and restricts the intake of fast-fried foods, sweets, butter, and margarine [5]. In addition to the cognitive protection, benefits brought by the MIND diet are ongoingly revealed, such as preventing ASCVD [6], protecting physical function through strengthening muscles [7], reducing depression symptoms [8], and lowering the risk of breast cancer [9]. Since the MIND diet is designed from the DASH and MED diets and has shown great therapeutic potential in numerous aspects, the value of adhering to the MIND diet in the prevention and treatment of hypertension raised interest. However, evidence for this aspect is scarce.
In this study, we analyzed 6,887 participants consisting of 2,984 hypertensive patients based on The U.S. National Health and Nutrition Examination Survey (NHANES), aiming to investigate the value of the MIND diet in the primary and secondary prevention of hypertension.
## Study design and participants
In this study, we utilized data from the 2 continuous cycles of NHANES from 2003 to 2006 because of no available questionnaire data on the MIND diet in the other years. Adult participants were included in this study, and the exclusion criterion was a lack of data on the components of the MIND diet. A total of 7,205 adult participants with complete data on dietary patterns were included initially. After excluding participants without information on smoking status ($$n = 309$$), mortality data ($$n = 7$$), and blood lipid levels ($$n = 2$$), we analyzed 6,887 participants (2,984 hypertensive patients) in the final analysis (Figure 1). Generally, 6,887 participants were ultimately included in this study. Participants in this study were allocated into 3 groups according to the MIND diet scores (MDS): groups of MDS-low (< 7.5), MDS-medium (7.5–8.0), and MDS-high (≥ 8.5). The optimal MDS cut-offs were defined as the tertiles of MDS in all participants. The main focus of this study is the value of the MIND diet in primary prevention (the association of MDS with the levels of blood pressure [BP] and the prevalence of hypertension in the entire population) and secondary prevention (the association of MDS with the prevalence of ASCVD, and the risk of all-cause death, and CV death in hypertensive patients) of hypertension.
**Figure 1:** *Flowchart of participant selection. MDS-Low, Low MIND score, the MIND diet score < 7.5; MDS-Mdeium, Medium MIND score, the MIND diet score ≥ 8 and < 8.5; MDS-High, High MIND score, the MIND diet score ≥ 8.5. NHANES, National Health and Nutrition Examination Survey; MIND diet, Mediterranean-DASH Diet Intervention for Neurodegenerative Delay diet; MDS, the MIND diet scores.*
Hypertension was defined as a self-reported medical history of high blood pressure, receiving antihypertensive drugs, or blood pressure measurement ≥$\frac{140}{90}$ mmHg [10], and ASCVD was defined as a series of coronary artery disease (CAD), heart attack, angina, congestive heart failure, stroke, and peripheral artery disease.
## Dietary assessment
In this study, we analyzed each diet component relevant to the MIND diet according to the Food Frequency Questionnaire during the NHANES 2003 to 2006-year cycles. The food frequency questionnaire (FFQ) of this study was only conducted at baseline, and concrete information was presented in Supplementary Text S1. MDS was applicated to evaluate adherence to the MIND diet. The MIND diet includes 10 brain-healthy food groups (green leafy vegetables, other vegetables, nuts, berries, beans, whole grains, seafood, poultry, olive oil, and wine) and 5 unhealthy food groups (red meats, butter, and stick margarine, cheese, pastries and sweets, and fried/fast food). Olive oil consumption was scored 1 if identified by the participant as the primary oil usually used at home and 0 otherwise. For all other diet score components, we summed the frequency of consumption of each food item portion associated with that component and then assigned a concordance score of 0, 0.5, or 1. The total MDS was computed by summing over all 15 of the component scores, concrete information on the calculation of MDS was presented in Supplementary Table S1 [4].
## Follow-up and outcomes
The primary outcome of this study was all-cause and the secondary outcome was CV death. Mortality status was ascertained with death certificate records by linkage to the National Death Index through December 31, 2019. The specific cause of death was determined based on the International Statistical Classification of Disease, Tenth Revision (ICD-10). CV death was defined as deaths from heart diseases (ICD-10 codes I00-I09, I11, I13, I20-I51) or cerebrovascular diseases (ICD-10 codes I60-I69). The follow-up time was calculated from the NHANES Mobile Examination Center (MEC) date to the date of death or end of follow-up (December 31, 2019), whichever came first. These final mortality statuses, follow-up time, and the underlying leading causes of death files are available for online access.1
## Covariates
Covariates were collected at baseline (NHANES 2003–2004). Information on age, sex, race/ethnicity, smoking status, alcohol consumption, physical activity, and self-reported medical conditions were obtained through standardized questionnaires during in-home interviews by trained interviewers. Heights, weights, waist circumferences, blood pressures, and blood samples were collected from physical examinations at Mobile Examination Center (MEC) using standard protocols.
Race/ethnicity was categorized as non-Hispanic White people, non-Hispanic Black people, Mexican American people, and others. Smoking status was categorized as never (smoked less than 100 cigarettes in life), former (smoked more than 100 cigarettes in life but quit smoke now), and current (smoked more than 100 cigarettes in life and still smoke some days or every day). Physical activity was measured as the weekly minutes of moderate and vigorous activities multiplied by the metabolic equivalent (MET) level and divided into four categories: sedentary (without regular physical activity, MET-minutes/week = 0), insufficient (0 < MET-minutes/week <500), moderate (500 ≤ MET-minutes/week ≤1,000), and high (>1,000 MET-minutes/week) [10], Body mass index (BMI, kg/m2) was calculated as weight in kilograms divided by height in meters squared. Diabetes mellitus was defined as a self-reported medical history of diabetes, receiving oral hypoglycemic agents or insulin, fasting glucose level ≥ 126 mg/dl, or hemoglobin A1c (HbA1c) level ≥ $6.5\%$ [11]. Hyperlipidemia was defined as serum triglycerides (TG) ≥ 150 mg/L, total cholesterol (TC) ≥200 mg/dl, low-density lipoprotein cholesterol (LDL-C) ≥ 130 mg/dL, high-density lipoprotein cholesterol (HDL-C) ≤ 40 mg/dl in men or ≤ 50 mg/dL in women, or receiving medication for hyperlipidemia [12]. The biochemical parameters, including TG, TC, HDL-C, LDL-C, and HbA1c were measured among partial participants who provided blood samples ($95.7\%$, $\frac{6590}{6887}$) at MEC.
## Statistical analysis
As part of the NHANES complex sampling design, we utilized appropriate weights to ensure a representative sample of the US national population.2 The results of baseline characteristics were presented as weighted means ± standard error for continuous variables and frequency (weighted percentages) for categorical variables. We compared the differences among groups using ANOVA for continuous variables and χ2 tests for categorical variables. The percentages of missing data for covariates were lower than $5\%$ (BMI [$1.7\%$], and energy intake [$5.0\%$]). Imputation with the median of each variable was used to incorporate all data for modeling.
The analysis mainly included three parts: [1] In the first part of our analysis, we made a cross-sectional analysis of the entire population to investigate the association of adhering to the MIND diet with the prevalence of hypertension and levels of BP, [2] Next, we focused on hypertensive patients and made a cross-sectional analysis to explore the association of adhering to the MIND diet with the prevalence of ASCVD, and [3] At last, we performed a longitudinal analysis (a 9.25-year clinical follow-up) for hypertensive patients with the outcomes of all-cause and cardiovascular death to explore the value of the MIND diet in the secondary prevention of hypertension.
The odd ratios (ORs) and $95\%$ confidence intervals (CIs) for the association of MDS with the prevalence of hypertension and ASCVD were estimated using multi-variate Logistics regression models (cross-sectional analysis), and the hazard ratios (HRs) and $95\%$ CIs for the association of MDS with the risk of all-cause death and CV death were explored using multivariate Cox proportional hazards models (longitudinal analysis). The correlation of MDS with levels of BP was estimated in the linear regression with the fully adjusted model. Restricted cubic spline (RCS) with 4 knots (5th, 35th, 65th, and 95th percentiles) in the fully adjusted model was used to estimate the dose–response relationship between MDS and outcomes. Nonlinearity was tested using the likelihood ratio test. Apart from the crude model, we adjusted potential covariates progressively in the 3 models. Model 1 was adjusted for age (continuous), sex (male or female), and race/ethnicity (non-Hispanic White people, non-Hispanic Black people, Mexican American people, and other). Model 2 was further adjusted for all covariates in Model 1 and smoking status (never, former, and current), physical activity (sedentary, insufficient, moderate, and high), and BMI (< 25.0, 25.0–29.9, and ≥ 30.0 kg/m2). Model 3 (fully adjusted model) was further adjusted for all covariates in Model 2 and diabetes, hyperlipidemia, and energy intake.
Subgroup analysis was performed by age (< 60 or ≥ 60 years), sex (male or female), race/ethnicity (White people or non-White people), smoking status (never or former and current), BMI (<30.0 or ≥ 30.0 kg/m2), physical activity (sedentary and insufficient or moderate and high), and diabetes (yes or no), and examined the significance of multiplicative interaction terms between the stratification variables and MDS by the Wald test.
Sensitivity analyzes were conducted based on the fully adjusted model. Firstly, we excluded non-Hispanic Black participants because of the higher prevalence of mortality among non-Hispanic Black individuals. Secondly, we excluded Mexican American people and other participants because of the oversampled non-Hispanic participants. Thirdly, we excluded participants who died within 1 year of follow-up to minimize the potential reverse causation bias. Finally, we excluded participants with cerebral diseases because of the brain-protective effects of the MIND diet which might enlarge its protective effects on long-term mortality.
All analyzes were performed with R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria) using the “survey” package. A 2-tailed value of $p \leq 0.05$ was considered significant.
## Characteristics of the study population
Generally, 6,887 participants (age range:20 to 85 years old) were ultimately included in this study. In this study, MDS in all participants ranged from 4.5 to 13 points (Supplementary Table S2; Supplementary Figure S1). Table 1 showed the baseline characteristics of participants grouped by MDS. The overall weighted mean age of all participants was 47.13 years and $53.8\%$ of them were female. Participants who better adhered to the MIND diet were older, more likely to be non-Hispanic white people, non-smokers, and have higher levels of DBP, physical activity, and less likely to combine cerebral diseases. The baseline characteristics of excluded participants with incomplete data were presented in Supplementary Table S3.
**Table 1**
| Characteristics | MDS tertile | MDS tertile.1 | MDS tertile.2 | MDS tertile.3 | p trend |
| --- | --- | --- | --- | --- | --- |
| Characteristics | Total (N = 6,887) | MDS-L (N = 1999) | MDS-M (N = 1847) | MDS-H (N = 3,041) | p trend |
| Age (years) | 47.13 ± 0.45 | 42.97 ± 0.52 | 47.37 ± 0.57 | 49.66 ± 0.61 | < 0.001 |
| Sex, n (%) | | | | | < 0.001 |
| Male | 3,181 (46.19) | 1,051 (52.29) | 876 (49.14) | 1,254 (39.13) | |
| Female | 3,706 (53.81) | 948 (47.71) | 971 (50.86) | 1787 (60.87) | |
| Race/ethnicity, n (%) | | | | | < 0.001 |
| Non-Hispanic White people | 3,809 (55.31) | 1,080 (70.53) | 993 (70.51) | 1736 (75.53) | |
| Non-Hispanic Black people | 1,333 (19.36) | 478 (15.09) | 369 (12.02) | 486 (8.67) | |
| Mexican American people | 1,272 (18.47) | 318 (7.11) | 353 (8.44) | 601 (8.20) | |
| Others | 473 (6.87) | 123 (7.27) | 132 (9.03) | 218 (7.60) | |
| Smoking status, n (%) | | | | | < 0.001 |
| Never | 3,526 (51.2) | 923 (43.95) | 964 (49.76) | 1,639 (53.92) | |
| Former | 1920 (27.88) | 478 (20.01) | 485 (25.40) | 957 (30.43) | |
| Current | 1,441 (20.92) | 598 (36.04) | 398 (24.84) | 445 (15.65) | |
| BMI (kg/m2), n (%) | | | | | 0.07 |
| <25.0 | 2082 (30.71) | 610 (35.08) | 531 (32.15) | 941 (33.80) | |
| 25.0–29.9 | 2,356 (34.75) | 639 (29.51) | 635 (34.03) | 1,082 (34.85) | |
| ≥30.0 | 2,341 (34.53) | 717 (35.41) | 653 (33.82) | 971 (31.35) | |
| Physical activity, n (%) | | | | | < 0.001 |
| Sedentary | 1774 (25.76) | 610 (23.94) | 504 (19.68) | 660 (15.03) | |
| Insufficient | 2,621 (38.06) | 728 (39.66) | 726 (43.71) | 1,167 (41.41) | |
| Moderate | 1,129 (16.39) | 302 (17.10) | 289 (16.32) | 538 (19.04) | |
| High | 1,363 (19.79) | 359 (19.30) | 328 (20.29) | 676 (24.52) | |
| MDS (score) | 8.16 ± 0.05 | 6.45 ± 0.02 | 7.75 ± 0.01 | 9.49 ± 0.03 | < 0.001 |
| DBP (mmHg) | 54.61 ± 0.31 | 51.75 ± 0.60 | 53.74 ± 0.42 | 56.93 ± 0.52 | < 0.001 |
| SBP (mmHg) | 123.26 ± 0.45 | 122.41 ± 0.55 | 123.71 ± 0.74 | 123.53 ± 0.55 | 0.23 |
| Hypertension, n (%) | 2,984 (43.33) | 846 (36.03) | 841 (40.34) | 1,297 (38.27) | 0.21 |
| Diabetes, n (%) | 1,021 (14.83) | 304 (11.01) | 277 (11.07) | 440 (10.78) | 0.96 |
| Hyperlipidemia, n (%) | 4,954 (71.93) | 1,428 (69.18) | 1,347 (71.10) | 2,179 (69.96) | 0.68 |
| Cerebral diseases, n (%) | 287 (4.17) | 96 (3.82) | 83 (2.80) | 108 (2.15) | 0.003 |
| TG (mg/dL) | 142.74 ± 2.53 | 150.65 ± 5.04 | 141.39 ± 4.38 | 138.86 ± 4.20 | 0.21 |
| HbA1c (%) | 70.47 ± 0.28 | 70.70 ± 0.54 | 70.32 ± 0.50 | 70.41 ± 0.38 | 0.87 |
| Fast blood glucose (mg/dL) | 5.47 ± 0.02 | 5.45 ± 0.03 | 5.50 ± 0.03 | 5.46 ± 0.02 | 0.48 |
| Waist circumference (cm) | 97.53 ± 0.45 | 98.50 ± 0.59 | 97.89 ± 0.62 | 96.71 ± 0.63 | 0.08 |
| Energy intake (Kcal) | 2117.02 ± 15.62 | 2219.03 ± 28.08 | 2093.35 ± 21.63 | 2065.08 ± 23.42 | < 0.001 |
## Association of adhering to the MIND diet with the prevalence of hypertension in the whole population
Table 2 and Figure 2A presented the logistic regression results of the association of adhering to the MIND diet with the prevalence of hypertension in 3 different models. Although no significant association was found in the crude model, results in other models presented that the MDS-high group showed a significantly lower prevalence of hypertension compared with participants in the low MDS group (model 3, HR 0.76, $95\%$ CI 0.58, 0.97, $$p \leq 0.040$$). Besides, per one-score increase in MDS was shown to be associated with a $9\%$ lower prevalence of hypertension (model 2, HR 0.91, $95\%$ CI, 0.86, 0.97) (Table 2), and RCS showed a linear association of MDS with the prevalence of hypertension (p for non-linearity = 0.259) (Figure 3A).
Linear regression analysis presented that MDS was inversely correlated with the levels of SBP (β = −0.41, $$p \leq 0.033$$) in the whole population (Supplementary Table S5).
## Association of adhering to the MIND diet on prevalence of ASCVD and BP levels in hypertensive patients
The baseline characteristics of hypertensive participants in this study were presented in online Supplemental files (Supplementary Table S4), and 641 ($21.5\%$) of them combined ASCVD. Table 3 showed that hypertensive participants in the MDS-high group presented a significantly lower prevalence of ASCVD (model 3, OR = 0.80, $95\%$ CI, 0.51, 0.97, p for trend = 0.043) compared with the MDS-low group in all models (Figure 2B). Moreover, per one-score increase in MDS was found to be associated with a $10\%$ lower prevalence of ASCVD (HR = 0.90, $95\%$ CI, 0.82, 0.99) (Table 3), and RCS showed a linear association of MDS with the prevalence of ASCVD (p for non-linearity = 0.614) in hypertensive participants (Figure 3B). No significant correlation was found between MDS and BP levels in hypertensive patients (Supplementary Table S5).
**Table 3**
| Model | Per one-score increases in MDS OR/HR (95% CI) | ORs/HRs (95% CI) | ORs/HRs (95% CI).1 | ORs/HRs (95% CI).2 | p trend |
| --- | --- | --- | --- | --- | --- |
| Model | Per one-score increases in MDS OR/HR (95% CI) | MDS-L | MDS-M | MDS-H | p trend |
| ASCVD | ASCVD | ASCVD | ASCVD | ASCVD | ASCVD |
| Number of ASCVD/totals | 641/2984 | 203/846 | 183/841 | 255/1297 | |
| Crude | 0.89 (0.82, 0.97) | 1.00 | 0.85 (0.58, 1.24) | 0.70 (0.52, 0.95) | 0.021 |
| Model 1 | 0.84 (0.76, 0.93) | 1.00 | 0.72 (0.48 1.08) | 0.56 (0.40, 0.80) | 0.002 |
| Model 2 | 0.89 (0.81, 0.98) | 1.00 | 0.78 (0.52, 1.17) | 0.68 (0.49, 0.95) | 0.030 |
| Model 3 | 0.90 (0.82, 0.99) | 1.00 | 0.80 (0.53, 1.22) | 0.71 (0.51, 0.97) | 0.043 |
| All-cause mortality | All-cause mortality | All-cause mortality | All-cause mortality | All-cause mortality | All-cause mortality |
| Number of deaths/totals | 787/2984 | 254/846 | 234/841 | 299/1297 | |
| Crude | 0.88 (0.83, 0.93) | 1.00 | 1.02 (0.80, 1.29) | 0.72 (0.58, 0.90) | 0.002 |
| Model 1 | 0.83 (0.79, 0.87) | 1.00 | 0.85 (0.72 1.01) | 0.58 (0.48, 0.69) | <0.001 |
| Model 2 | 0.87 (0.83, 0.93) | 1.00 | 0.90 (0.76, 1.07) | 0.69 (0.58, 0.82) | <0.001 |
| Model 3 | 0.90 (0.86, 0.95) | 1.00 | 0.91 (0.78, 1.09) | 0.69 (0.58, 0.81) | <0.001 |
| CV mortality | CV mortality | CV mortality | CV mortality | CV mortality | CV mortality |
| Number of deaths/totals | 293/2984 | 97/846 | 88/841 | 108/1297 | |
| Crude | 0.85 (0.77, 0.94) | 1.00 | 0.95 (0.66, 1.37) | 0.60 (0.40, 0.89) | 0.007 |
| Model 1 | 0.80 (0.73, 0.89) | 1.00 | 0.80 (0.57, 1.12) | 0.48 (0.33, 0.69) | <0.001 |
| Model 2 | 0.87 (0.79, 0.96) | 1.00 | 0.90 (0.66, 1.25) | 0.61 (0.44, 0.85) | 0.002 |
| Model 3 | 0.87 (0.79, 0.96) | 1.00 | 0.93 (0.67, 1.29) | 0.62 (0.46, 0.85) | 0.001 |
Besides, the inverse association of MDS with the prevalence of ASCVD was significant in the entire population (Supplementary Table S6), but not in participants without hypertension (Supplementary Table S7).
## Association of adhering to the MIND diet with risk of all-cause death and CV death in hypertensive patients
During the follow-up, a total of 787 ($26.4\%$) all-cause deaths and 293 ($9.8\%$) CV deaths in hypertensive patients were recorded. As is shown in Table 3, Compared with participants in the low MDS group, hypertensive patients in the MDS-high group showed a significantly lower risk of all-cause death (model 3, HR = 0.69, $95\%$ CI, 0.58, 0.81, P for trend <0.001) and CV death (model 3, HR = 0.62, $95\%$ CI, 0.46, 0.85, P for trend = 0.001) in the all models (Figures 2C,D). Per one-score increase in MDS was associated with a $10\%$ lower risk of all-cause death (HR = 0.90, $95\%$ CI, 0.86, 0.95) and a $13\%$ lower risk of CV death (HR = 0.87, $95\%$ CI, 0.79, 0.96) (Table 3). Besides, RCS showed linear associations of MDS with the risk of all-cause death (p for non-linearity = 0.585) and CV death (p for non-linearity = 0.662) in hypertensive participants (Figures 3C,D).
Kaplan–Meier curves for all-cause mortality and CV mortality of 3 groups among hypertensive patients were further performed (Supplementary Figure S2). Consistently, groups with higher MDS showed a significantly lower risk of all-cause death (Log-rank test, $p \leq 0.001$) and CV death (Log-rank test, $$p \leq 0.014$$).
Besides, the inverse associations of MDS with the risk of all-cause death and CV death were significant in the entire population (Supplementary Table S6). As for participants without hypertension, the inverse associations of MDS with the risk of all-cause death, but not CV death, was significant (Supplementary Table S7).
## Verification of results
Subgroup analyzes for the prevalence of hypertension in the whole population (Supplementary Table S8; Supplementary Figure S3), the prevalence of ASCVD (Supplementary Table S9; Supplementary Figure S4), and the risk of all-cause death (Supplementary Table S10; Supplementary Figure S5), and CV death (Supplementary Table S11; Supplementary Figure S6) in hypertensive patients were presented in supplemental files. Analyzes were stratified by age (<60 or ≥ 60 years), sex (male or female), race/ethnicity (non-Hispanic White people or other), smoking status (never or former/current), hypertension (yes or no), diabetes (yes or no), BMI (<30 or ≥ 30 kg/m2) and physical activity (sedentary/insufficient or moderate/high). Results in subgroup analyzes did not change. Specifically, significant interactions were found in the subgroup analysis of age for the risk of all-cause death, the subgroup analysis of sex for the prevalence of ASCVD, and all-cause death, and the subgroup analysis of race for the risk of CV death.
When it came to the sensitivity analysis for the risk of all-cause death and CV death in hypertensive participants (Supplementary Table S12), the results remained consistent after excluding Hispanic participants, Mexican American and other participants, individuals who died within 1 year of follow-up, and participants with cerebral diseases.
To further validate the results above, we compared subjects with the mean score of each MDS category and found that the results were consistent (Supplementary Table S13). Besides, we also explored the protective effect of each food component in Supplementary Table S14. Results showed that the points were largely from restricted intake of butter and red meat. High intakes of fish, green leafy vegetables, nuts, and poultry, limited consumption of red meat, and proper intake of wine were the main protective contributors.
## Discussion
In this cross-sectional and longitudinal study, a total of 6,887 participants consisting of 2,984 hypertensive patients were ultimately included. The main findings of this study include [1] better adherence to the MIND diet is associated with a lower prevalence of hypertension in the whole population, [2] hypertensive patients who adhered better to the MIND diet presented a lower prevalence of ASCVD, and a lower risk of all-cause death, and CV death, and [3] The inverse associations of MDS with the prevalence of hypertension, ASCVD, and the risk of all-cause death, and CV death all presented as linear relationships, and per 1-score increase in MDS was shown to significantly reduce the risk above. To date, this study documented the protective value of adhering to the MIND diet in both primary and secondary prevention of hypertension for the first time.
Investigations for anti-hypertensive dietary patterns have nowadays been widely performed. In previous studies, the DASH and MED diets have been revealed to confer great value in the prevention of hypertension, since numerous randomized control trials (RCT) reported that the MED and DASH diets significantly decreased both SBP and DBP in the whole population [2, 3]. The MIND diet was initially designed based on the dietary components of the MED and DASH diets, including the great emphasis on natural plant foods and restricted consumption of animal and high saturated fat foods [4]. However, the anti-hypertensive value of the MIND diet has not been investigated so far. For the first time, this study revealed that better adherence to the MIND diet was associated with decreased SBP and lower prevalence of hypertension in the whole population, documenting the significant value of the MIND diet in the primary prevention of hypertension.
Another major finding of this study is firstly revealing the values of the MIND diet in the secondary prevention of hypertension. Numerous studies have confirmed the therapeutic benefits in hypertensive patients who adhered to the DASH diet (3, 13–15) and the MED diet [2, 16]. As for the MIND diet, its benefits of lowering long-term all-cause mortality in old participants have been reported recently [17], however, no research explored the therapeutic value of the MIND diet among hypertensive patients. In this study, we focused on patients with hypertension and revealed the improved prognosis in those with better adherence to the MIND diet. In addition to all-cause mortality, the cardioprotective potential of the MIND diet was recently discussed. In a current prospective cohort study with 2,863 participants, Mahdieh et al. revealed a significant inverse relationship between MDS and CVD (comprised of CAD, stroke, and CV mortality) risk [6]. Besides, a recent case–control study focused on patients with stroke also presented similar results [18]. Consistent with previous studies, a significantly lower risk of ASCVD and CV death was also reported in the entire population and hypertensive patients who better adhered to the MIND diet, further supporting that the MIND diet was a cardioprotective dietary pattern and concreting the therapeutic value of the MIND diet in the secondary prevention of hypertension.
In the subgroup analyzes of this study, we reported that the protective anti-hypertensive impact of the MIND diet was more significant in old people, females, and non-white people. These results indicated the protective roles of the MIND diet might be various in different people. Thus, future studies are also expected to compare the beneficial impact of adhering to the MIND diet on participants with different age groups, sex, and races to further illuminate the anti-hypertensive roles of the MIND diet.
In the components of MIND diets, we found that MDS was largely coming from a restricted intake of butter and red meat. High intakes of fish, green leafy vegetables, nuts, and poultry, limited consumption of red meat, and proper intake of wine were the main protective contributors. In previous studies, the high intake of fish with huge sources of α-linolenic acid and marine omega-3 fatty acids [19], and high consumption of whole grains [20], olive oil [21], beans [22], and nuts [23], which were also emphasized in the DASH or MED diet, have been demonstrated as great anti-hypertension dietary components in previous studies. In addition, the MIND diet uniquely emphasized the consumption of berries and green leafy vegetables for dementia prevention, and these components also presented anti-hypertensive effects. In a rigorous investigation performed on hypertensive rats, Melissa et al. reported that the 6 week consecutive consumption of an experimental diet containing $4\%$ green leafy vegetables significantly decreased SBP [24]. Moreover, the consumption of berries was also indicated to hold great therapeutic potential in both the prevention and treatment of hypertension, as numerous recent clinical trials and RCT reported that the consumption of berries brought a significant reduction in levels of SBP and DBP regardless of the combination of hypertension (25–27). As for the mechanisms, great sources of quercetin, flavonoid, and folate from green leafy vegetables and berries were shown to be the main contributors through great anti-oxidative effects and vascular protection (28–30). As for the red meat, poultry, and wine intake, their associations with hypertension were still controversial, our results need to be further validated in future studies.
In the subgroup analysis, we found that the protective effect of the MIND diet was more significant in females. This conclusion was also reported by a recent study that focused on the relationship between the MIND diet and the risk of dementia, as the result showed that MIND adherence contributed to a decrease in the risk of dementia in females but not males [31]. Besides, a more significant association between adherence to the MIND diet and a decrease in mortality among olde people was reported in the current study. Although there is no study comparing the protective effects of the MIND diet between old people and young people before, scholars have previously focused on old people and determined that closer adherence to the MIND diet is significantly associated with lower all-cause mortality [17]. The interaction between race and the MIND diet has not been discussed previously, and the current study reported that the MIND diet-related reduction in the risk of CV death was more significant in white people for the first time. This conclusion was warranted to be further validated in future studies focusing on white people.
The main strength of this study is the first illustration of the impact of adhering to the MIND diet on the prevalence of hypertension in the whole population, and the prevalence of ASCVD and prognosis in hypertensive patients. However, there were several limitations in this study. Firstly, the association of adhering to the MIND diet with the prevalence of hypertension and ASCVD was investigated in cross-sectional analyzes, which might not have identified robust causal inferences. Secondly, the diagnosis of ASCVD was based on questionnaires without medical records, laboratory tests, or imaging, which might cause misdiagnosis. Thirdly, the 24 h dietary recall data was not applied in this study for the reason that these data mostly presented as “g/day” but not “serving/week” which is not suitable for the calculation of the MIND diet scores. Fourthly, the non-Hispanic participants were oversampled in the NHANES database. To decrease the bias from ethnicity, we made the related subgroup and sensitivity analysis. Nevertheless, it is still worthy to be validated in a population with proper percentages of ethnicity. Fifthly, the intake of each component of the MIND diet was collected based on a questionnaire without correction from specialists and continuous follow-up, which might contribute to bias. At last, the small or moderate sample size of this study limited the strength of the conclusions. Therefore, future exploration for the association of the MIND diet with hypertension is expected to be performed in large cohort studies or RCTs.
## Conclusion
In conclusion, this study focused on the whole population and hypertensive patients and revealed the therapeutic potential of the MIND diet in the primary and secondary prevention of hypertension. These results documented the MIND diet as a novel anti-hypertensive dietary pattern for the first time.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements.
## Author contributions
KD and YS: conceptualization. QD, YS, and ZCh: methodology. YS and ZCh: software and formal analysis. YS and KC: validation. YS, ZCh, KC, and ZCa: investigation. KD and QD: resources. ZCh: data curation. YS, ZCh, KC, CS, and BS: writing—original draft preparation. QD and KD: writing—review and editing. YS: visualization. KD: supervision, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (grant no. 2021-I2M-1-008).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1129667/full#supplementary-material
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---
title: Incidence and risk factors of hearing loss in patients with Turner Syndrome
authors:
- Huijia Lin
- Xiaoya Wang
- Shuang Qin
- Fanglan Luo
- Yingmei Cen
- Gendie E. Lash
- Li Li
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043252
doi: 10.3389/fpubh.2023.1076812
license: CC BY 4.0
---
# Incidence and risk factors of hearing loss in patients with Turner Syndrome
## Abstract
### Background
Hearing loss (HL) is one of the main medical complications for Turner Syndrome (TS) patients, with an earlier presentation and higher incidence than normal women. However, the etiology of HL in TS is unclear. The aim of this study was to investigate the hearing status of TS patients in China and the influencing factors, so as to provide a theoretical basis for early intervention treatment for TS patients with HL.
### Methods
In total 46 female patients aged 14–32 diagnosed with TS received tympanic membrane and audiological examinations, including pure tone audiometry and tympanometry. In addition, the effects of karyotype, sex hormone levels, thyroid function, insulin, blood lipids, bone mineral density, age and other factors on hearing levels were analyzed, and the possible risk factors associated with HL in TS patients were explored.
### Results
In 9 patients ($19.6\%$) had HL, including 1 ($2.2\%$) with mild conductive hearing loss, 5 ($10.9\%$) with mild sensorineural hearing loss, 3 ($6.5\%$) with moderate sensorineural hearing loss. TS is often associated with age-related mid-frequency and high-frequency HL, and the incidence of HL increases with age. Compared with other karyotypes, patients with 45, X haplotype have an increased risk of mid-frequency HL.
### Conclusions
Therefore, karyotype may be a predictor of hearing problems in TS.
## 1. Introduction
Turner Syndrome (TS) is one of the most common human chromosomal abnormalities and is a major cause of short stature and ovarian insufficiency in women, which is caused by partial or complete loss of the X chromosome. The most common karyotype of TS is 45, X, accounting for about 30 to $40\%$ of all karyotypes, followed by mosaic patterns (45, X/46, XX), accounting for about 20–$30\%$, and the rest are X chromosome abnormalities [1, 2]. The incidence of TS in live born girls is about $\frac{1}{2}$,000–$\frac{1}{2}$,500, and the incidence may vary in different countries and regions [3]. In recent years, with the wide application of prenatal diagnosis technology, the age of diagnosis of TS has been advanced, but most TS patients are usually diagnosed in childhood or adolescence due to short stature or primary gonadal dysplasia, and the average age of diagnosis is 15 years old [3].
In addition to gonadal dysgenesis and short stature, with the increase of age, TS is often accompanied by a variety of complications, such as cardiovascular disease, autoimmune disease, metabolic disease, osteoporosis, and neurocognitive deficits [4]. Hearing loss (HL) is one of the main medical problems for girls and women with TS and has a negative impact on the health and quality of life for these patients. A previous study demonstrated that only $48\%$ of Swedish women aged 25 to 38 with TS had normal hearing, while $45\%$ had mild HL and $7\%$ had moderate or severe HL [5]. On average, the hearing threshold of TS women is equivalent to the hearing threshold of women in the general population 20 years older than their actual age [5]. The specific etiology of HL caused by TS has not been extensively studied, it may be the influence of estrogen, chromosome and gene abnormalities, or their combination [6, 7]. Due to ovarian hypoplasia, TS patients have little or no endogenous estrogen production, and estrogen has a certain protective effect on hearing [8, 9]. In addition, some genes on the short arm of the X chromosome may have a regulatory effect on hearing function, and their deletion may cause abnormal craniofacial development and auricle abnormalities, which may be related to the prolonged cell cycle caused by chromosomal abnormalities [7, 10]. Some patients with TS may have abnormalities of the Eustachian tube and the skull base, repeated occurrence of otitis media, middle ear effusion, and cholesteatoma can lead to middle ear destruction and progressive HL [7, 11].
TS patients often also have endocrine abnormalities, metabolic disease and abnormal bone metabolism. Studies have shown that low bone mineral density (BMD), thyroid dysfunction and metabolic syndrome are risk factors for HL (12–17), and timely supplementation of thyroid hormone or lowering of blood lipids can improve hearing. However, it is not known if this association also exists in women with TS.
At present, the treatment of TS is mainly growth-promoting therapy, inducing and maintaining the development of secondary sexual characteristics, preventing and curing osteoporosis. Less attention has been paid to the ear health of TS patients, which delays optimal treatment time. Early diagnosis and proper management of associated hearing abnormalities are important to minimize the adverse effects on learning and social functions and improve quality of life. Here, we analyzed the incidence, types and risk factors of HL in TS patients in China, with an aim to improve the prevention and treatment of TS ear diseases and improve the quality of life of women living with TS.
## 2.1. Clinical characteristics
Forty-six female patients diagnosed with TS in the Department of Gynecology and Endocrinology, Guangzhou Women and Children's Medical Center affiliated to Guangzhou Medical University were recruited and confirmed by chromosome karyotype analysis of their peripheral blood as previously described [17]. Patients who also had another genetic disease (or another related syndrome) affecting hearing and/or refused to cooperate and were unable to perform audiometry testing were excluded. All patients denied a history of ear trauma, noise exposure, ototoxic drug use, smoking and drinking history. General information and clinical data of the patients were collected, including height, weight, chromosome karyotype, blood lipid concentrations, thyroid function, BMD, and hormone treatment status. This study was approved by the Ethics Committee of Guangzhou Women and Children's Medical Center [2016042019], and all participants or their guardians signed an informed consent form.
## 2.2.1. Turner Syndrome diagnostic criteria
The diagnostic criteria for TS included a karyotype containing one X chromosome and complete or partial absence of the second sex chromosome, associated with one or more typical clinical manifestations of TS, including short stature, hypoplastic secondary sexual characteristics, primary or secondary amenorrhea during adolescence, webbed neck, elbow valgus, cardiovascular and urinary system and other malformations [3].
## 2.2.2. Diagnostic criteria for audiology
According to the 2021 World Health Organization's hearing loss classification standards, using the pure tone average air conduct threshold between 500, 1,000, 2,000, and 4,000 Hz frequencies as the hearing threshold, HL is defined as having hearing thresholds greater (worse) than 20 decibel (dB) alteration at 6 and/or 8 kHz, and the PTA value obtained by average air conduction thresholds between 500, 1,000, 2,000, and 4,000 Hz frequencies is used as the basis for classifying hearing loss levels: [1] Normal hearing: less than 20 dB; [2] Mild HL: 20 to <35 dB; [3] Moderate HL: 35 to <50 dB; [4] Moderately severe HL: 50 to <65 dB; [5] Severe HL: 65 to <80 dB; [6] Profound HL: 80 to <95 dB; [7] Complete or total HL: 95 dB or greater; and [8] Unilateral: <20 dB in the better ear, 35 dB or greater in the worse ear. Using the PTA air-bone gap (PTA-ABG) and bone conduction pure tone average (PTA), HL is divided into conductive, sensorineural and mixed hearing loss.
According to the pressure variations exerted in the outer ear canal 19, the tympanic impedance is divided into Type A (normal, with a symmetric wave), Type B (rounded or flat curve), Type C (low wave with leftward shift) and Type Ad (very high wave) [18, 19].
## 2.2.3. Diagnostic criteria for patient investigations
All TS patients received diagnosis and treatment in Guangzhou Women and Children's Medical Center, and underwent detailed consultation (including past and present medical history), tympanic membrane and audiological examination by otorhinolaryngology specialists. Pure tone audiometry was performed with GSI AudioStarPro in the audiometry room with background noise less than 25 dB. Each frequency was repeated three times, and the air conducting threshold was recorded at 125, 250, 1,000, 2,000, 4,000, and 8,000 Hz frequencies, respectively. Bone conduction thresholds were recorded at 125, 250, 1,000, 2,000, and 4,000 Hz respectively. Tympanometry was performed with a GSI TympStar middle ear analyzer, with probe tone set to a frequency of 226 Hz. Peripheral blood was collected into inert separating gelatinizing tubes, serum collected and stored at −20°C until required for analysis. Thyroid stimulating hormone (TSH), free thyroxine (FT4), thyroid peroxidase antibody (TPO-Ab), triglyceride (TG), total cholesterol (TC), low- density lipoprotein (LDL), and high-density lipoprotein (HDL) were measured in the serum of all subjects. Thyroid function was measured by the electron chemiluminescence immunoassay in the Abbott I2000 analyzer, and the blood lipid levels of all subjects were measured by the Hitachi 7600-200 analyzer. Body fat, waist:hip ratio and BMD in different sites were measured by dual energy X-ray absorptiometry (DXA) using a Lunar DXA densitometer (Lunar Corporation, Madison WI, U.S.A.).
## 2.3. Statistical analysis
The SPSS 26.0 software package was used for statistical analysis of all data. The measurement data with normal distribution is represented by mean ± standard deviation, and the comparison between the two groups was performed by test. Counting data are expressed as the number of cases and percentages and compared using chi-square test or Fisher's exact test. Linear regression analysis was used to explore causal relationships. $P \leq 0.05$ was considered as statistically significant.
## 3.1. Patient characteristics
A total of 46 patients aged 14–32 years with a diagnosis of TS were included in the study, and the basic characteristics of the study participants is shown in Table 1. In total of 18 patients had a chromosome karyotype of 45, X, and the rest had other chromosome karyotypes, including mosaicism, X chromosome aberration and contained Y chromosome, among which 3 patients had no deletion of the SHOX gene. In line with existing guidelines for the treatment of TS, all the participants received hormone replacement treatment (HRT).
**Table 1**
| Subject | Value |
| --- | --- |
| Age (years) | 20.57 ± 0.67 |
| Height (m) | 1.50 ± 0.01 |
| Weight (kg) | 51.94 ± 1.43 |
| BMI (kg/m2) | 23.13 ± 0.58 |
| Age at diagnosis (years) | 14.35 ± 5.36 |
| Duration of GHT | 4.47 ± 3.18 |
| Duration of HRT | 3.70 ± 3.28 |
| Karyotype | Karyotype |
| Monosomy X (45, X) [n (%)] | 18 (39.1%) |
| Other alterations [n (%)] | 28 (60.9%) |
| Short stature homeobox | Short stature homeobox |
| Exists [n (%)] | 3 (6.5%) |
| Missing [n (%)] | 43 (93.5%) |
| FSH (mIU/ml) | 34.21 (8.89, 67.16) |
| LH (mIU/ml) | 7.99 (3.09, 17.52) |
| E2 (pmol/l) | 93.00 (37.00, 134.50) |
| INS (Uu/mL) | 7.50 (5.78, 10.15) |
| TC (mmol/l) | 5.27 ± 0.16 |
| TG (mmol/l) | 1.62 ± 0.11 |
| HDL-C (mmol/l) | 1.61 ± 0.05 |
| LDL-C (mmol/l) | 2.98 ± 0.13 |
| TG/HDL-C | 1.06 ± 0.83 |
| TSH (mIU/l) | 2.16 (1.75, 2.95) |
| FT4 (pmol/l) | 13.46 ± 0.24 |
| TPO-Ab (IU/ml) | 1.46 (0.08, 247.68) |
| WHR | 0.93 ± 0.02 |
| BF (%) | 35.16 ± 0.90 |
| Whole Body BMD (g/cm2) | 0.89 ± 0.01 |
| Lumbar Spine BMD (g/cm2) | 0.77 ± 0.02 |
| Femur Neck BMD (g/cm2) | 0.65 ± 0.02 |
| Total Hip BMD (g/cm2) | 0.65 ± 0.02 |
## 3.2. Audiological examination
A total of 92 ears of 46 patients underwent pure tone audiometry and tympanometry (Table 2). It was found that 9 ($19.6\%$) had HL, including 1 ($2.2\%$) with mild conductive hearing loss, 5 ($10.9\%$) with mild sensorineural hearing loss, 3 ($6.5\%$) with moderate sensorineural hearing loss, but none had moderately severe or higher hearing loss. In addition, 6 ($13.0\%$) only had high-frequency hearing loss in the PTA; 36 ($78.2\%$) had “A” type tympanogram, 7 ($15.2\%$) “Ad” type, 1 ($2.2\%$) “B” type, and 2 ($4.4\%$) “C” type. Compared with other karyotypes (including mosaicism, chromosomal abnormality, and Y chromosome-containing type), 45, X haplotype patients had higher hearing thresholds at 1, 2, and 4 KHz, and hearing thresholds at 1 and 2 KHz were higher than the normal level ($P \leq 0.05$) (Table 3). However, there was no significant correlation between chromosome karyotype and the type and degree of HL (Table 4). In addition, three TS patients without SHOX gene deletion had normal audiological examinations, but SHOX gene abnormalities had no association with the pure tone audiometry and tympanogram results (data not shown).
## 3.3. Associated factors for hearing loss in TS subjects
The results showed that there were statistically significant differences in age and height between the normal hearing group and hearing loss group (as defined by PTA) ($P \leq 0.05$) (Table 5). The age of the hearing loss group was higher than that of the normal hearing group, while the height of the hearing loss group was lower than that of the normal hearing group. Obesity, insulin resistance, blood lipid levels, thyroid function, estrogen levels, and BMD were not associated with HL in TS, and growth hormone treatment (GHT) and hormone replacement treatment (HRT) were not significantly associated with hearing status in TS. Linear regression analysis was performed on age and pure tone average of each frequency, and it was found that with an increase in age, the hearing thresholds and average hearing thresholds of 2, 4, and 8 KHz increased ($P \leq 0.05$) (Table 6, Figure 1).
## 4. Discussion
Due to complete or partial deletion of the X chromosome, patients with TS often have associated developmental abnormalities and malformations, and different cytogenetic changes can lead to clinical heterogeneity. In addition to gonadal dysgenesis and short stature, with the increase of age, TS women often encounter a variety of complications, such as cardiovascular disease, autoimmune disease, metabolic disease, osteoporosis, and neurocognitive deficits [4].
Hearing problems can occur at all ages in TS patients, and the incidence is higher than that of normal people (5, 20–22). The types of HL in TS patients included conductive hearing loss (CHL), sensorineural hearing loss (SNHL), and mixed hearing loss (MHL). The study found that the incidence of TS combined with HL varies greatly in different regions and populations. The occurrence of CHL is related to pharyngeal canal and craniofacial dysplasia [23]. The incidence of CHL in TS patients is about 6–$43\%$, and it is often secondary to chronic or recurrent otitis media (23–25), which is more common in childhood and adolescence, and continues to develop into adulthood [23]. At present, the diagnostic criteria and definition of the intermediate frequency range of SNHL vary, and the incidence of SNHL varies greatly among different studies. Some scholars further distinguished mid-frequency hearing loss (MFHL) or high frequency hearing loss (HFHL) as distinct forms of HL. Chan et al. [ 26] stipulated that the intermediate frequency was between 1–2 k Hz, while Verver et al. [ 27] believed that the intermediate frequency was between 0.5–2 k Hz. Studies have found that the incidence of SNHL in women with TS is about 9–$66\%$, often presenting as HFHL and/or MFHL. The pure tone audiometry shows a gradual descent and steep descent curve, which is similar to age-related HL, but the onset age is much younger and the progression rate is much faster [20]. This progressive HL is more common in people with a history of recurrent ear infections in the early stage, and is usually associated with a defect in the outer helix hair cells in the middle and lower part of the cochlea, which is aggravated with age [28]. MHL occurs in 3–$13\%$ of TS patients, and is more common in patients aged 30 years and above [10, 28]. In this study, 46 TS patients aged 14–32 years were studied. The incidence of CHL was $2.2\%$, SNHL $17.4\%$, HFHL $13.0\%$, and MHL was not found, presenting with age-related HL. Compared with previous studies, the incidence of various types of HL in TS patients in this study was lower, which may be related to race, age composition ratio and small sample size. In addition, we did not compare HL levels with the normal population, however based on relevant reference ranges levels of HL were higher in our TS population.
Young and middle-aged women with TS often present with progressive HL, which intensifies rapidly in adulthood (20–22). HL is mainly composed of two causes: MFHL, which may be related to genetic factors; and age-related HFHL, which may be affected by estrogen deficiency [21]. The incidence of HL in TS women was much higher than that in the general population. Regardless of initial age, hearing level, karyotype, or the presence or absence of MFHL, TS patients had a similar rate of de-cline as normal women aged 70–90 years, especially in the high-frequency region, with a decrease of 0.8–2.2 dB per year [21]. High-frequency SNHL is common in TS patients [5, 29]. Compared with chimeric TS patients, 45, X haplotype TS patients had a more significant age-dependent increase in high-frequency hearing threshold [29]. Morimoto et al. [ 29] studied the hearing status of 33 female TS patients aged 8–40 years and found that more than $60\%$ of TS patients suffered from high-frequency SNHL, and the incidence increased with age, and is usually associated with defect in the outer helix hair cells in the middle and lower part of the cochlea [30, 31]. Animal experiments [32, 33] showed that “Turner mice” had ear and hearing problems similar to those of TS women, SNHL was usually accompanied by MFHL and early HFHL, and the loss of outer hair cells was obvious in the cochlear basal turn of Turner mice, and the expression of estrogen receptor in outer hair cells of the helical organ was weakened. These results suggest that estrogen and its receptor have protective effects on the development of cochlea. The results of our study also found that TS patients presented with age-related HL in middle and high frequency, and 45, X karyotypes were more common, indicating that HL was related to age and chromosome karyotype. Given the age related incidence of HL in TS patients we would recommend yearly follow up for these patients, in line with Chinese guidelines for people at high risk of HL.
Previous studies have observed that karyotype is related to the type and degree of HL in TS patients. It has been reported that those with a TS karyotype of 45, X are more likely to suffer from SNHL, compared to mosaicism or structural anomaly, patients with a monosomy 45, X or isochromosome (both have a total deletion of the short (p) arm of the X-chromosome) had more pronounced HL, which supports the hypothesis that hearing can be affected due to the loss of the X chromosome p arm, and the loss of different parts of the short arm of the X chromosome causes different phenotypes of HL [27, 33, 34]. Barrenas et al. [ 35] found that the incidence of SNHL and auricular abnormalities increased significantly with the proportion of 45, X cells in TS individuals, suggesting that the disease was not only caused by specific X chromosome deletion, but also related to the cell cycle extension caused by chromosome aberration itself. Hearing is affected by dysfunctions of the outer, middle and inner ears caused by growth disorders during development. However, Bazilio et al. [ 28], Bois et al. [ 36] found that there was no statistically significant correlation between karyotype and the type or degree of HL. This study explored the relationship between chromosome karyotype and hearing in TS patients, and it was found that chromosome karyotype was unrelated to the type and degree of HL. Compared with other chromosome karyotypes (including chimeric type, abnormal chromosome structure type and body type with Y staining), hearing frequency of 45, X haplotypes increased at 1,000, 2,000, and 4,000 Hz. It suggests that the HL of TS may be related to the short arm deletion of X chromosome. In addition, our study found that audiology was normal in the 3 TS patients without deletion of SHOX gene, supporting the hypothesis that the absence of growth regulation genes, such as SHOX gene, may be associated with the occurrence of early HL, suggesting that karyotype or SHOX gene can be used as a predictor of hearing problems in TS.
Studies have shown that the incidence of HL with TS women is much higher than that in women of the same age, and age-related hearing impairment may be affected by estrogen deficiency [10, 21, 29]. It has been observed that the incubation period of the auditory brainstem response (ABR) in women is shorter than that in men, but the incubation period and amplitude of postmenopausal women are the same as those of men of the same age [8]. In addition, HL in women continues to decline after menopause, with hearing decline being more rapid after menopause than before [9]. The difference of hearing level between males and females and the change of hearing level in perimenopausal women suggests a protective effect of estrogen on hearing. TS is a chromosome aberration caused by the deletion of all or part of the X chromosome. The main feature is ovarian hypoplasia, little or no endogenous estrogen production, often accompanied by ear and hearing problems. “ Turner mice”, as a model of TS ear problems, showed otitis media and early HL, and the ABR of Turner mice showed progressive HL in high-frequency ranges, and was related to age [32, 33]. Another study found that the hearing of female mice before menopause is better than that of male mice, but this advantage is weakened after menopause, which indicates that estrogen has certain effects on hearing [7, 37]. However, in the current study there was no correlation between hearing loss and estrogen levels in TS patients. The reason for this is unclear but may relate to all patients receiving HRT. In addition, TS patients often display additional different non-sex chromosomal abnormalities that may affect hearing associated genes, although this association required further investigation.
TS patients often also present with endocrine abnormalities, metabolic diseases and abnormal bone metabolism. Approximately 30–$50\%$ of TS patients suffer from thyroiditis or hypothyroidism, 40–$45\%$ are positive for peroxidase antibodies, and have an increased risk for development of autoimmune diseases (38–40). Congenital and acquired hypothyroidism is a common cause of SNHL, mainly manifested by changes in audiological examinations such as brainstem auditory evoked potentials and hearing thresholds, these changes will occur after timely thyroid hormone replacement therapy [12]. Animal models also found that reduced thyroid hormone secretion affects the development of the inner ear, leading to changes in the structure of the inner ear cochlea, which leads to SNHL (41–43). At present, there is no relevant research on the relationship between hearing level and thyroid function in patients with TS, and our data demonstrate no association between HL and thyroid function, further studies are needed to assess the relationship.
Metabolic syndrome is more common in TS, and it has been reported that about $50\%$ of adolescents with TS have hypercholesterolemia [4, 44]. Previous studies have shown that lipid profiles and obesity are risk factors for HL [13, 14, 45]. Metabolic syndrome and its related factors are risk factors for HL. With an increase in the number of related factors, such as abdominal obesity, hypertension, increased fasting blood glucose, in-creased TG level and decreased HDL-C level, the risk of HL will increase, and the number of metabolic syndrome related factors is significantly correlated with hearing threshold (46–48). It has also been shown that patients with mosaic karyotypes are more likely to develop dyslipidemia than patients with other karyotypes, while patients with circular chromosomes are more likely to develop metabolic disorders [22]. Alvarez-Nava et al. [ 49] found that metabolic syndrome was a risk factor for SNHL in adult patients with TS and that reducing the severity of metabolic syndrome may help reduce the progression of SNHL. Our study compared the blood lipid, insulin levels, and BMI of the TS between the normal hearing group and the hearing loss group, and found that TS with HL was not associated with insulin resistance, obesity, and dyslipidemia, may be related to lifestyle or drug treatment interventions, such as metformin, metabolic abnormalities, and further studies are needed to explain the potential pathological mechanism between metabolic abnormalities and SNHL.
The occurrence time of abnormal bone metabolism of TS is about 20–30 years earlier than that of normal women, and the risk of fracture is about $25\%$ higher than that of normal people [50, 51]. Previous studies have found that osteoporosis and low BMD can lead to HL, which may be associated with abnormal bone metabolism in the temporal bone, including loss of bone mass in the ossicular chain in the middle ear and osseous structures in the inner ear such as the bone labyrinth (15–17, 52). Yueniwati [53] found that BMD of cochlea, ossicles, femur or spine was negatively correlated with hearing threshold, indicating that abnormal bone metabolism may lead to HL. Current studies have found that decreased HL and BMD increase the risk of fracture in women with TS [54, 55], while no study has confirmed that decreased BMD in women with TS increases the risk of HL. Previous studies have found that HRT can effectively improve the BMD levels of the whole body, femoral neck and hip in TS patients [56]. In this study, it was found that after HRT in TS patients, there was no statistical difference in BMD between the normal hearing group and the hearing loss group, suggesting that HRT can improve BMD of patients to a certain extent, but there was no significant improvement in hearing level of patients. Therefore, hearing abnormalities in TS patients cannot be explained only by insufficient estrogen level, and it may be necessary to expand the sample size and conduct grouping studies to explore the correlation between clinical phenomena of HL and related etiology in TS.
## 5. Conclusion
TS is often manifested as age-related MFHL and HFHL, and the incidence of HL increases with age. Compared with other karyotypes, patients with 45, X haplotype have an increased risk of MFHL; karyotype or SHOX gene can be used as a predictor of hearing problems in TS. For patients with confirmed TS, endocrinologists should pay attention to HL, conduct regular (yearly) endoscopic examination and hearing monitoring, strengthen patient education, and reduce the impact of TS on their study, psychology and quality of life.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Guangzhou Women and Children's Medical Center (protocol code 2016042019 and May 2016). Informed consent was obtained from all subjects involved in the study. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
## Author contributions
Conceptualization: LL. Methodology: LL and XW. Formal analysis: HL, XW, SQ, FL, and YC. Data curation and writing—original draft preparation: HL. Writing—review and editing and supervision: LL and GEL. Funding acquisition: GEL. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: The circadian clock in the piriform cortex intrinsically tunes daily changes
of odor-evoked neural activity
authors:
- Shunsuke Takeuchi
- Kimiko Shimizu
- Yoshitaka Fukada
- Kazuo Emoto
journal: Communications Biology
year: 2023
pmcid: PMC10043281
doi: 10.1038/s42003-023-04691-8
license: CC BY 4.0
---
# The circadian clock in the piriform cortex intrinsically tunes daily changes of odor-evoked neural activity
## Abstract
The daily activity in the brain is typically fine-tuned by the circadian clock in the local neurons as well as by the master circadian clock in the suprachiasmatic nucleus (SCN) of the hypothalamus. In the olfactory response, odor-evoked activity in the piriform cortex (PC) and olfactory behavior retain circadian rhythmicity in the absence of the SCN, yet how the circadian rhythm in the PC is achieved independently of the SCN remains elusive. Here, to define neurons regulating the circadian rhythm of the odor-evoked activity in the PC, we knocked out the clock gene Bmal1 in a host of specific neurons along the olfactory circuit. We discovered that Bmal1 knockout in the PC largely abolishes the circadian rhythm of the odor-evoked activity. We further showed that isolated PC exhibits sustained circadian rhythms of the clock gene Per2 expression. Quantitative PCR analysis revealed that expression patterns of multiple genes involved in neural activity and synaptic transmission exhibit circadian rhythm in the PC in a BMAL1-dependent manner. Our findings indicate that BMAL1 acts intrinsically in the PC to control the circadian rhythm of the odor-evoked activity in the PC, possibly through regulating expression patterns of multiple genes involved in neural activity and transmission.
Loss of the clock gene, Bmal1, in the piriform cortex largely abolishes the circadian rhythm of odor-evoked activity in mice, suggesting a broader regulatory role for Bmal1 in olfactory neural activity and transmission.
## Introduction
Many organs including the brain exhibit circadian rhythms in their biological processes. Recent physiological and behavioral studies showed that the circadian rhythmicity is observed in multiple neural functions including memory & learning, emotional response, and sensory activity such as audition and olfaction1–4. These circadian rhythms of neural activities are typically generated and maintained through a series of transcription factors called clock genes. The transcription/translation levels of these clock genes oscillate in a 24-h cycle, via the negative transcription-translation feedback loop regulation5–7. Indeed, knocking out the core clock genes such as Bmal1 and Period$\frac{1}{2}$ (Per$\frac{1}{2}$) largely abolishes the circadian rhythms of neural activities8,9, underscoring their crucial roles in the circadian rhythms of neural activities.
In mammals, the suprachiasmatic nucleus (SCN) of the hypothalamus is widely believed to be the master circadian oscillator orchestrating the clock of the entire body10–12. Consistently, the core clock genes are abundantly expressed in the SCN13,14. On the other hand, a growing body of evidence suggests that the core clock genes are widely expressed in virtually all cells of the entire body, and that certain neural activities show circadian rhythms independent of the SCN15,16. One such example is the olfactory circuit, in which odor information is initially detected by the olfactory sensory neurons in the olfactory epithelium, then relayed to the mitral/tufted cells in the olfactory bulb, and eventually processed in the pyramidal neurons of the piriform cortex (PC)17,18. Odor-evoked activity in the PC shows a circadian rhythm with the highest response at night1, which is paralleled by the olfactory discrimination accuracy19,20. This circadian rhythmicity of the odor-evoked activity in the PC is unaffected by surgical removal of the SCN, although the oscillation phase tends to be affected1. These data suggest that the circadian rhythmicity of the odor-evoked activity in the PC is generated by circadian oscillators outside of the SCN. Consistent with this notion, surgical removal of the olfactory bulb in mice partially disturbs the circadian rhythm of the odor-evoked activity in the PC1. It is, however, still unknown what neural populations in the olfactory circuit contributes to the circadian rhythm of the odor-evoked activity in the PC, partially due to less accessibility of the surgical approach.
In this study, by combining multiple Cre recombinase-expressing mice and adeno-associated virus (AAV) transfection, we knocked out the clock gene Bmal1 in specific sets of neurons in the olfactory circuit. We show that BMAL1 in the PC is required for the circadian rhythm of the odor-evoked activity. Furthermore, the isolated PC ex vivo maintained the circadian oscillation of Per2 expression, another core clock gene. Interestingly, we found that expression levels of multiple genes related to neural activity and synaptic transmission exhibit circadian rhythms in the PC in a BMAL1-dependent manner. These findings suggest that the intrinsic molecular clock in the PC is critical for the circadian rhythm of the odor-evoked neural activity, and hint at the possibility that the intrinsic clock might exert such function through transcriptional regulation of the molecules related to neural activity and synaptic transmission.
## Odor-evoked neural activity in the PC exhibits a circadian rhythm
To investigate the circadian rhythm of the odor-evoked activity in the mouse brain, we presented cedar oil to the mouse as described previously1, and quantified the number of c-Fos positive cells21 in the PC for six timepoints throughout the day (Fig. 1a). Consistent with previous studies1,22, by comparing the number of c-Fos positive cells among six timepoints, we observed a peak at the subjective night CT16 (Fig. 1b, Supplementary Fig. 1a). Of note, the number of c-Fos positive cells showed no obvious correlation with other external conditions such as humidity and temperature (Supplementary Fig. 1b), suggesting that the circadian rhythm of odor-evoked PC activity is unrelated to external stimuli, but rather regulated through an internal mechanism. Additionally, mice exposed to another neutral odorant limonene[-] showed a significantly higher number of c-Fos positive cells in the PC at CT16 compared to CT4 (Supplementary Fig. 1c), comparable to the response to cedar oil. Thus, the circadian rhythm of the odor-evoked activity in the PC is not specific to cedar oil. Fig. 1Circadian rhythms of neural activity are most evident in the PC.a Schematic illustration of the odor-presentation experiment for c-Fos quantification. 100 µL of cedar oil (1:1000 dilution in paraffin oil) was presented at six different timepoints for 5 min. b The number of c-Fos positive cells in the PC of mice exposed to the odor at six timepoints (CT0, 4, 8, 12, 16, 20). $$n = 4$$, 6, 3, 4, 8, 4 for CT0, CT4, CT8, CT12, CT16, CT20, respectively. $$p \leq 0.001185$$, one-way ANOVA. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. c Representative whole-brain reconstruction of c-Fos positive cells in mice that were kept in home cages and then sacrificed at two timepoints (CT4, CT16). Red dots indicate individual c-Fos positive cells. d Quantification of the number of c-Fos positive cells in the whole brain. $$n = 4$$ and 2 for CT4 and CT16, respectively. e The number of c-Fos positive cells in five sensory cortical areas: visual cortex (V1), auditory cortex (A1), gustatory cortex, and PC in mice that were kept in home cages and then sacrificed at two timepoints (CT4, CT16). $$n = 4$$ and 2 for CT4 and CT16, respectively.
We next assessed whether odor-evoked neural activity might specifically fluctuate in the PC. To this end, we collected brain samples from subjective day CT4 and subjective night CT16 and quantified the number of c-Fos positive cells in serial sections spanning the whole brain. We found that the total number of c-Fos positive cells was higher at CT16 compared to CT4 (Fig. 1c, d). We further quantified the number of c-Fos positive cells in each sensory cortex by mapping the c-Fos positive cells onto a reference mouse brain of the Allen Brain institute three-dimensionally23. As a result, while the PC showed a higher number of c-Fos positive cells at CT16 (Fig. 1e), the number of c-Fos positive cells was scarce both at CT4 and CT16 in the primary visual cortex (V1), primary auditory cortex (A1) and gustatory cortex (Fig. 1e). The circadian fluctuation of neural activity is thus most obvious in the PC among sensory cortices. Interestingly, we found multiple brain areas in which c-Fos-positive cells were significantly higher at CT16 compared to CT4 including the somatomotor cortex, cortical subplate, basolateral amygdala, and hypothalamus (Supplementary Fig. 1d).
## Circadian rhythmicity of the odor-evoked activity in the PC are intrinsically maintained independently of the SCN
Circadian rhythms of neural activities are widely thought to be generated by the SCN10–12 and the clock genes such as Bmal18,9. While a previous report has suggested that the circadian rhythm of the odor-evoked activity in the PC operates independent of the SCN1, this report solely relied on surgical removal of the SCN, which could be confounded by inevitable damages to other brain structures. We thus sought to test whether BMAL1 expression in the SCN contributes to the circadian rhythm of the odor-evoked activity in the PC with less invasive methodologies. To this end, we took advantage of the evidence that the SCN is mostly comprised of GABAergic neurons, and that GABAergic neurons are required for the circadian rhythm of SCN activity24. Specifically, we conditionally knocked out the clock gene Bmal1 in GABAergic neurons by crossing Vgat-Cre mice25 against Bmal1-floxed mice in which loxP sequences are inserted in the coding region of the *Bmal1* gene26. As expected, the number of BMAL1-positive cells in the SCN was significantly reduced in these conditional KO mice (“Vgat-Bmal1 KO mice”) compared to control mice (Fig. 2a). While control mice showed a clear circadian rhythm in locomotor activity both under the LD condition (light condition is indicated by yellow-shaded area) and the following DD condition (Fig. 2b, left), Vgat-Bmal1 KO mice failed to show a circadian rhythm under the DD condition (Fig. 2b, right), suggesting that the circadian rhythm in the SCN was effectively disrupted in Vgat-Bmal1 KO mice. By contrast, the number of c-Fos positive cells in the PC following cedar oil presentation retained circadian rhythmicity (Fig. 2c), suggesting that BMAL1 expression in GABAergic SCN neurons is dispensable for the circadian rhythmicity of the odor-evoked activity in the PC. We note that the number of c-Fos positive cells peaked at CT4 in Vgat-Bmal1 KO mice while this number peaked at CT16 in control mice (Fig. 1d), consistent with a previous study in which SCN was surgically removed1. These data suggests that BMAL1 in GABAergic SCN neurons is dispensable for rhythm generation but might contribute to phase control of the circadian rhythm of the odor-evoked activity in the PC.Fig. 2Bmal1 KO in the SCN fails to influence the circadian rhythmicity of the odor-evoked neural activity in the PC.a Representative images of the suprachiasmatic nucleus (SCN) of Bmal1-control mice and Vgat-Bmal1 KO mice, immunohistochemically stained with BMAL1 antibody. The ratios of BMAL1 + cells over DAPI + cells were quantified in the SCN. b Representative locomotor activity patterns of Bmal1-control mice and Vgat-Bmal1 KO mice. Yellow shades and gray shades indicate light ON and OFF respectively. c Quantification of c-Fos positive cells in the PC of Vgat-Bmal1 KO mice which underwent odorant exposure identical to Fig. 5A. $$n = 3$$, 2, 4, 2 for CT4, CT8, CT16, CT20, respectively. $$p \leq 0.03224$$, one-way ANOVA. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile.
## Bmal1 KO in neurons containing GCs and PC disrupts the circadian rhythm of the odor-evoked activity in the PC
Since we failed to detect contribution of BMAL1 in the SCN to the circadian rhythmicity of the odor-evoked activity of the PC, we next asked whether BMAL1 functions inside the olfactory circuit. To this end, against Bmal1-floxed mice, we crossed multiple transgenic mice expressing Cre recombinase known to label specific neuronal populations along the olfactory circuit, including Omp-Cre mice27, Pcdh21-CreER mice28, and Emx1-Cre mice29. To check precisely where and how efficiently Bmal1 was knocked out in these conditional KO mice (“Omp-Bmal1 KO mice”, “Pcdh21-Bmal1 KO mice”, “Emx1-Bmal1 KO mice”), we first conducted immunohistochemical analyses in the olfactory circuit including mitral/tufted cells, granule cells of the olfactory bulb, and the PC. In the mitral/tufted cell layer, we analyzed the ratio of BMAL1-positive cells against Tbx21-positive mitral/tufted cells and found that $97.6\%$ of Tbx21-positive cells were BMAL1-positive in Bmal1-control mice while this ratio dropped to 64.9 ± $2.29\%$ and 7.02 ± $5.85\%$ in Pcdh21-Bmal1 KO mice and Emx1-Bmal1 KO mice, respectively (Fig. 3a). In the granule cell layer of the olfactory bulb, we analyzed the ratio of BMAL1-positive cells in five different depths since different granule cell types likely reside in the different depths30,31. As a result, while >$90\%$ of Nissl-positive neurons was BMAL1-positive in Bmal1-control mice and Pcdh21-Bmal1 KO mice, this ratio was reduced in all depths of the Emx1-Bmal1 KO mice (Fig. 3b). In the PC, while the ratio of BMAL1-positive cells was 98.6 ± $0.69\%$ in Bmal1-control mice, this ratio was reduced to 90.2 ± $3.78\%$ and 25.3 ± $5.40\%$ in Pcdh21-Bmal1 KO mice and Emx1-Bmal1 KO mice, respectively (Fig. 3c). Thus, by utilizing the three Cre-lines, we successfully managed to knock out Bmal1 in different neuronal subsets of the olfactory circuit (Fig. 3d).Fig. 3Bmal1 KO in GCs and PC partially disrupt the circadian rhythm of the odor-evoked activity in the PC.a Representative images of the mitral/tufted cell layer in the olfactory bulb of Bmal1-floxedfl/fl mice (Bmal1-control), Pcdh21-CreERTg/+; Bmal1-floxedfl/fl mice (Pcdh21-Bmal1 KO) and Emx1-CreTg/+; Bmal1-floxedfl/fl mice (Emx1-Bmal1 KO) immunohistochemically stained with BMAL1 antibody and mitral/tufted cell marker Tbx21 antibody. Arrowheads indicate BMAL1-; Tbx21+ cells. The ratios of BMAL1 + cells over Tbx21+ cells were $97.6\%$, 64.9 ± $2.29\%$, 7.1 ± $5.85\%$ (mean ± standard deviation), in Bmal1-control mice, Pcdh21-Bmal1 KO mice, and Emx1-Bmal1 KO mice, respectively. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. b Representative images of the granule cell layer in the olfactory bulb of Bmal1-control mice, Pcdh21-Bmal1 KO mice and Emx1-Bmal1 KO mice immunohistochemically stained with BMAL1 antibody and neuron marker Nissl. The ratio of BMAL1 + cells over Nissl+ cells were quantified in granule cell layers divided into five layers. $$n = 3$$ for each genotype, 91.1 ± 5.24, 99.0 ± 1.75, 53.5 ± $13.6\%$; 100.0 ± 0.00, 98.1 ± 0.09, 64.1 ± $5.53\%$; 97.7 ± 4.03, 98.6 ± 1.23, 73.9 ± $6.52\%$; 98.6 ± 1.18, 97.8 ± 2.68, 84.6 ±;$8.55\%$; 96.9 ± 2.73, 98.3 ± 1.63, 82.1 ± $6.72\%$ (mean ± standard deviation), in layers 1~5 of Bmal1-control mice, Pcdh21-Bmal1 KO mice, and Emx1-Bmal1 KO mice, respectively. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. c Representative images of the PC in Bmal1-control mice, Pcdh21-Bmal1 KO mice, and Emx1-Bmal1 KO mice immunohistochemically stained with BMAL1 antibody and DAPI. The ratios of BMAL1 + cells over DAPI + cells were 98.6;± $0.69\%$, 90.2 ± $3.78\%$, 25.3 ± $5.43\%$ (mean ± standard deviation), in Bmal1-control mice, Pcdh21-Bmal1 KO mice, and Emx1-Bmal1 KO mice, respectively. $$n = 4$$, 2, 4 for Bmal1-control mice, Pcdh21-Bmal1 KO mice, and Emx1-Bmal1 KO mice, respectively. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. d Summary of the regions where Bmal1 was knocked-out in each Cre-line. In Omp-Bmal1KO mice, Bmal1 was knocked out in olfactory sensory neurons. In Pcdh21-Bmal1 KO mice, Bmal1 was knocked out in mitral/tufted cells. In Emx1-Bmal1 KO mice, Bmal1 was knocked out in mitral/tufted cells, granule cells, and PC neurons. e The number of c-Fos positive cells in the PC of Bmal1-control mice, Omp-Bmal1 KO mice, Pcdh21-Bmal1 KO mice, Emx1-Bmal1 KO mice exposed to an odorant. For Bmal1-control mice, $$n = 13$$ each for CT4 and CT16. For Omp-Bmal1 KO mice, $$n = 11$$ and 9 for CT4 and CT16, respectively. For Pcdh21-Bmal1 KO mice, $$n = 11$$ and 8 for CT4 and CT16, respectively. For Emx1-Bmal1 KO mice, $$n = 9$$ each for CT4 and CT16. $$p \leq 0.005072$$, 0.006549, 0.02052, 0.1615 for Bmal1-control mice, Omp-Bmal1 KO mice, Pcdh21-Bmal1 KO mice, Emx1-Bmal1 KO mice, respectively. Wilcoxon rank-sum test with Bonferroni correction. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile.
We next presented cedar oil to these mice and quantified the number of c-Fos positive cells in the PC. First, in the control group where Cre recombinase is not expressed (Bmal1-control mice), the number of c-Fos positive cells in the PC was significantly higher at CT16 compared to CT4 (Fig. 3e). Similarly, the number of c-Fos positive cells in the PC was significantly higher at CT16 compared to CT4 both in Omp-Bmal1 KO mice and in Pcdh21-Bmal1 KO mice (Fig. 3e). By contrast, we observed no significant difference in the number of c-Fos positive cells in the PC between CT4 and CT16 in Emx1-Bmal1 KO mice (Fig. 3e). Further, Pcdh21-Bmal1 KO mice and Emx1-Bmal1 KO mice both showed rhythmic circadian locomotor activity in a constant dark condition (Supplementary Fig. 2), suggesting that the SCN-dependent circadian regulation was maintained in both KO mice. Given that Bmal1 is knocked out in the mitral/tufted cells, granule cells, and the PC in Emx1-Bmal KO mice and that Pcdh21-Bmal1 KO mice in which Bmal1 is knocked out in the mitral/tufted cells retained circadian rhythm of the odor-evoked activity in the PC, our data suggest that BMAL1 in the granule cells and/or the PC is required for the circadian rhythm of the odor-evoked activity in the PC.
## AAV-mediated PC-specific Bmal1 KO abolishes the circadian rhythm of the odor-evoked activity in the PC
To further define the neural population required for the circadian rhythm of the odor-evoked activity in the PC, we limited Bmal1 KO into a specific population of the olfactory circuit by expressing Cre recombinase via adeno-associated virus (AAV) injection. Specifically, we generated AAV where Cre recombinase and red fluorescent protein mCherry are simultaneously expressed downstream of the CaMKIIα promoter. CaMKIIα is known to be expressed both in the granule cells of the olfactory bulb and in the pyramidal neurons of the PC32–34. When we injected this AAV to the granule cell layers of the olfactory bulb (Fig. 4a), mCherry expression was largely observed in the deeper layers of the granule cell, and correspondingly BMAL1 expression was especially weak in the deeper layers (Fig. 4b, c, Supplementary Fig. 3a, b). We validated that mCherry is not expressed in cells outside of the granule cell layers, i.e. mitral/tufted cells (Supplementary Fig. 3a). When we injected the AAV to the PC (Fig. 4d), 55.5 ± $6.14\%$ of Nissl-positive cells expressed mCherry (Supplementary Fig. 3c, d), and only 13.5 ± $8.59\%$ of Nissl-positive cells expressed BMAL1 (Fig. 4e, f).Fig. 4PC-specific Bmal1 KO abolishes the circadian rhythm of the odor-evoked activity.a AAVDJ-CaMKII-iCre-T2A-mCherry injection to the olfactory bulb (GC-Bmal1 KO). b Representative images of the granule cell layer in the olfactory bulb of GC-Bmal1 KO mice, immunohistochemically stained with BMAL1 antibody. c The ratio of BMAL1 + cells over Nissl+ cells were quantified in granule cell layers divided into five layers. For Bmal1-control mice: 91.1 ± $5.24\%$, 100.0 ± $0.00\%$, 97.7 ± $4.03\%$, 98.6 ± $1.18\%$, 96.9 ± $2.73\%$, (mean ± standard deviation), in layers 1~5, respectively. For GC-Bmal1 KO mice: 92.5 ± $7.81\%$, 96.8 ± $2.80\%$, 69.3 ± $15.8\%$, 17.1 ± $6.82\%$, 36.6 ± $8.85\%$, (mean ± standard deviation), in layers 1~5, respectively. $$n = 3$$ and 4 for Bmal1-control mice and GC-Bmal1 KO mice, respectively. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. d AAVDJ-CaMKII-iCre-T2A-mCherry injection to the PC (PC-Bmal1 KO). e Representative images of the PC of PC-Bmal1 KO mice, immunohistochemically stained with BMAL1 antibody. f The ratios of BMAL1 + cells over Nissl+ cells were quantified in the PC. 98.6 ± $0.69\%$, 13.5 ± $8.59\%$ (mean ± standard deviation) for Bmal1-control mice and PC-Bmal1 KO mice, respectively. $$n = 4$$ and 7 for Bmal-control mice and PC-Bmal1 KO mice, respectively. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. g The number of c-Fos positive cells in the PC of olfactory bulb-injected granule cell conditional KO mice exposed to an odorant. $$n = 9$$ each for CT4 and CT16. $$p \leq 0.02394$$, Wilcoxon rank-sum test. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. h The number of c-Fos positive cells in the PC of PC-injected with AAVDJ-CaMKII-mCherry (PC Control) and PC conditional KO mice (PC-Bmal1 KO) exposed to an odorant. For PC Control, $$n = 4$$ and 6 for CT4 and CT16, respectively. $$p \leq 0.07323$$, one-way ANOVA. For PC-Bmal1 KO, $$n = 7$$, 2, 7, 3 for CT4, CT10, CT16, CT22, respectively. $$p \leq 0.9506$$, one-way ANOVA. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile.
We next presented cedar oil to these mice and quantified the number of c-Fos positive cells in the PC. In mice where Bmal1 was conditionally knocked out in granule cells (“GC-Bmal1 KO mice”), the number of c-Fos positive cells was significantly higher at CT16 compared to CT4 (Fig. 4g). On the other hand, in mice where Bmal1 was knocked out in the PC (“PC-Bmal1 KO mice”), there was no significant difference in the number of c-Fos positive cells in the PC across four timepoints (Fig. 4h). Since the number of c-Fos positive cells was significantly higher at CT16 compared to CT4 in control group in which AAV-CaMKII-mCherry was injected to the PC (Fig. 4h), AAV injection alone has no significant effect on odor-evoked activity in the PC. These data suggest that the circadian rhythm of the odor-evoked activity in the PC was abolished in these mice. PC-Bmal1 KO mice showed rhythmic locomotor activity in a constant dark condition (Supplementary Fig. 3e), suggesting that the molecular clock in the SCN is functionally intact in PC-Bmal1 KO mice. These results indicate that BMAL1 expression in the CaMKIIα-positive pyramidal neurons of the PC is required for the circadian rhythm of the odor-evoked activity in the PC.
## PC can maintain circadian rhythm of clock gene expression in an autonomous manner
Given that BMAL1 expression in the CaMKIIα-positive pyramidal neurons of the PC is responsible for the circadian rhythm of the odor-evoked activity in the PC, we reasoned that PC may be capable of self-sustaining the circadian rhythm of its clock gene expression. To test this possibility, we next conducted an ex vivo measurement of the circadian rhythm of the intrinsic clock using isolated tissues from PER2::LUC knock-in mice, in which the firefly luciferase cDNA is inserted immediately downstream of the *Per2* gene35. In brief, we generated acute slice sections of the PC and measured the luminescence level for up to 1 week in a luciferin-containing medium (Fig. 5a). As a positive control, we first confirmed that the SCN showed a sustained circadian rhythm of luminescence with a period of 24.4 ± 1.10 h (Fig. 5b) as reported previously35. Importantly, we observed sustained circadian rhythm in the PC with a period of 24.73 ± 0.43 h (Fig. 5b). There was no significant difference in the observed period between the SCN and the PC (Fig. 5c). To investigate whether action potential-based neural activity is necessary for the oscillation of clock gene expression in the PC, we applied tetrodotoxin (TTX) to the culture medium and found that the circadian rhythm damped out in the PC as well as the SCN and that the rhythm was re-activated by media change safter TTX treatment. These data suggest that neural activity is necessary for circadian oscillation of clock gene expression in the PC (Fig. 5d). Our findings thus indicate that the PC can intrinsically maintain the circadian rhythm of the clock gene expression in the absence of other brain regions. Fig. 5Clock gene expression in the PC retains circadian rhythmicity in the absence of SCN.a Schematic illustration of luciferase assay. The PC and suprachiasmatic nucleus were cut out from PER2::LUC mice and incubated in a luciferin-containing medium for up to 1 week. b Luminescence levels of the suprachiasmatic nucleus and the PC. Lines indicate the mean luminescence levels, and the shade indicates the mean ± standard error. c Mean period of the suprachiasmatic nucleus and PC. Periods were calculated by the Lomb-Scargle periodogram analysis. $$n = 4$$, 24.4 ± 1.10 h; $$n = 13$$, 24.73 ± 0.43 h, for the SCN and PC, respectively. $$p \leq 0.5331$$, Wilcoxon rank-sum test. The line at the center of each boxplot depicts the median; the box depicts the third quartile and first quartile. d Representative graphs of the luminescence levels of the SCN and the PC with TTX application.
## Genes related to neural activity show circadian rhythms in the PC in a BMAL1-dependent manner
Given that clock genes including Bmal1 and Per2 encode transcriptional regulators, the molecular clock in the PC is likely to regulate neural activity in a circadian manner through downstream gene expression. A previous study36 identified a set of genes acting downstream of the clock genes in the SCN, and we reasoned that at least some of these genes are likely to operate similarly in the PC. We thus extracted nine candidates from this set of genes deemed related to neural function through our gene ontology analyses and measured the mRNA levels of these genes in the PC at six timepoints throughout the day (Fig. 6a, Supplementary Table 1). We found that seven out of nine genes—Avpr1a, Cacna2d3, Chrnb2, Clcn4, Gad1, Snap25, and SynI—showed circadian rhythms of expression levels, and all seven peaked at CT8 (Fig. 6b). Next, we investigated whether the mRNA levels of these seven genes are regulated through BMAL1 transcription regulation. To this end, we collected the PC from Emx1-Bmal1 KO mice and quantified the mRNA levels of these genes. First, we confirmed that the mRNA levels of Bmal1 were significantly suppressed compared to wild-type mice (Supplementary Fig. 4). Importantly, we failed to detect circadian rhythms in the seven genes that showed circadian rhythm in the wild-type mice (Fig. 6c), suggesting that the expression of these genes is transcriptionally regulated through BMAL1 in the PC. Taken together, our data suggest that BMAL1 in the PC regulates the expression patterns of multiple target genes related to neural function in a circadian manner, which could contribute to intrinsic control of the odor-evoked neural activity of the PC.Fig. 6Genes related to neural activity show circadian rhythmicity in the PC.a Schematic illustration of real-time quantitative PCR. Mice were kept in 12-h Light/Dark condition for at least 3 weeks. Prior to sacrifice, mice underwent a constant dark condition, and PC and SCN were dissected for and RNA extraction, purification for real-time quantitative PCR. b Relative mRNA expression of Avpr1a, Cacna2d3, Calb2, Chrnb2, Clcn4, Gad1, Snap25, Sst, Syn1 mRNA levels in the PC of wild-type mice. $$n = 4$$, 3, 4, 4, 3, 4 for CT0, CT4, CT8, CT12, CT16, CT20, respectively. One-way ANOVA with Bonferroni correction. Error bars indicate standard error. c Relative mRNA expression of Avpr1a, Cacna2d3, Calb2, Chrnb2, Clcn4, Gad1, Snap25, Sst, Syn1 mRNA levels in the PC of Emx1-Bmal1 KO mice. $$n = 2$$, 3, 3, 2, 2, 2 for CT0, CT4, CT8, CT12, CT16, CT20, respectively.
## Discussion
In this study, we focused on the mouse olfactory system and showed that BMAL1 in the pyramidal neurons of the PC intrinsically regulates the circadian rhythm of the odor-evoked neural activity. This notion is supported by the following lines of evidence. Firstly, the circadian rhythm of the odor-evoked neural activity was maintained in Vgat-Bmal1 KO mice in which BMAL1 expression was largely diminished in GABAergic neurons including over $90\%$ of SCN neurons (Fig. 2), suggesting that the clock genes in GABAergic neurons in the SCN are dispensable for the odor-evoked neural activity in the PC. Secondly, Bmal1 KO exclusively in the CaMKIIα-positive pyramidal neurons in the PC largely abolished the circadian rhythm of the odor-evoked neural activity in the PC (Figs. 3, 4). Lastly, isolated brain slices of the PC maintained the circadian rhythm of Per2 expression at least for 3 days and this rhythm damped out upon TTX application (Fig. 5), suggesting that action potential-based neural activity likely contributes to intrinsic control of the clock gene expressions in the PC.
The present study showed an indispensable role of BMAL1 expression in the PC for the circadian rhythm of the odor-evoked activity in the PC. However, we could not clearly test the role of BMAL1 expression in the olfactory bulb because Pcdh21-Bmal1 KO removed BMAL1 from only ~$30\%$ of mitral/tufted cells (Fig. 3). Notably, a previous report suggested that the olfactory bulb functions as an independent circadian system regulating circadian rhythms of the odor-evoked neural activity, as surgical removal of the olfactory bulb abolished the circadian rhythms of the odor-evoked response in the PC1. Since the PC receives the vast majority of afferent fibers from the olfactory bulb37,38, it would be important in the future to understand how the olfactory bulb and the PC interact with each other to shape the circadian rhythms of the odor-evoked activity in the PC. Given that the mitral/tufted cells in the olfactory bulb project to the PC37,38, one potential scenario is that neural activity might contribute to synchronization between the olfactory bulb project and the PC.
The present study identified seven genes whose mRNA expressions showed circadian rhythms under the control of BMAL1 (Fig. 6). Of the seven genes identified, Avpr1a encodes a G-protein coupled arginine vasopressin receptor, and it increases intracellular Ca2+ levels to activate ERK/CREB39. Chrnb2 encodes a subunit of a nicotinic acetylcholine receptor that promotes cation influx to depolarize the membrane potential40. Clcn4 encodes a voltage-gated chloride channel. Hence the circadian rhythms of expressions of these genes may directly shape the circadian rhythm of the odor-evoked activity in the PC. Unexpectedly, other genes are associated with presynaptic, rather than postsynaptic, functions. Cacna2d3 encodes a subunit of the voltage-gated calcium channel Cav2.241 which functions at presynaptic terminals42–44. Cav2.2 channels are known to interact with the active zone proteins including SNAP2545–48. SynI encodes Synapsin I which contributes to the size control of the synaptic vesicle pool within the presynapses49. One possible scenario, therefore, is that BMAL1 in the PC regulates the expression of Cav2.2, SNAP25, and Synapsin I to regulate the circadian rhythm of the odor-evoked output from the PC. Consistently, we found that action potential-based neural activity is required for in intrinsic circadian oscillation of clock gene Per2 in the isolated SCN as well as the PC (Fig. 5d). However, we have to take into account that our experiments were limited to c-Fos induction, which does not directly measure neural activity. We would need to conduct other experiments such as calcium imaging and electrophysiological analyses to measure neural activity in a less indirect approach. For example, further investigations using electrophysiology and calcium imaging will be needed to understand how the local circadian clock indeed impacts on neural activity and synapse transmission in the PC.
Overall, we show in our current analyses that the molecular clockwork in the pyramidal neurons of the PC intrinsically regulates the circadian rhythm of the odor-evoked neural activity. We further show candidate genes that might contribute to the circadian rhythm of the odor-evoked neural activity under the control of the molecular clock. Since the molecular clock appears to target distinct genes in different tissues50,51, it is highly possible that additional genes might be transcriptionally regulated by BMAL1 and contribute to the circadian rhythms of the olfactory response in the PC. It will be thus important to understand the comprehensive sets of BMAL1-targeting genes in the PC and how the gene products could contribute to the intrinsic control of the circadian rhythm of neural activity.
## Animals
All animal experiments had received approval from the Animal Care Committee at the University of Tokyo. C57BL/6 J wild-type mice were purchased from Japan SLC, Inc. Bmal1-floxed mice, Emx1-Cre mice, Vgat-Cre mice, and PER2::LUC mice were provided as a kind gift from Dr. Fukada, Omp-Cre mice, Pcdh21-CreER mice, and tdTomato-flox(Ai9) mice were purchased from The Jackson Laboratory. Animals were group-housed, up to four animals per cage, in a temperature (23 ± 1˚C) and humidity (50 ± $20\%$) controlled environment, in a 12 h light, 12 h dark cycle with ad libitum access to food and water, light on at 7:00 AM. Male mice were used in all experiments.
## Odor presentation paradigm
Adult male mice (2–3 months of age) were isolated to a new cage (Japan CLEA, CL-0113-1) 1 week prior to odor presentation with ad libitum access to food and water. Two days prior to odor presentation, mice were transferred to a new cage and underwent a constant dark condition for >24. On the day of the experiment, 100 µL of odorant (cedar oil, limonene[-]) was applied to a cotton swab and placed on the cage lid for 5 min. 50 min after odor presentation, mice were perfused for further histological experiments.
## Immunohistochemistry
Mice were anesthetized with Isoflurane (Pfizer), then transcardially perfused with ice-cold 10 mL 0.1 M phosphate buffer (pH 7.4), followed by ice-cold 25 mL fixative ($4\%$ paraformaldehyde in 0.1 M phosphate buffer). Whole brains were dissected, immersed in ice-cold fixative for 6 h. Next, brains were immersed in 0.1 M PBS (pH 7.4) containing $20\%$ sucrose for 6 h, then transferred to 0.1 M PBS (pH 7.4) containing $30\%$ sucrose for 6 h. Forty µm-thick coronal sections were cut with a sliding microtome (Yamato Kohki). Cut sections were stored in an Anti-freezing buffer ($25\%$ glycerol (Nakalai Tesque), $30\%$ ethylene glycol (Nakalai Tesque), 0.1 M PBS) at −30 °C.
Brain sections were incubated in blocking buffer ($3\%$ Normal Donkey Serum (Jackson Immuno Research Laboratories), $0.05\%$ TritonX-100, PBS) for 2 h at room temperature. Next, sections were incubated in primary antibody (Anti-c-Fos Rabbit pAb, Calbiochem, 1:20,000 dilution in blocking buffer; Anti-BMAL1 Rabbit Polyclonal, Novus Biologicals, 1:1000 dilution in blocking buffer; Anti-Tbr1 Rabbit Polyclonal, Abcam, 1:1000 dilution in blocking buffer; anti-Tbx21 guinea pig, a gift from Y Yoshihara, 1:10,000 dilution in blocking buffer) for 3 days at 4 °C. After primary antibody incubation, sections were washed with PBST for 10 min, five times at room temperature. Next, sections were incubated with a second antibody (Alexa 488-conjugated donkey anti-rabbit, Invitrogen, 1:500 in blocking buffer). Afterward, sections were washed with PBST for 10 min, five times at room temperature. Finally, for nuclear staining, sections were incubated with Nissl (Invitrogen, 1:200 in PBS) or DAPI (Sigma-Aldrich, 1:2000 in PBS) for 1 h at room temperature. Sections were mounted with Prolong Diamond (Thermo Fisher Scientific) antifade.
## Microscopy and cell counting
For whole-brain 3D Mapping, brain sections were imaged by KEYENCE.
For other imaging, brain slices were imaged by confocal microscopy (Leica, SP8).
Brain sections immunostained with c-Fos antibody were quantified with the following procedures using ImageJ. First, the background signal intensity was calculated by averaging ten areas of the brain section without any cells. The background intensity was subtracted and despeckled. The signal threshold was set to 40, and the signals >30 µm were counted as c-Fos positive cells. For other imaging, brain slices were imaged by confocal microscopy (Leica, SP8).
## Locomotor activity analysis
Mice were housed individually, and their spontaneous locomotor activities were recorded using an area sensor (Elekit) with an infrared detector. Locomotor activity was collected every minute and analyzed by ClockLab software (Actimetrics).
## Adeno-associated virus production
AAV293 cells (Agilent Technologies) were cultured in 10 cm culture dish (1.25~1.5 × 106 cells/dish) with 10 mL culture medium (DMEM (Sigma-Aldrich, D5796-500ML), $10\%$ FBS (GE Life Sciences, HyClone SH30396.03), Penicillin Streptomycin (gibco, 15140-122), GlutaMax (gibco, 35050-061)). After 2~3 days, cells were passaged into T-150 flasks (2.5~3.0 × 106 cells/flask) with 35 mL of culture medium. After 48~72 h, when cells reached 60~$70\%$ confluency, 25 mL of culture medium was removed, and 9 mL of fresh culture medium was added. Subsequently, plasmids were transfected via calcium phosphate co-precipitation. Briefly, three plasmids—pAAV-DJ, pHelper, plasmid including genes of interest—were added to 6 mL of 0.3 M CaCl2. Next, 6 mL of 2x HBS (280 mM NaCl, 1.5 mM Na2HPO4, 50 mM Hepes) was added with thorough mixing. 3 mL of the mixed compound was applied to each flask and further cultured for 6~12 h. Next, the culture medium was replaced with 35 mL of medium containing trichostatin A (DMEM, $2\%$ FBS, Penicillin, Glutamax, 100 nM trichostatin A (Wako)), and further cultured for 48~96 h. Subsequently, the medium containing scraped cells was collected and underwent freeze-thaw three times. Finally, $\frac{1}{10}$,000 volume of 250 U/µL TurboNuclease (Accelagen) was added and incubated for 30 min at 37 °C. The AAVs were purified with ViraPur Kit (Virapur).
## Adeno-associated virus injection
We performed AAVs injection as previously described52,53. In brief, mice 6–8 weeks of age were stereotaxically injected. Animals were anesthetized with isoflurane (1–$2\%$) and placed in a stereotaxic frame. The adeno-associated virus AAVDJ-CaMKII-iCre-T2A-mCherry (3.55~7.87 ×1011 vg/mL) or AAVDJ-CAMKII-mCherry (7.83 × 1012 vg/mL) were injected to the olfactory bulb or the PC bilaterally. For AAV transduction to the granule cell layer of the olfactory bulb, we injected 300 nL of AAV to each olfactory bulb bilaterally. For AAV transduction to the PC, we injected AAV to three coordinates to either side of the brain, with a total of six injection coordinates bilaterally with 300 nL injection volume per injection coordinate. For injection, we used a pulled glass capillary (Drummond) and injected through nanoliter pressure injection (Nanoject3, Drummond). Stereotactic injection coordinates to target the olfactory bulb or PC were obtained from the Paxinos and Franklin atlas (for olfactory bulb AP:4.2 mm ML:1.0 mm DV:1.0 mm from brain surface; for PC AP:1.8, 0.5, −0.5 mm ML:2.7, 3.5, 3.9 mm, DV:3.8, 3.8, 4.2 mm from brain surface). Animals were allowed to recover for at least 4 weeks before being used in experiments.
## Luciferase assay
PER2::LUC mice 4 weeks of age were used for luciferase assay. Mice were sacrificed, and dissected brains were immediately cooled in ice-cold Hank’s Balanced Sodium Saline. The piriform cortex and the SCN were further coronally dissected in 150μm-thickness with vibratome. Sectioned brain slices were placed on Milli Cell immersed in culture medium ($5\%$ MEM, $18\%$ HBSS, 15 mM NaHCO3, 2 mM HEPES, 10 mM D-glucose, 1 mM L-glutamine, 175 mg/L Ascorbic Acid, 1 mg/mL insulin in 0.01 M HCl, $25\%$ Horse Serum; pH adjusted to 7.0~7.4) with Luciferin, and incubated at 37 °C, $5\%$ CO2. Luminescence was detected by Kronos Dio (Atto) with 1-min exposure, 10-min intervals. For tetrodotoxin application, Tetrodotoxin (Fujifilm) was applied to the culture medium with a final concentration of 20 µM.
## In silico data analysis
From the list of genes reported to show circadian fluctuations of mRNA levels in the SCN36, we manually searched the function of each gene via UniProt (https://www.uniprot.org/) and identified genes that were documented to be involved in neuronal processes.
## Real-time quantitative PCR
For RNA extraction, 8–10 weeks-old mice underwent a constant dark condition for 24–48 h. Brains were dissected, washed with HBSS, and immediately frozen with liquid nitrogen. RNA was extracted with RNeasy kit (Promega), and the cDNA library was created by first-strand reverse transcription with ReverTraAce Kit (Toyobo). For real-time quantitative PCR, cDNA samples were mixed with THUNDERBIRD SYBR qPCR Mix (Toyobo) and primers and underwent two-step PCR (95 ˚C 15 s→ 60 ˚C 60 s, 40 cycles).
Primer sequences for real-time quantitative PCR are explained in Supplementary Table 1.
## Statistics and reproducibility
The statistical significance between values were determined by one-way ANOVA, one-way ANOVA with Bonferroni correction, Wilcoxon rank-sum test, Wilcoxon rank-sum test with Bonferroni correction. All experiments were independently repeated two or more times.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04691-8.
## Peer review information
Communications Biology thanks Hugh Piggins, Christopher S. Colwell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Daniel Bendor and George Inglis.
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|
---
title: Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
authors:
- Matthias Griebel
- Dennis Segebarth
- Nikolai Stein
- Nina Schukraft
- Philip Tovote
- Robert Blum
- Christoph M. Flath
journal: Nature Communications
year: 2023
pmcid: PMC10043282
doi: 10.1038/s41467-023-36960-9
license: CC BY 4.0
---
# Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
## Abstract
Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.
The signal-to-noise ratio in bioimages is often low, which is problematic for segmentation. Here the authors report a deep learning method, deepflash2, to facilitate the segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance.
## Introduction
Partitioning images into meaningful segments (e.g., cells, cellular compartments, or other anatomical structures) is one of the most ubiquitous tasks in bioimage analysis1. Segmentation facilitates downstream tasks such as detection (both 2D and 3D), tracking, quantification, and statistical evaluation of image features. Depending on the biological analysis setting, we distinguish between semantic and instance segmentation. Semantic segmentation means subdividing the image into meaningful categories2. Instance segmentation further differentiates between multiple instances of the same category by assigning the segmented structures to unique entities (e.g., cell 1, cell 2, …). Performing image feature segmentation manually is tedious and time-consuming, which severely limits scalability. Conversely, its automated segmentation promises additional insights, more precise analyses, and more rigorous statistics2.
Deep learning (DL) has proven to be a flexible method to analyze large amounts of bioimage data3, and numerous solutions for automated segmentation have been proposed2,4–10. Depending on annotated training data, these tools and analysis pipelines are well suited for settings where the observable phenomena exhibit a high signal-to-noise ratio (SNR), for instance, in monodispersed cell cultures. However, the SNR in bioimages is often low, influenced by experimental conditions, sample characteristics, and imaging trade-offs. Such image material is inherently ambiguous, which hampers a reliable analysis. A case in point is the analysis of fluorescent images of complex brain tissue—a core technique in modern neuroscience—which is frequently subject to various sources of ambiguity, such as cellular and structural diversity, heterogeneous staining conditions, and challenging image acquisition processes.
Establishing DL-based segmentation pipelines in low SNR settings means overcoming substantial challenges during model training and evaluation and during the application of the model for the analysis of new images. Training and evaluation challenges commence with the manual annotation process. Here, human experts rely on heuristic criteria (e.g., morphology, size, signal intensity) to cope with low SNRs. Relying on a single human expert’s annotations for training can result in biased DL models11. At the same time, inter-expert agreement suffers in such settings, which, in turn, leads to ambiguous training annotations2,12. Without reliable annotations, there is no stable ground truth, which complicates both model training and evaluation. The application challenge emerges when DL models are deployed for analyzing large numbers of bioimages. This scaling-up step is a crucial leap of faith for users as it effectively means delegating control over the study to a black box system. DL models will generate segmentations for any image. However, the segmentation quality is unknown as the reliability of model generalizations beyond the training data cannot be guaranteed. Selecting a representative subset of images for training and evaluation in a single experiment is already challenging. Maintaining a representative training set across multiple experiments with possibly varying conditions compounds these problems and may eventually prevent reliable automation. For this reason, a viable deployment needs effective quality assurance, or as Ribeiro et al.13,p. 1135] put it, “if the users do not trust […] a prediction, they will not use it.” *In this* work, we introduce deepflash2, a DL-based analysis tool that addresses the key challenges for DL-based bioimage analysis. We illustrate the capabilities of deepflash2 using five representative fluorescence microscopy datasets of mouse brain tissue with varying degrees of ambiguity. In addition, we demonstrate the tool’s performance on three recent challenge datasets for prostate cancer grading, multi-organ nuclei segmentation, and colonic nuclear instance segmentation and classification. We benchmark the tool against other common analysis tools, achieving competitive predictive performance under the economical usage of computational resources.
## Results
In bioimage analysis, supervised DL models are typically embedded in two consecutive pipelines2—training and application. deepflash2 extends these pipelines to better cope with ambiguous data (Fig. 1).Fig. 1deepflash2 pipelines. Proposed integration of deepflash2 into the bioimage analysis workflow. In contrast to traditional DL pipelines, deepflash2 integrates annotations from multiple experts and relies on model ensembles for training and evaluation. Additionally, the application pipeline facilitates quality monitoring and out-of-distribution detection for predictions on new data.
The training and evaluation pipeline serves to fit a model on a given data set. It comprises data annotation, model training, and model validation. In deepflash2, this pipeline integrates annotations from multiple experts and relies on model ensembles to ensure highly accurate and reliable results. The evaluation of the model ensembles is achieved through a two-step evaluation process. The application pipeline leverages a trained DL model to predict the annotations of new images. By facilitating quality monitoring and out-of-distribution detection of new data, deepflash2 goes a step beyond mere prediction.
## Training and evaluation of DL model ensembles
Training builds upon a representative sample of the bioimage dataset under analysis, annotated by multiple experts (the annotations can be performed with any tool). To derive objective training annotations from multi-annotator data, deepflash2 estimates the ground truth (GT) via majority voting or simultaneous truth and performance level estimation (STAPLE14). deepflash2 computes similarity scores between expert segmentations and the estimated GT (Dice score for semantic segmentation, average precision for instance segmentation; Section “Evaluation metrics”). These measures of inter-expert variation serve as a proxy for data ambiguity, as shown in the second row of Fig. 2. Well-defined fluorescent labels are typically unanimously annotated (green), whereas ambiguous signals are marked by fewer experts (blue). This causes a high inter-rater variability when different experts annotate the same images11.Fig. 2Exemplary results on different immunofluorescence images. Representative image sections from the test sets of five immunofluorescence imaging datasets (first row) with corresponding expert annotations and ground truth (GT) estimation (second row). The inter-expert variation is indicated with ranges (lowest and highest expert similarity to the estimated GT) of the Dice score (DS) for semantic segmentation and mean Average Precision (mAP) for instance segmentation. The predicted segmentations and the similarity to the estimated GT are depicted in the third row, and the corresponding uncertainty maps and uncertainty scores U for quality assurance are in the fourth row. Areas with a low expert agreement (blue) or differences between the predicted segmentation and the estimated GT typically exhibit high uncertainties. deepflash2 also provides instance (e.g., somata or nuclei)-based uncertainty measures that are not depicted here. The maximum pixel uncertainty has a theoretical limit of 1.
DL model training in deepflash2 capitalizes on model ensembles to ensure high accuracy and reproducibility in the light of data ambiguity11. In contrast to recent work on the segmentation of ambiguous data, which focuses on explicitly modeling disagreements among experts15,16, our training on the estimated GT aims to provide the most objective basis possible for bioimage analysis. Furthermore, the usage of model ensembles facilitates reliable uncertainty quantification17. To ensure training efficiency, deepflash2 leverages pretrained feature extractors (encoders) and advanced training strategies (see “Methods”, Section “Training Procedure”).
The model ensemble predicts semantic segmentation maps, which are evaluated on a hold-out test set (Fig. 2, third row). For instance segmentation tasks, we leverage the cellpose library9, a generalist algorithm for cell and nucleus segmentation. By combining the semantic segmentation maps with cellpose’s flow representations, deepflash2 ensures reliable separation of touching objects. In doing so, we extend the original cellpose implementation to multichannel input images and multiclass instance segmentation tasks.
Each segmentation is accompanied by a predictive uncertainty map which is summarized by means of the average foreground uncertainty score U (Fig. 2, fourth row; Section “Uncertainty quantification”). These uncertainties are used for quality assurance during application (Section “Application and quality assurance”). To assess the model validity for bioimage analysis, deepflash2 implements the following two-step evaluation process:Absolute performance: Calculating the similarity scores between the predicted segmentations and the estimated GT on the test set. The scores can be accessed via the GUI or Excel/CSV export functions. Relative performance: Relating the performance scores to data ambiguity. The performance scores of individual experts are used to establish the desired performance range and can also be accessed through the GUI or Excel/CSV export.
The proposed evaluation procedure can generally be performed with any analysis tool as long as the required predictive performance is achieved. With regard to the practical application of a DL tool, however, we evaluate the tool’s performance along four dimensions: absolute predictive performance as indicated by the similarity to the estimated GT, relative predictive performance compared to the expert annotations, reproducibility of the experiments, and training duration (Fig. 3).Fig. 3Evaluation of predictive performance, relative performance, reliability, and speed on different immunofluorescence datasets.a, b Predictive performance on the test sets for a semantic segmentation ($$n = 40$$, 8 images for each dataset) and b instance segmentation ($$n = 32$$, 8 images for each depicted dataset except GFAP in HC), measured by similarity to the estimated GT. The grayscale filling depicts the comparison against the expert annotation scores. The p-values result from a two-sided Wilcoxon signed-rank test (semantic segmentation: $$p \leq 0.000170298$$ for nnunet, $$p \leq 0.000001405$$ for cellpose, $$p \leq 0.000000001$$ for U-Net [2019]; instance segmentation: $$p \leq 0.000090546$$ for nnunet, $$p \leq 0.000557802$$ for cellpose, $$p \leq 0.000000012$$ for U-Net [2019]). The expert comparison bars below the method names indicate the share of test instances that scored below the worst expert (white), in expert range (gray), or above the best expert (black). c Similarity of the predicted test segmentation masks for three repeated training runs with different training-validation splits ($$n = 40$$, 8 images for each dataset). Box plots are defined as follows: the box extends from the first quartile (lower bound of the box) to the third quartile (upper bound of the box) of the data, with a center line at the median. The whiskers extend from the box by at most 1.5x the interquartile range and are drawn down to the lowest and up to the highest data point that falls within this distance. d Training speed (duration) on different platforms: Google Colaboratory (Colab, gratuitous Nvidia Tesla T4 GPU) and Google Cloud Platform (GPC, costly Nvidia A100 GPU). Source data are provided as a Source Data file.
We benchmark the predictive performance of deepflash2 against a select group of well-established algorithms and tools. We utilize Otsu’s method18 as a simple baseline for semantic segmentation and cellpose9 as a generic baseline for (cell) instance segmentation. Additionally, we consider U-Net2, nnunet8, and fine-tuned cellpose model ensembles. cellpose has previously proven to outperform other well-known methods for instance segmentation such as Mask-RCNN19 or StarDist20. For greater clarity, Fig. 3 omits the two baseline models which offered subpar performance (an extensive comparison of all tools is provided in Supplementary Information 2.2).
Across all evaluation datasets, deepflash2 achieves competitive predictive performance for both semantic and instance segmentation tasks. To disentangle the difficulty of the prediction task (driven by data ambiguity) from the predictive performance, we scrutinize the absolute performance by relating it to the underlying expert annotation scores (relative performance). Notably, only deepflash2 achieves human expert performance across all evaluation tasks and, in some cases, even outperforms the best available expert annotation (Fig. 3a, b).
Moreover, Fig. 3c shows that the ensemble-based methods nnunet and deepflash2 yield very stable results (high similarity scores between the predicted segmentations of different training runs with different training-validation splits) across all datasets. The U-Net2, based on a single model, is subject to higher performance variability. The cellpose model ensembles exhibit a high variability for the semantic-segmentation-only GFAP in HC dataset but yield competitive results on the other (instance segmentation) datasets.
Relying on generic pretrained encoders, deepflash2 model ensembles are trained in less than an hour on machines with state-of-the-art GPUs (free and paid), similar to the pretrained cellpose model ensembles (Fig. 3d). Due to dynamic architecture reconfiguration, nnunet ensembles cannot leverage pretraining, and training from scratch can last longer than a week.
## Application and quality assurance
During application, scientists typically aim to analyze a large number of bioimages without ground truth information. To establish trust in its predictions, deepflash2 enables quality assurance on image as well as on instance/region level: For quality assurance on image level, the predicted segmentations are sorted by decreasing uncertainty score U.
We find that U is a strong predictor for the obtained predictive performance as measured by the Dice score (Fig. 4a). Consequently, U can be used as a proxy for the expected performance on unlabeled data, and the U values of the test set can serve as a reference for the quality assurance procedure (see Section “Quality Assurance” for further details). Note that the model ensembles are solely trained on the estimated GT, that is, there is no longer a concept of ambiguous annotations. However, Fig. 4b confirms that the uncertainty maps capture expert disagreement: Low pixel uncertainty is indicative of high expert agreement, whereas high pixel uncertainty arises in settings where experts submitted ambiguous annotations. Fig. 4Relationship between expert annotations, uncertainty, and similarity scores.a Correlation between Dice scores and uncertainties on the test set. We quantify the linear correlation using Pearson’s r and a two-tailed p-value ($$p \leq 0.00000002$$) for testing non-correlation. The grayscale filling depicts the comparison against the expert annotation scores. b Relationship between pixel-wise uncertainty and expert agreement (at least one expert with differing annotation; upper plot) and average prediction error rate (relative frequency of deviations between different expert segmentations and the predicted segmentation; lower plot) on the test set. Source data are provided as a Source Data file.
In situations with high uncertainty scores, scientists may want to check predictions through manual inspection using the provided uncertainty maps. For semantic segmentation, the uncertainty maps facilitate rapid visual identification of regions where the predicted segmentations are subject to high uncertainties. For instance segmentation tasks, deepflash2 additionally calculates an average uncertainty score for each instance. Subsequently, it allows a single click export-import to ImageJ/Fiji ROIs (regions of interest), with ROIs sorted by their average uncertainty score. This enables a focused inspection and adjustment of specific instances that are supposedly segmented poorly. Thus, the quality assurance process helps the user prioritize the review of both images and single instances within images that exhibit high uncertainties.
The quality assurance procedure also facilitates the detection of out-of-distribution images, i.e., images that differ from the training data and are thus prone to erroneous predictions. We showcase the out-of-distribution detection on a large bioimage dataset comprising 256 in-distribution images (same properties as training images), 24 partly out-of-distribution images (same properties with previously unseen structures such as blood vessels), and 32 fully out-of-distribution images (different immunofluorescent labels) (Fig. 5b–d). Using the uncertainty score for sorting, the lowest uncertainty ranks are entirely taken by the 32 fully out-of-distribution images. Most of the partly out-of-distribution images obtain uncertainty ranks between 33 and 150 (Fig. 5a). A conservative protocol could require scientists to verify all images with an uncertainty score exceeding the reference uncertainty scores (Section “Quality Assurance”). Out-of-distribution images may then be excluded from the analysis or annotated for retraining in an active learning manner21.Fig. 5Out-of-distribution detection.a Out-of-distribution (ood) detection performance using heuristic ranking via uncertainty score. Starting the manual verification of the predictions at the lowest rank, all images with deviant fluorescence labels (fully ood, $$n = 32$$ images) are detected first. The partly ood images with previously unseen structures ($$n = 24$$) are mostly located in the lower ranks, and the in-distribution images (similar to training data of cFOS in HC, $$n = 264$$) are in the upper ranks. b–d Representative image crops of the three categories used in (a). Source data are provided as a Source Data file.
## Evaluation in the biomedical imaging wild
So far, the evaluation of our study has been focused on ambiguous fluorescent images, as the underlying datasets allow us to demonstrate the use of deepflash2 along the entire bioimage analysis pipeline. However, deepflash2 can out-of-the-box deliver convincing segmentation results for other types of 2D images with an arbitrary number of input channels. Also, multiclass GT estimation, as well as multiclass semantic or instance segmentation, are supported. We showcase the use and performance of deepflash2 on three distinct biomedical imaging datasets that were part of recent data science challenges (Fig. 6, see Section “Evaluation metrics” for detailed dataset descriptions). We used default training parameter settings for all datasets except for the gleason dataset, where we adjusted a single hyperparameter to account for the large tumor regions (we increased the receptive field of the image tiles by selecting a zoom-out factor of 4).Fig. 6Demonstration on challenge datasets gleason, monuseg, conic. Exemplary test image slices (first column), corresponding GT segmentations (second column), predicted segmentations (third column), and uncertainty maps (fourth column) with uncertainty scores U. GT segmentations for the gleason dataset were estimated via STAPLE. The bar plots in the last column summarize the results over the entire test sets by class for semantic segmentation (gleason, $$n = 49$$ test images) and instance segmentation (monuseg $$n = 15$$ test images, conic $$n = 48$$ test images). The color codes in the y-axis labels and bars of the bar charts indicate the different class numbers in the segmentation masks (first and second row). We additionally report the average score across all classes (Av.) in multiclass settings. The error bars depict the $95\%$ confidence interval of the observations estimated via bootstrapping around the arithmetic mean (center). Source data are provided as a Source Data file.
The gleason challenge [2019] aims at the automatic Gleason grading (multiclass semantic segmentation) of prostate cancer from H&E-stained histopathology images22. The grading of prostate cancer tissue performed by different expert pathologists suffers from high inter-expert variability. deepflash2 outperforms the nnunet baseline Fig. 6 (last column) on all classes except the third class (very rare Gleason grade 5).
The monuseg [2018] challenge aims at nuclei segmentation in digital microscopic tissue images23. In this binary instance segmentation task, deepflash2 also outperforms the nnunet baseline and would have reached a Top-10 rank in the challenge monuseg leaderboard yielding 0.67 in the challenge metric Aggrated Jaccard Index.
Finally, the recent conic [2022] challenge also aims at nuclei segmentation of H&E-stained histology images. The challenge is based on the Lizard dataset24 containing half a million labeled nuclei in colon tissue and requires multiclass instance segmentation. deepflash2 outperforms the nnunet baseline Fig. 6 (last column) on all classes except the fourth class (Eosinophil).
## Discussion
The deepflash2 DL pipelines facilitate the objective and reliable segmentation of ambiguous bioimages integrating multi-expert annotations, deep model ensembles, and quality monitoring. They may thereby offer a blueprint for the training, evaluation, and application of DL in bioimaging projects, as they can be used with any tool or in custom DL pipelines.
As a tool, deepflash2 supports various use-cases for the integration of expert annotations, e.g., one annotation per image, multiple annotations per image (can be achieved by providing the same image under different names for each annotation), or training on the est. GT. Here, we want to discuss the best use of multi-expert annotations. These can help to mitigate the emerging DL replication crisis in the bioimage analysis as single-expert annotations may introduce errors or bias into model training25. Recall that image feature annotation is a complex perception task for humans and is subject to the individual annotator’s graphical perceptual abilities26. Clear labeling instructions are of special importance to reduce the need for multi-expert annotations, as highlighted by Rädsch et al.27.
There is not a per-se best annotation strategy, but the choice will rather depend on the bioimaging project and the available resources, i.e., we need to trade-off the number of training images, which should represent the diversity of the data, against the annotation quality gains from multiple annotations. Unbiased and precise annotations are typically acquired via GT estimation from multiple experts. Also, the repeated annotation of the same images allows us to approximate a human performance level on the given data, which is part of our proposed two-step evaluation process. Yet, repeated labeling of identical images results in a markedly higher annotation effort for each training image. Given a fixed annotation budget, multi-expert annotations would directly reduce the number of training images, which can have a detrimental effect on the predictive model performance if the underlying data distribution is not captured sufficiently. To obtain a better understanding of the annotation strategy trade-offs, we conducted some initial experiments regarding the most efficient use of expert time (see Supplementary Notes S4). We compared two strategies over different annotation budgets: The first strategy required the images to be annotated by all available experts. The second strategy required the experts to annotate different images, resulting in larger training sets. The results indicate that the second strategy is superior when only a few image annotations are available (small annotation budget). In this case, the model performance benefits from more (but less precise) image-annotation-pairs to capture the diverse data distribution. The first strategy is superior when more training annotations are available (higher annotation budget). Our results suggest that the consensus segmentations are indeed learnable by the DL models.
deepflash2 builds upon the integration of established DL libraries. For segmentation architectures such as the U-Net3, deepflash2 leverages the segmentation-models-pytorch library28. The library has a large record of use in data science competition-winning solutions (see the “Hall of Fame”28), including deepflash2’s Gold Medal and Innovation Award in the *Kaggle data* science competition hosted by the HuBMAP consortium29. Moreover, the encoder architectures of these segmentation models are based on the timm library30, which has emerged as the de-facto benchmark DL library for image classification and is continuously updated with the latest model architectures, including the currently used ConvNext encoder31. There is currently no bioimaging tool making these resources easily accessible to life science researchers. Also, deepflash2’s capability to automatically integrate new encoders and pretrained weights is a significant advantage over existing tools in the rapidly materializing field of DL.
By offering uncertainty measures (uncertainty maps, uncertainty score U), deepflash2 facilitates the aforementioned quality assurance procedure. Exploiting these measures in the bioimage analysis process promises insights into experimental conditions as well as biological mechanisms. Uncertainty arises from biological processes in experimental groups, for instance, when signal-to-noise ratios change due to global changes in image feature expression levels. Recognizing such quality issues during prediction offers a valuable feedback loop from analysis to experiment design and execution.
Initiatives such as the BioImage Model Zoo32 or the Hugging Face Model Hub (https://huggingface.co/models) are simplifying DL model sharing in the research community. deepflash2 simplifies sharing of trained model ensembles, and we highly encourage scientists in making their research reproducible, accessible, and transparent. As deepflash2 addresses the segmentation of ambiguous data that potentially varies across experiments, we think that a rigorous and transparent evaluation, as well as an easily accessible demonstration of the model’s capabilities, can contribute to build trust in new, DL-enabled research.
deepflash2 aims to be a tool with preconfigured settings that offer out-of-the-box, very high predictive accuracy for typical bio-imaging tasks. However, this comes with some rigidity concerning the chosen hyperparameters. This may limit the tool’s predictive performance on some datasets using default settings. A case in point was the gleason dataset, where we had to adjust the scaling factor to accommodate untypically large input images, which we could not capture with our default 512 × 512 patch sizes—such a manual expert adjustment of course runs against the goal of user-friendliness (note that nnunet, which automatically configures hyperparameters during training, does not face this problem). The proposed quality assurance procedure offers a direct assessment of the training data representativeness for a particular test instance by answering the question: How well-suited is the trained model ensemble for assessing this very instance? However, it does not provide any formal guarantees on the overall performance of the model ensemble and should be interpreted with caution. Ultimately, the reported uncertainty measures are influenced by the underlying DL models, training procedures, and the theoretical disentanglement between epistemic and aleatoric uncertainty (Section “Uncertainty quantification” and Supplementary Fig. S5.1).
deepflash2 offers an end-to-end integration of DL pipelines for bioimage analysis of ambiguous data. An easy-to-use GUI allows researchers without programming experience to rapidly train performant and robust DL model ensembles and monitor their predictions on new data. We are confident that deepflash2 can help establish more objectivity and reproducibility in natural sciences while lowering the overall workload for human annotators. deepflash2 introduces a concept for objective bioimage analysis that goes beyond ground truth estimation and measures of predictive accuracy. It also introduces ambiguity not only as a technical but also as a biological data variable in the bioimage analysis process. We think that this concept can serve as a baseline for DL-based biomedical image feature segmentation. Going forward, the tool will benefit from a growing user base which in turn helps reveal image specifications for which the default parameters may be less suitable. Subsequent releases will try to address such instances by establishing useful alternative configurations.
## Ethical statement
All experiments and experimental procedures were in accordance with the guidelines set by the European Union and our local veterinary authority (Veterinäramt der Stadt Würzburg). In addition, all experiments and experimental procedures were approved by our institutional Animal Care, the Utilization Committee, and the Regierung von Unterfranken, Würzburg, Germany (License numbers: 55.2-2531.01-$\frac{95}{13}$ and 55.2.2-352-2-$\frac{509}{1067}$).
## Implementation details
The deepflash2 code library is implemented in Python 3, using numpy, scipy, and opencv for the base operations. The ground truth estimation functionalities are based on the simpleITK33. The DL-related part is built upon the rich ecosystem of PyTorch34 libraries, comprising fastai35 for the training procedure, segmentation models pytorch28 for segmentation architectures, timm30 for pretrained encoders, and albumentations36 for data augmentations. Instance segmentation capabilities are complemented using the cellpose library9. The trained model ensembles are designed to be directly executed in ImageJ using the DeepImageJ Plugin37, can be shared on the BioImage Model Zoo32, or hosted for inference. The deepflash2 GUI is based on the Jupyter Notebook environment38. Using interactive widgets39 deepflash2 allows users to execute all analysis steps directly in the GUI or use the export functionality for subsequent processing in other tools (e.g., ImageJ or Fiji). Statistical analyses in this study were performed using pingouin; Figures were created using seaborn and matplotlib.
## Ground truth estimation
To train reproducible and unbiased models, deepflash2 relies on GT estimation from the annotations of multiple experts. deepflash2 offers GT estimation via simultaneous truth and performance level estimation (STAPLE)14 (default in our analyses) or majority voting. Note that due to the ambiguities in the data, GT estimation can yield biologically implausible results (e.g., by merging the areas of two cells). We corrected such artifacts in our test sets. deepflash2 supports both multi-expert joining as well as single-expert annotations.
## Training procedure
The training of deepflash2 model ensembles is designed to achieve out-of-the-box rapid and high-quality segmentation of most bioimages without custom tuning. To achieve this, the deepflash2 pipeline was developed in an iterative manner seeking to establish a reliable base configuration.
The starting point for the selection parameter process was the award-winning solution at the *Kaggle data* science competition HuBMAP - Hacking the Kidney (see Section “Discussion”). To obtain a computationally manageable search space, we conducted some initial experiments on the training sets of the immunofluorescence data (PV in HC, cFOS in HC, mScarlet in PAG, YFP in CTX, and GFAP in CTX) via k-fold cross-validation. During this preselection phase, we fixed the architecture of our neural network as well as the weight initializations. Subsequently, we set up large-scale computational experiments to define the remaining hyperparameters via Bayesian optimization using sweeps on the Weights & Biases40 MLOps platform. The search spaces included different encoders (ResNet18-50, EfficientNet b0-b4, ConvNext tiny and standard), tile shapes (256 × 256, 512 × 512, 1024 × 1024), mini-batch sizes [2, 4, 8, 16, 32], learning rates (0.00001–0.01) for the Adam optimizer41 with decoupled weight decay (0.00001–0.1), and training iterations (100–10,000). The sweeps were also evaluated on the immunofluorescence datasets. The training procedure for individual applications is outlined below.
## Default settings and customization options
The default DL-model architecture in deepflash2 is a U-net3 with a ConvNext Tiny encoder31. The encoder is initialized with ImageNet42 pretrained weights to allow better feature extraction and fast training convergence. The remaining weights in the segmentation architecture are initialized from a truncated normal distribution43. By combining pretraining and random initialization, this approach improves diversity in model ensembles. The encoder architectures were pretrained on 3-channel input images. If the new data has fewer than three input channels, we remove the excess pretrained weights in the first layer. If the new data comprises more than three input channels, we initialize the weights from a truncated normal distribution. Similar to the nnunet, we chose the mean of the cross-entropy and Dice loss44 as the learning objective.
Each model is trained using the fine-tune policy of the fastai library35. This entails freezing the encoder weights, one-cycle training45 of one epoch, unfreezing the weights, and again one-cycle training. During each epoch, we sample equally sized patches from each image in the training data. To address the issue of class imbalances, we use a weighted random sampling approach that ensures that the center points of the patches are sampled equally from each class. This kind of sampling also contributes to the data augmentation pipeline. Data augmentation operations include random augmentations such as rotating, flipping, and gamma correction; again, this follows best practices established by nnunet. We trained each model with one epoch in the first (frozen encoder weights) cycle and 25 epochs in the second cycle using a mini-batch size of four (patch size 512 × 512), a base learning rate of 0.001 and decoupled weight decay (0.001). We used a scale factor of 4 (zoom-out) for the gleason dataset and a scale factor of 1 for all other datasets (scaling is only applied during training and does not change the size of the final predictions). The training and validation data for the different models are shuffled by means of a k-fold cross-validation (with $k = 5$ in our experiments).
While they were designed for out-of-the-box usage, the deepflash2 Python API and GUI allow us to easily change all configuration parameters. These parameter choices can also be imported and exported via a JSON file. Experienced users can select alternative architectures (e.g., Unet++46 or DeepLabV3+47) and encoders (e.g., ResNet48, EfficientNet49). This flexibility is facilitated by the segmentation models pytorch package28. deepflash2 also provides options for common segmentation loss functions such as Focal50, Tversky51, or Lovasz52. Users can also adjust augmentation strategies or add more augmentations (e.g., contrast limited adaptive histogram equalization or grid distortions). One can also customize all training settings, for example, by opting for a different optimizer or setting a dataset-specific learning rate using the learning rate finder.
## Semantic segmentation
For the semantic segmentation of a new image with features \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\bf{X}}}} \in {{\mathbb{R}}}^{d \times c}$$\end{document}X∈Rd×c deepflash2 predicts a semantic segmentation map y ∈ {1, …, K}d, with K being the number of classes, d the dimensions of the input, and c the input channels. Without loss of generality, class 1 is defined as background. We use the trained ensemble of M deep neural networks to model the probabilistic predictive distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{\theta }\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{\bf{X}}}}}}}}\right)$$\end{document}pθy∣X, where θ = (θ1, …, θM) are the parameters of the ensemble. Here, we leverage a sliding window approach with overlapping borders and Gaussian importance weighting8. We improve the prediction accuracy and robustness using T deterministic test-time augmentations (rotating and flipping the input image). Each augmentation t ∈ {1, …, T} applied to an input image creates an augmented feature matrix Xt. To combine all predictions, we follow Lakshminarayanan et al.17 and treat the ensemble as a uniformly weighted mixture model to derive1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p({{{{{{{\bf{y}}}}}}}}|{{{{{{{\bf{X}}}}}}}})=\frac{1}{T}\mathop{\sum }\limits_{$t = 1$}^{T}\frac{1}{M}\mathop{\sum }\limits_{$m = 1$}^{M}{p}_{{\theta }_{m}}\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{{\bf{X}}}}}}}}}_{{{{{{{{\bf{t}}}}}}}}},\,{\theta }_{m}\right)$$\end{document}p(y∣X)=1T∑$t = 1$T1M∑$m = 1$Mpθmy∣Xt,θmwith \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{{\theta }_{m}}\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{{\bf{X}}}}}}}}}_{{{{{{{{\bf{t}}}}}}}}},{\theta }_{m}\right)=\,{{\mbox{Softmax}}}\,({f}_{{\theta }_{m}}({{{{{{{{\bf{X}}}}}}}}}_{{{{{{{{\bf{t}}}}}}}}}))$$\end{document}pθmy∣Xt,θm=Softmax(fθm(Xt)) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{{\theta }_{m}}$$\end{document}fθm representing the neural network parametrized with θm. We use $M = 5$ models and $T = 4$ augmentations in all our experiments. Finally, we obtain the predicted segmentation map2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{{{{{{{{\bf{y}}}}}}}}}=\mathop{{{{{{\mathrm{argmax}}}}}}}\limits_{{{{{{{\bf{k}}}}}}}\in {\{1,\ldots,K\}}^{d}}\ p({{{{{{{\bf{y}}}}}}}}={{{{{{{\bf{k}}}}}}}}|{{{{{{{\bf{X}}}}}}}}).$$\end{document}y^=argmaxk∈{1,…,K}dp(y=k∣X).
## Uncertainty quantification
The uncertainty is typically categorized into aleatoric (statistical or per-measurement) uncertainty and epistemic (systematic or model) uncertainty53. To approximate the uncertainty maps of the predicted segmentations, we follow the approach of Kwon et al.54. Here, we replace the Monte-Carlo dropout approach of Gal and Ghahramani55 with deep ensembles, which have proven to produce well-calibrated uncertainty estimates and a more robust out-of-distribution detection17. In combination with test-time augmentations (inspired by Wang et al.56), we approximate the predictive (hybrid) uncertainty for each class k ∈ {1, …, K} as3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mbox{Var}}}_{p\left({{{{{\mathbf{y}}}}}}=k \mid {{{{{\mathbf{X}}}}}} \right)}:=\underbrace{\frac{1}{T} \sum\limits_{$t = 1$}^{T} \frac{1}{M} \sum\limits_{$m = 1$}^{M} \left[p_{\theta_{m}}\left({{{{{\mathbf{y}}}}}}=k \mid {{{{{\mathbf{X}}}}}}_t,\theta_{m}\right)-p_{\theta_{m}}\left({{{{{\mathbf{y}}}}}}=k \mid {{{{{\mathbf{X}}}}}}_t,\theta_{m}\right)^{2}\right]}_{\substack{{{{{{\mathrm{epistemic}}}}}}\,\,{{{{{\mathrm{uncertainty}}}}}}}}\\+\underbrace{\frac{1}{T} \sum\limits_{$t = 1$}^{T} \frac{1}{M} \sum\limits_{$m = 1$}^{M} \left[p_{\theta_{m}}\left({{{{{\mathbf{y}}}}}}=k \mid {{{{{\mathbf{X}}}}}}_t,\theta_{m}\right)-p \left({{{{{\mathbf{y}}}}}}=k \mid {{{{{\mathbf{X}}}}}}\right)\right]^{2}}_{\substack{{{{{{\mathrm{aleatoric}}}}}}\,\,{{{{{\mathrm{uncertainty}}}}}}}}$$\end{document}Varpy=k∣X:=1T ∑$t = 1$T1M ∑$m = 1$Mpθmy=k∣Xt,θm−pθmy=k∣Xt,θm2⏟epistemicuncertainty+1T ∑$t = 1$T1M ∑$m = 1$Mpθmy=k∣Xt,θm−py=k∣X2⏟aleatoricuncertaintywhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p\left({{{{{{{\bf{y}}}}}}}}=k|{{{{{{{\bf{X}}}}}}}}\right)$$\end{document}py=k∣X denotes probabilities of a single class k.
To allow an intuitive visualization and efficient calculation in multiclass settings, we aggregate the results of the single classes to retrieve the final predictive uncertainty map:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mbox{Var}}}}_{p\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{\bf{X}}}}}}}},\theta \right)}=\frac{\zeta }{K}\mathop{\sum }\limits_{$k = 1$}^{K}{{{\mbox{Var}}}}_{p\left({{{{{{{\bf{y}}}}}}}}=k|{{{{{{{\bf{X}}}}}}}},\theta \right)}$$\end{document}Varpy∣X,θ=ζK∑$k = 1$KVarpy=k∣X,θwhere ζ is a scaling factor. Following the derivation in Kwon et al.54, the moment-based predictive uncertainty \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mbox{Var}}}}_{p\left({{{{{{{\bf{y}}}}}}}}=k|{{{{{{{\bf{X}}}}}}}}\right)}\in [0;0.25]$$\end{document}Varpy=k∣X∈[0;0.25]. Therefore, we set ζ to 4 in our experiments which scales the theoretical maximal pixel uncertainty to 1. Note that the formulation in Equation [4] may differ from the general formulation in Kwon et al.54 for K > 2.
For the heuristic sorting and out-of-distribution detection, we define an aggregated uncertainty metric on image level. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{y}}_{i}$$\end{document}y^i be the predicted segmentation of pixel i, xi the feature vector of pixel i and N the total number of pixels defined by d. We define the scalar-valued foreground uncertainty score for all predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${N}_{f}=\left\{i\in \left\{1,\ldots,N\right\}|{\hat{y}}_{i} > 1\right\}$$\end{document}Nf=i∈1,…,N∣y^i>1 as5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${U}_{p\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{\bf{X}}}}}}}},\theta \right)}:\!\!=\frac{1}{|{N}_{f}|}\mathop{\sum}\limits_{i\in {N}_{f}}{{{\mbox{Var}}}}_{p\left({y}_{i}|{{{{{{{{\bf{x}}}}}}}}}_{i},\theta \right)}.$$\end{document}Upy∣X,θ:=1∣Nf∣∑i∈NfVarpyi∣xi,θ.
## Instance segmentation
If the segmented image contains touching objects (e.g., cells that are in close proximity), deepflash2 integrates the cellpose library9, a generalist algorithm for cell and nucleus segmentation. We use the combined predictions of each class \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p\left({{{{{{{\bf{y}}}}}}}}=k|{{{{{{{\bf{X}}}}}}}}\right)$$\end{document}py=k∣X to predict the flow representations with the pretrained cellpose models. We then leverage the post-processing pipeline of cellpose to derive instance segmentations by combining the flow representations with the predicted segmentation maps \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{y}$$\end{document}y^. This procedure scales to an arbitrary number of classes and is, in contrast to the original cellpose implementation, not limited to one (or two) input channels. However, it requires the image feature shapes to be compatible with the pretrained cellpose models. To monitor the compatibility deepflash2 automatically reports the number of pixels that were removed during the instance segmentation process in the results table (column cellpose_removed_pixels). The differences were negligible in our experiments (<$0.005\%$). We recommend increasing the cellpose flow threshold, which is directly adjustable in the deepflash2 GUI, or fine-tuning the cellpose models if these differences become more significant.
## Evaluation metrics
For semantic segmentation, we calculate the similarity of two segmentation masks ya and yb using the Dice score. For binary masks, this metric is defined as6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,{{\mbox{DS}}}:\!\!\!=\frac{2{{\mbox{TP}}}}{2{{\mbox{TP}}}+{{\mbox{FP}}}+{{\mbox{FN}}}\,},$$\end{document}DS:=2TP2TP+FP+FN,where the true positives (TP) are the sum of all matching positive (pixels) elements of ya and yb, and the false positives (FP) and false negatives (FN) are the sum of positive elements that only appear in ya or yb, respectively. In multiclass settings, we use macro averaging, i.e., we calculate the metrics for each class and then find their unweighted mean. The Dice score is commonly used for semantic segmentation tasks but is unaware of different instances (sets of pixels belonging to a class and instance).
For instance segmentation, let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\bf{y}}}}}}}}}_{a}^{I}$$\end{document}yaI and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\bf{y}}}}}}}}}_{b}^{I}$$\end{document}ybI be two instance segmentation masks that contain a finite number of instances Ia and Ib, respectively. An instance *Ia is* considered a match (true positive—TPη) if an instance Ib exists with an Intersection over Union (also known as Jaccard index) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,{{\mbox{IoU}}}\,({I}_{a},{I}_{b})=\frac{{I}_{a}\cap {I}_{b}}{{I}_{a}\cup {I}_{b}}$$\end{document}IoU(Ia,Ib)=Ia∩IbIa∪Ib exceeding a threshold η ∈ (0, 1]. Unmatched instances Ia are considered as false positives (FPη), and unmatched instances Ib as false negatives (FNη). We define the Average Precision at a fixed threshold η as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mbox{AP}}}}_{\eta }:\!\!=\frac{T{P}_{\eta }}{T{P}_{\eta }+F{N}_{\eta }+F{P}_{\eta }}$$\end{document}APη:=TPηTPη+FNη+FPη. To become independent of fixed values for η, it is common to average the results over different η. The resulting metric is known as mean Average Precision and is defined as7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,{{\mbox{mAP}}}:\!\!=\frac{1}{|H|}\mathop{\sum}\limits_{\eta \in H}{{{\mbox{AP}}}}_{\eta }.$$\end{document}mAP:=1∣H∣∑η∈HAPη.
We use a set of 10 thresholds \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H=\{\eta \in [0.50,\ldots,0.95]|\eta \equiv 0\,{{{{{{\mathrm{mod}}}}}}}\,\,0.05\}$$\end{document}H={η∈[0.50,…,0.95]∣η≡0mod0.05} for all evaluations. This corresponds to the metric used in the COCO object detection challenge57. Additionally, we exclude all instances I that are below a biologically viable size from the analysis. The minimum size is derived from the smallest area annotated by a human expert: 61 pixel (PV in HC), 30 pixel (cFOS in HC), 385 pixel (mScarlet in PAG), 193 pixel (YFP in CTX, 38 pixel (monuseg), and 3–6 pixel (conic).
## Quality assurance
Once the deepflash2 model ensemble is deployed for predictions on new data, the quality assurance process helps the user prioritize the review of more ambiguous or out-of-distribution images. The predictions on such images are typically error-prone and exhibit a higher uncertainty score U. Thus, deepflash2 automatically sorts the predictions by decreasing the uncertainty score. Depending on the ambiguities in the data and the expected prediction quality (inferred from the hold-out test set), a conservative protocol could require scientists to verify all images with an uncertainty score exceeding a threshold Umin. Given the hold-out test set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q=\left\{({{{{{{{{\bf{X}}}}}}}}}_{1},{{{{{{{{\bf{y}}}}}}}}}_{1}),\ldots,({{{{{{{{\bf{X}}}}}}}}}_{L},{{{{{{{{\bf{y}}}}}}}}}_{L})\right\}$$\end{document}Q=(X1,y1),…,(XL,yL) where L is the number of samples, we define8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${U}_{min}:\!\!=\min \left\{{U}_{p\left({{{{{{{\bf{y}}}}}}}}|{{{{{{{\bf{X}}}}}}}},\theta \right)}|({{{{{{{\bf{y}}}}}}}},{{{{{{{\bf{X}}}}}}}})\in Q,S(y,\hat{y}) < \tau \right\}$$\end{document}Umin:=minUpy∣X,θ∣(y,X)∈Q,S(y,y^)<τwith \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S(y,\hat{y})$$\end{document}S(y,y^) being an arbitrary evaluation metric (e.g., DS or mAP) and τ ∈ [0, 1], a threshold that satisfies the prediction quality requirements. From a practical perspective, this means selecting all predictions from the test set with a score below the predefined threshold (e.g., DS = 0.8) and taking their minimum uncertainty score value U as Umin. The verification process of a single image is simplified by the uncertainty maps that allow the user to quickly find difficult or ambiguous areas within the image.
## Evaluation datasets
We evaluate our pipeline on five datasets that represent common bioimage analysis settings. The datasets exemplify a range of fluorescently labeled (sub-)cellular targets in mouse brain tissue with varying degrees of data ambiguity.
The PV in HC dataset published by Segebarth et al.11 describes indirect immunofluorescence labeling of Parvalbumin-positive (PV-positive) interneurons in the hippocampus. Morphological features are widely ramified axons projecting to neighbored neurons for soma-near inhibition of excitatory neuronal activity58. The axonal projections densely wrap around the somata of target cells. This occasionally causes data ambiguities when the somata of the PV-positive neurons need to be separated from the PV-positive immunofluorescent signal around the soma of neighbored cells. Thresholding approaches such as Otsu’s method (see Supplementary Note S2.2) typically fail at this task as it requires differentiating between rather brightly labeled somata that express PV in the cytosol vs. brightly labeled PV-positive axon bundles that can appear in the neighborhood.
The publicly available cFOS in HC dataset59 describes indirect immunofluorescent labeling of the transcription factor cFOS in different subregions of the hippocampus after behavioral testing of the mice11. The counting or segmentation of cFOS-positive nuclei is an often-used experimental paradigm in the neurosciences. The staining is used to investigate information processing in neural circuits60. The low SNR of cFOS labels for most but not all image features renders its heuristic segmentation a very challenging task. This results in a very high inter-expert variability after manual segmentation (see Segebarth et al.11 and Supplementary Fig. S2.1). We use 280 additional images of this dataset to demonstrate the out-of-distribution detection capabilities of deepflash2. There are no expert annotations available for the additional images; however, 24 images comprise characteristics that do not occur in the training data. We classified such partly out-of-distribution images into three different error categories for our study: blood vessels if the images contained blood vessels (13 images); folded tissue (4 images); fluorescent particles if there was at least one strongly fluorescent particle unrelated to the actual fluorescent label (7 images) (see examples in Supplementary Fig. S1.1).
The mScarlet in the PAG dataset shows an indirect immunofluorescent post-labeling of the red-fluorescent protein mScarlet, after viral expression in the periaqueductal gray (PAG). Here, microscopy images visualize mScarlet, tagged to the light-sensitive inhibitory opsin OPN3. The recombinant protein was delivered via stereotactic injection of an adeno-associated viral vector (AAV$\frac{2}{5}$-Ef1a-DIO-eOPN3-ts-mScarlet-ER) to the PAG. Optogenetics is a key technology in neuroscience that allows the control of neuronal activity in selected neuronal populations61,62. Consequently, the number of opsin-expressing neurons provides highly relevant information in optogenetic experiments. However, due to the substantial efforts that these analyses require, this data is rarely acquired2. Therefore, we chose this dataset of a recombinant opsin that shows a particularly low signal-to-noise ratio (Fig. 2) in order to evaluate the usability of deepflash2 for this commonly requested use-case.
The YFP in CTX dataset shows direct fluorescence of yellow fluorescent protein (YFP) in the cortex of so-called thy1-YFP mice. In thy1-YFP mice, a fluorescent protein is expressed in the cytosol of neuronal subtypes with the help of promoter elements from the thy1 gene63. This provides a fluorescent Golgi-like vital stain that can be used to investigate disease-related changes in neuron numbers or neuron morphology, for instance, for hypothesis-generating research in neurodegenerative diseases (e.g., Alzheimer’s disease). Here, computational bioimage analysis is aggravated by the pure intensity of the label that causes strong background signals by light scattering or out-of-focus light. Both can blur the signal borders in the image plane.
Finally, the GFAP in HC dataset shows indirect immunofluorescence signals of glial acidic fibrillary protein (GFAP) in the hippocampus. Anti-GFAP labeling is one of the most commonly used stainings in the neurosciences and is also used for histological examination of brain tumor tissue. Glial cells labeled by GFAP in the hippocampus show different morphologies (e.g., radial-like or star-like). GFAP-positive cells occupy separate anatomical parts64 (like balls in a ball bath). Thus, it is highly laborious to manually segment the spatial area of GFAP-positive single astrocytes in a brain slice. Here, the extensions of the GFAP-labeled astrocytic skeleton cannot be separated from parts of neighboring astrocytes, rendering a reliable instance separation and thus instance segmentation impossible. Albeit the signal is typically bright and very clear around the center of the cell, the signal borders of the radial fibers become ambiguous due to the 3D-ball-like structure, low SNR at the end of the fibers, and out-of-focus light interference.
A high-level comparison of the key dataset characteristics is provided in Table 1.Table 1Comparison of immunofluorescence datasetsPV in HCcFOS in HCmScarlet in PAGYFP in CTXGFAP in HCAnnotation targetSomataNucleiSomataSomataMorphologySemantic segmentationYesYesYesYesYesInstance segmentationYesYesYesYesNoTrain images3636121212Test images88888Experts554–54–53Additional images–280–––Fluorescence microsc. ConfocalConfocalLightLightLightSize (pixel)1024 × 10241024 × 10242752 × 22082752 × 2208580 × 580Resolution (px/μm)1.611.613.73.73.7
## Challenge datasets
We additionally evaluate the performance of deepflash2 on three recent biomedical imaging challenge datasets. The gleason challenge [2019] aims at the automatic Gleason grading (multiclass semantic segmentation) of prostate cancer from H&E-stained histopathology images22. The grading of prostate cancer tissue performed by different expert pathologists suffers from high inter-expert variability. Ground truth estimation was performed using STAPLE14. For undecided pixels, we assigned the segmentation of the expert with the highest score. Class 0 corresponds to benign or other tissue, class 1 to Gleason grade 3, class 2 to Gleason grade 4, and class 3 to Gleason grade 5.
The monuseg [2018] challenge aims at nuclei segmentation in digital microscopic tissue images23. The task is binary instance segmentation (class 1 nucleus, class 0 other/background).
The conic [2022] challenge aims at nuclei segmentation of H&E-stained histology images. The challenge is based on the Lizard dataset24. The images were acquired with a 20x objective magnification (about 0.5 microns/pixel) from six different data sources. They contain half a million labeled nuclei in colon tissue and require multiclass instance segmentation. Here, class 1 corresponds to the category epithelial, class 2 to lymphocyte, class 3 to plasma, class 4 to eosinophil, class 5 to neutrophil, and class 6 to connective tissue.
## Performance benchmarks
We benchmark the predictive performance of deepflash2 against a select group of well-established algorithms and tools. These comprise the U-Net2 and nnunet8 for both semantic and instance segmentation as well as two out-of-the-box baselines. We utilize Otsu’s method18 as a simple baseline for semantic segmentation and cellpose9 as a generic baseline for (cell) instance segmentation. Additionally, we benchmark deepflash2 against fine-tuned cellpose models and ensembles, showing superior performance of our method (see Supplementary Table S2.1). cellpose has previously proven to outperform other well-known methods for instance segmentation (e.g., Mask-RCNN19 or StarDist20).
For each dataset, we apply the tools as described by their developers to render the comparison as fair as possible. We train the U-Net2 on a $\frac{90}{10}$ train-validation-split for 10,000 iterations (learning rate of 0.00001 and the Adam optimizer41) using the authors’ TensorFlow 1.x implementation. This includes all relevant features, such as overlapping tile strategy and border-aware loss function. We derive the parameter values for the loss function (border weight factor (λ), border weight sigma (σsep), and foreground-background ratio (νbal) by means of Bayesian hyperparameter tuning: Parv in HC: λ = 25, σsep = 10, νbal = 0.66; cFOS in HC: λ = 44, σsep = 2, νbal = 0.23; mScarlet in PAG: λ = 15,σsep = 10, νbal = 0.66; YFP in CTX: λ = 15, σsep = 5, νbal = 0.85; GFAP in HC: λ = 1, σsep = 1, νbal = 0.85.
We train the self-configuring nnunet (version 1.6.6) model ensemble8 following the authors’ instructions provided on GitHub.
cellpose provides three pretrained model ensembles (nuclei, cyto, and cyto2) for out-of-the-box usage9. We select the ensemble with the highest score on the training data: cyto for Parv in HC and YFP in CTX, cyto2 for cFOS in HC, and mScarlet in PAG. During inference, we fix the cell diameter (in pixel) for each dataset: Parv in HC: 24; cFOS in HC: 15; mScarlet in PAG: 55; YFP in CTX: 50. We additionally provide a performance comparison for fine-tuned cellpose models and ensembles in Supplementary Note S2.2. We use the cellpose GitHub version with commit hash 316927e (August 26, 2021) for our experiments.
We repeat our experiments with different seeds to ensure that our results are robust and reproducible (see Supplementary Note S2.2). The experiments for training duration comparison are executed on the free platform Google Colaboratory (Nvidia Tesla K80 GPU, 2 vCPUs; times were extrapolated when the 12-h limit was reached) and the paid Google Cloud Platform (Nvidia A100 GPU, 12 vCPUs). The remaining experiments are executed locally (Nvidia GeForce RTX 3090) or in the cloud (Google Cloud Platform on Nvidia Tesla K40 GPUs).
## Experimental animals
The datasets mScarlet in PAG, YFP in CTX, and GFAP in HC were acquired for this study. Here, all mice were bred in the animal facility of the Institute of Clinical Neurobiology at the University Hospital of Würzburg, Germany, and housed under standard conditions (55 ± $5\%$ humidity, 21 ± 1 C, 12:12-h light:dark cycle) with access to food and water ad libitum. VGlut2-IRES-Cre knock-in mice65 (stock no. 208863), as well as Thy1-YFP mice63 (stock no. 003782), were obtained from Jackson Laboratory. Additionally, we used wild-type mice with the genetic background C57BL/6J (Charles River, CRL:027). Only male mice at ages between 4 and 8 months were used.
## Surgeries
The surgeries for mice in mScarlet in PAG were conducted as follows: Male VGlut2-IRES-Cre knock-in mice were injected at the age of 4 months, and adeno-associated virus (AAV) was used as vectors to deliver genetic material into the brain. AAV vectors encoding Cre-dependently for the inhibitory opsin eOPN366 were injected into the periaqueductal gray (PAG) bilaterally. The construct EF1α-DIO-eOPN3-ts-mScarlet-ER was kindly provided by Simon Wiegert, Center for Molecular Neurobiology Hamburg, Germany. Respective AAV vectors were produced in house (AAV$\frac{2}{5}$ capsid). For stereotactic surgeries, animals were prepared with an administration of Buprenorphin (Buprenorvet, Bayer). Mice were deeply anesthetized with 4–$5\%$ isoflurane/O2 (Anesthetic Vaporizer, Harvard Apparatus). Animals were fixed into the stereotactic frame (Kopf, Model 1900), and anesthesia was maintained with 1.5–$2\%$ isoflurane/O2. Subcutaneous injection of Ropivacaine (Naropin Aspen) was used for local analgesia before opening the scalp. Craniotomies were performed at bregma coordinates AP −4.5 mm, ML ±0.6 mm. A glass pipette (Drummond Scientific) was filled with the viral vector and lowered to the target depth of −2.9 mm from bregma. A volume of 100 nl was injected with a pressure injector (NPI electronic). After injection, the pipette was held in place for 8 min before retracting. The wound was closed, and the animal was treated with a subcutaneous injection of Metacam (Metacam, Boehringer Ingelheim) for post-surgery analgesia. After 6 weeks of expression time, animals were perfused, and brain tissue was dissected for further analysis.
## Sample preparation
Following intraperitoneal injection (for YFP in CTX and GFAP in HC): 12 μl/g bodyweight of a mixture of ketamine (100 mg/kg; Ursotamin, Serumwerk) and xylazine (16 mg/kg; cp-Pharma, Xylavet, Burgdorf, Germany); for mScarlet in PAG: urethane (2g/kg; Sigma-Aldrich) at a volume of 200 μl diluted in $0.9\%$ sterile sodium chloride solution), the depth of the anesthesia was assessed for each mouse by testing the tail and the hind limb pedal reflexes. Upon absence of both reflexes, mice were transcardially perfused using phosphate-buffered saline (PBS) with (for YFP in CTX and GFAP in HC) or without (mScarlet in PAG) $0.4\%$ heparin (Heparin-Natrium-25000, ratiopharm), and subsequently a $4\%$ paraformaldehyde solution in PBS for fixation. After dissection, brains were kept in $4\%$ paraformaldehyde solution in PBS for another 2 h (for YFP in CTX and GFAP in HC) or overnight (for mScarlet in PAG) at 4 °C. Brains were then washed twice with PBS and stored at 4 °C until sectioning. For cutting, brains were embedded in $6\%$ agarose in PBS, and a vibratome (Leica VT1200) was used to cut 40 μm (for YFP in CTX and GFAP in HC) or 60 μm (for mScarlet in PAG) coronal sections. Immunohistochemistry was performed in 24-well plates with up to three free-floating sections per well in 400 μl solution and under constant shaking.
For YFP in CTX and GFAP in HC: brain sections were incubated for 1 h at room temperature in 100 mM Tris-buffered glycine solution (pH 7.4). Slices were then incubated with blocking solution ($10\%$ horse serum, $0.3\%$ Triton X100, $0.1\%$ Tween 20, in PBS) for 1 h at room temperature. Subsequently, sections were labeled with primary antibodies at the indicated dilutions in blocking solution for 48 h at 4 °C (rabbit anti-GFAP, Acris, DP014, 1:200; chicken anti-GFP, Abcam, Ab13970, 1:1000). Primary antibody solutions were washed off thrice with washing solution ($0.1\%$ Triton X100 and $0.1\%$ Tween 20 solution in PBS) for 10 min each. Sections were then incubated with fluorescently labeled secondary antibodies at 0.5 μg/ml in blocking solution for 1.5 h at room temperature (goat anti-chicken Alexa-488 conjugated, Invitrogen; donkey anti-rabbit Cy3 conjugated, Jackson ImmunoResearch). Finally, sections were incubated again twice for 10 min with the washing solution and once with PBS at room temperature, prior to embedding in Aqua-Poly/Mount (Polysciences).
For mScarlet in PAG: brain sections were incubated in blocking solution ($10\%$ donkey serum, $0.3\%$ Triton X100, $0.1\%$ Tween in 1x TBS) for 2 h at room temperature. For labeling, sections were incubated for 2 days at 4 °C with rabbit anti-RFP (Biomol, 600-401-379, 1:1000) in $10\%$ blocking solution in 1X TBS-T. Sections were washed thrice with washing solution for 10 min each and then incubated with the fluorescently labeled secondary antibody at 0.5 μg/ml (donkey anti-rabbit Cy3, Jackson ImmunoResearch). Following a single wash with 1x TBS-T for 20 min at room temperature, sections were incubated with DAPI (Roth, 6335.1, 1:5000) in TBS-T for 5 min and eventually washed twice with 1x TBS-T. The labeled sections were embedded in an embedding medium (2.4 g Mowiol, 6 g Glycerol, 6 ml ddH2O, diluted in 12 ml 0.2 M Tris at pH 8.5).
## Image acquisition, processing, and manual analysis
Image acquisition for mScarlet in PAG, YFP in CTX, and GFAP in HC was performed using a Zeiss Axio Zoom. V16 microscope, equipped with a Zeiss HXP 200C light source, an Axiocam 506 mono camera, and an APO Z 1.5x/0.37 FWD 30 mm objective. Images covering 743.7 × 596.7 μm of the corresponding brain regions at a resolution of 3.7 px/μm were acquired as 8-bit images. To foster manual ROI annotation, these raw 8-bit images were enhanced for brightness and contrast using the automatic brightness and contrast enhancer implemented in Fiji67. The corresponding image features of interest were manually annotated by Ph. D.-level neuroscientists.
## Statistics and reproducibility
To evaluate the predictive performance of deepflash2 and the benchmark tools, we used the train-test split from Segebarth et al.11 for the PV in HC and cFOS in HC datasets. Here, we removed one image (id 1608) from each test set to ensure a balanced evaluation with eight test images across all five fluorescent datasets in this study. The datasets mScarlet in PAG, YFP in CTX, and GFAP in HC were randomly split into 12 images used for training and eight images used for evaluation. The challenge datasets were randomly split into $80\%$ train and $20\%$ test data, resulting in 196 training and 49 test images for the gleason dataset and 190 training and 48 test images for the conic dataset. For the monuseg dataset, we used the provided train-test split from the challenge, comprising 30 training and 15 test images.
All computational experiments were independently repeated three times with similar results.
No statistical method was used to predetermine the sample size. The experts were not blinded during the image annotation process; however, they did not receive information on the annotations of the other experts.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary Description of Additional Supplementary Files Supplementary Software Peer Review File The online version contains supplementary material available at 10.1038/s41467-023-36960-9.
## Source data
Source Data
## Peer review information
Nature Communications thanks Klaus Maier-Hein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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title: Neuraminidase 1 promotes renal fibrosis development in male mice
authors:
- Qian-Qian Chen
- Kang Liu
- Ning Shi
- Gaoxiang Ma
- Peipei Wang
- Hua-Mei Xie
- Si-Jia Jin
- Ting-Ting Wei
- Xiang-Yu Yu
- Yi Wang
- Jun-Yuan Zhang
- Ping Li
- Lian-Wen Qi
- Lei Zhang
journal: Nature Communications
year: 2023
pmcid: PMC10043283
doi: 10.1038/s41467-023-37450-8
license: CC BY 4.0
---
# Neuraminidase 1 promotes renal fibrosis development in male mice
## Abstract
The functions of the influenza virus neuraminidase has been well documented but those of the mammalian neuraminidases remain less explored. Here, we characterize the role of neuraminidase 1 (NEU1) in unilateral ureteral obstruction (UUO) and folic acid (FA)-induced renal fibrosis mouse models. We find that NEU1 is significantly upregulated in the fibrotic kidneys of patients and mice. Functionally, tubular epithelial cell-specific NEU1 knockout inhibits epithelial-to-mesenchymal transition, inflammatory cytokines production, and collagen deposition in mice. Conversely, NEU1 overexpression exacerbates progressive renal fibrosis. Mechanistically, NEU1 interacts with TGFβ type I receptor ALK5 at the 160-200aa region and stabilizes ALK5 leading to SMAD$\frac{2}{3}$ activation. Salvianolic acid B, a component of Salvia miltiorrhiza, is found to strongly bind to NEU1 and effectively protect mice from renal fibrosis in a NEU1-dependent manner. Collectively, this study characterizes a promotor role for NEU1 in renal fibrosis and suggests a potential avenue of targeting NEU1 to treat kidney diseases.
The influenza virus neuraminidase has been well documented, yet the functions of mammalian neuraminidases remain less explored. Here, the authors show that neuraminidase 1 promotes renal fibrosis development by interacting with ALK5 to activate SMAD$\frac{2}{3.}$
## Introduction
Renal fibrosis is an inevitable consequence of almost all forms of progressive chronic kidney disease (CKD)1. Despite its high prevalence and severe morbidity and mortality, there is currently limited effective therapy that can halt or reverse renal fibrogenesis and the progression of CKD2. Comprehensive characterization of the complex mechanisms and signaling mediators that underpin renal fibrosis could propose new therapeutic avenues.
Kidney fibrosis is a multifactorial and progressive disease, involving diverse cell types of renal and extrarenal origin. Four major cell types are involved in CKD progression: tubular epithelial cells (TEC), myofibroblasts, endothelial cells, and immune cells3,4. The different types of cells play intricate, distinct, and interrelated roles. TEC constitute a major component of the kidney that respond to injuries. Previous studies have highlighted resident TEC as initiators of human kidney fibrosis rather than victims of same5. Injured TEC undergo partial epithelial-to-mesenchymal transition (EMT) but still reside within the basement membrane of the tubules. They are characterized by the acquisition of mesenchymal features, and co-expression of epithelial and mesenchymal cell markers. As a consequence, damaged TEC release paracrine signals such as pro-inflammatory and pro-fibrotic factors into the renal interstitium, thus reshaping the microenvironment to promote both inflammation and fibrogenesis6. A diverse array of molecular determinants that reside in the TEC have been characterized to play critical roles in renal fibrosis and CKD. Some act as promotors of TEC injury and tubulointerstitial fibrosis, for instance, Snail, Twist, Wnt9a, Notch1, Hif1α, and Kim17–12. Others such as Mst$\frac{1}{2}$ and Atg5 play a suppressor role13,14.
Neuraminidases or sialidases are a family of glycoside hydrolase enzymes that catalyze the removal of sialic acid from viral and cellular glycoconjugates15. The most widely studied neuraminidase is on the surface of the influenza virus, where the enzyme removes sialic acid from host receptors to facilitate viral release. In mammals, neuraminidases have been shown to be involved in diverse physiological and pathological processes. First, the mammalian neuraminidase family consists of four members (NEU1-NEU4), responsible for the initial step of degradation of glycoconjugates by removing sialic acids16. Among them, NEU1 and NEU4 are typically located in lysosomes. NEU1 deficiency leads to sialidosis, a disease characterized by tissue accumulation of sialo-glycopeptides and sialo-oligosaccharides17. Second, neuraminidases participate in post-translational modifications via desialylation, and modulate the structure and function of glycoproteins18,19. Third, neuraminidases can affect protein functions via protein–protein interaction. Our previous work showed that neuraminidases bind to transcriptional factor GATA4 to drive cardiac hypertrophy20. Fourth, we also reported that neuraminidases activation resulted in accumulation of circulating N-acetyl-neuraminic acid, a signaling metabolite that can trigger RhoA and Cdc42-dependent myocardial injury21. On account of their various functions, mammalian neuraminidases have been shown to play an emerging role in several human diseases, including autoimmune22,23, cardiovascular diseases20,24,25 cancers26, neurodegenerative disorders27, and lung diseases28,29. Design of the specific inhibitors targeting neuraminidases have the potential to treat these diseases30,31. The role of neuraminidases in CKD and TGFβ signaling, however, remain largely unexplored.
To address the existing scientific gray areas and loopholes in respect of the neuraminidases, this work aimed to study their roles in renal fibrosis. We detected the neuraminidase 1 (NEU1) expression in patients with renal fibrosis, and in mice subjected to unilateral ureteral obstruction (UUO) or administered folic acid. TEC-specific NEU1 knockout and overexpression mice were generated to characterize the role of NEU1 in renal fibrosis progression. PCR array, co-immunoprecipitation, and surface plasmon resonance (SPR) were employed to investigate the underlying mechanisms by which NEU1 promotes renal fibrosis. In addition, natural compounds were screened to bind to mammalian NEU1 and protect kidneys from injury in mice.
## NEU1 was elevated in TEC of CKD patients
We first examined expressions of the four neuraminidase members (NEU1-NEU4) by analyzing renal transcriptomics database Nephroseq (https://www.nephroseq.org/resource/login.html). In the ‘Ju CKD Tublnt’ dataset, the mRNA of NEU1 but not NEU2-NEU4 was significantly upregulated in kidney biopsy tissues of the CKD patients ($$n = 123$$) compared with controls ($$n = 31$$) (Fig. 1a). Specifically, NEU1 was elevated in most types of CKD including IgA ($$n = 25$$), diabetic kidney disease ($$n = 17$$), lupus nephritis ($$n = 32$$), focal segmental glomerulosclerosis ($$n = 17$$), and membranous glomerulonephritis ($$n = 18$$), but not in minimal change disease ($$n = 14$$) (Supplementary Fig. 1a). The NEU1 was also highly expressed in ‘Ju CKD Glom’ dataset ($$n = 149$$ for CKD and $$n = 21$$ for healthy control) and GSE66494 ($$n = 53$$ for CKD and $$n = 8$$ for healthy control) (Fig. 1b, c).Fig. 1NEU1 is significantly upregulated in kidneys from patients with CKD.a Relative NEU1, NEU2, NEU3, and NEU4 mRNA levels. Data analysis from Nephroseq database (‘Ju CKD Tublnt’ dataset, median-centered log2). $$n = 31$$ samples in control group, $$n = 123$$ samples in CKD group. Unpaired t-test. ns, no significant difference. nd, not detected. b The expression of NEU1 (median-centered log2) in kidney specimens from control ($$n = 21$$ samples) and patients with chronic kidney disease (CKD, $$n = 149$$ samples). Unpaired t-test. Data analysis from Nephroseq database (‘Ju CKD Glom’ dataset). c The mRNA levels of NEU1 in kidney specimens of CKD ($$n = 53$$ samples) and control ($$n = 8$$ samples) in GSE66494 dataset (Probe ID: A_24_P394533). Unpaired t-test. d Tissue adjacent sections of kidney from patients with non-renal fibrosis or renal fibrosis by immunohistochemistry, Masson staining, and HE staining. Scale bar = 20 μm. NRF non-renal fibrosis, RF Renal fibrosis. $$n = 8$$ samples per group. e–g Quantification of NEU1 expression (e), fibrotic area (f), and score of kidney damage (g) based on immunohistochemistry, Masson, or HE staining in (d). Data were presented as mean ± SD. $$n = 8$$ samples per group. Unpaired two-tailed t-test. IOD: integrated optical density. h The correlation of NEU1 expression and degree of tubular degeneration ($$n = 16$$, Pearson χ2 test). i–k Pearson’s correlation of NEU1 with serum creatinine level (i), blood urea nitrogen (BUN) (j), and glomerular filtration rate (GFR) (k) ($$n = 16$$, Pearson χ2 test). l–n Representative images of co-immunofluorescence staining of NEU1 and KIM1 in kidney tissues of non-renal fibrosis and renal fibrotic patients (l). Fluorescence intensity of NEU1 and KIM1 in diagram k-up (m) and k-down (n), Image J software was used for statistics. Scale bars = 20 μm. $$n = 3$$ samples per group. a–c Data are presented as box-and-whisker plots, solid line inside box indicates the median, the bottom and top of box represent first and third quartiles, and the bottom and top whisker show the minimum and maximum, respectively. All tests were two-tailed.
Next, we analyzed 16 microdissected human kidney samples collected from patients with renal fibrosis ($$n = 8$$) and without renal fibrosis (no specific pathologic alterations by biopsy procedures, $$n = 8$$). Clinical demographics of these subjects are provided in Supplementary table 1. Patients with renal fibrosis showed severe collagen deposition and kidney injury as captured by Masson and hematoxylin-eosin (HE) staining (Fig. 1d, f, g). Immunohistochemistry (Fig. 1d, e) and immunofluorescence (Supplementary Fig. 1b, c) revealed that NEU1 protein levels were significantly higher in patients with renal fibrosis than without renal fibrosis. In addition, the levels of NEU1 showed a strong positive correlation with the score of tubular injury (Fig. 1h), the level of serum creatinine (Fig. 1i), and the blood urea nitrogen (Fig. 1j), but showed a negative correlation with the estimated glomerular filtration rate (Fig. 1k). Immunofluorescence (Fig. 1l–n) showed that the increase of NEU1 content was mainly co-localized with kidney injury molecule 1 (KIM1), a biomarker of injured epithelial cells in acute kidney injury and CKD32,33. Analysis of Nephroseq database also showed that NEU1 expression was negatively correlated with glomerular filtration rate (Supplementary Fig. 1d) and was mainly expressed in the renal cortex part of the kidney (Supplementary Fig. 1e).
## NEU1 was upregulated in fibrotic kidneys of mice
Next, we determined the protein levels of NEU1 in mice. Renal fibrosis model was established by unilateral ureteral obstruction (UUO) or folic acid stimulation. NEU1 mRNA (Fig. 2a) and protein (Fig. 2b–d) were significantly increased in the kidneys of the fibrotic mice in response to UUO. These elevations were time-dependent manner (Fig. 2a, b). Staining of the kidney sections showed increased localization of NEU1 in the TEC, but not in the macrophages (Fig. 2e, f). NEU1 expression was also increased in the kidneys of folic acid-induced renal fibrosis (Fig. 2b–d, g). Next, we analyzed the fibrosis indices and then correlated them with NEU1 expression. The results showed that the mRNA expressions of Acta2, Vim, Col1a1, Col3a1, and Tgfβ greatly increased in fibrotic kidney compared with control group (Fig. 2h, i). Of note, the level of NEU1 showed a positive correlation with these fibrosis indices (Fig. 2j–s).Fig. 2NEU1 is elevated in mouse fibrotic kidneys.a NEU1 mRNA levels in kidneys of mice subjected to unilateral ureteral ligation (UUO) for 0, 3, 7, 10, and 12 days. $$n = 4$$ mice per group. Unpaired two-tailed t-test. Data were presented as mean ± SD. b Western blots of NEU1 levels in kidney from mice subjected to UUO for 0, 3, 7, 10, and 12 days or administered folic acid (FA, 250 mg/kg, i.p.) injection for 4 weeks. $$n = 3$$ mice per group. c, d Immunohistochemistry of kidney sections (c) and quantitative results (d) from mice after UUO surgery for 10 days or folic acid injection for 4 weeks. The NEU1 was determined with IOD by Image-Pro Plus 6.0. $$n = 3$$ mice per group. Scale bar, 20 μm. Data were presented as mean ± SD. Unpaired two-tailed t-test. IOD, integrated optical density. e–g Immunofluorescence images of NEU1 in kidney from mice subjected to UUO (e, f) or folic acid (g). Na+/K+-ATPase was used as tubular epithelial cell marker, CD68 was used as macrophage marker. $$n = 3$$ mice per group. Scale bar, 50 μm. h, i Neu1, Acta2, Vim, Col1a1, Col3a1, and Tgfβ mRNA level in kidney from mice after UUO surgery for 10 days (h) or folic acid injection for 4 weeks (i). $$n = 6$$ mice per group. Data were presented as mean ± SD. Unpaired two-tailed t-test. j–s Pearson’s correlation of NEU1 with Acta2, Vim, Col1a1, Col3a1, and Tgfβ mRNA. $$n = 12$$, two-tailed Pearson χ2 test.
## NEU1 mediated TGFβ-induced changes in HK-2 cell
Human tubular epithelial HK-2 cells were stimulated by TGFβ, a commonly used stimulator to induce fibrosis, to study the role of NEU1 in TEC injury. In response to TGFβ stimulation, NEU1 expression was increased at the mRNA (Supplementary Fig. 2a) and protein levels (Supplementary Fig. 2b). E-Cadherin (CDH1), a marker of epithelium cells, was upregulated in NEU1-knockdown (Supplementary Fig. 2c, d) HK-2 cells in the presence of TGFβ. The mesenchymal cell markers stimulated by TGFβ, such as Vimentin (VIM), Snail (SNAI1), and Slug (SNAI2) were significantly downregulated when NEU1 was knocked down (Supplementary Fig. 2e, f). Conversely, NEU1 overexpression (Supplementary Fig. 2g, h) promoted TGFβ-induced Fibronectin 1, KIM1, and EMT-associated genes transcription and protein expression (Supplementary Fig. 2i, j). Overexpressed NEU1 did not form aggregation when the cells transfected with NEU1 full-length plasmid (Supplementary Fig. 2k).
## TEC-specific NEU1 deletion inhibited renal fibrosis in mice
To investigate the role of NEU1 in kidney fibrosis, we generated mice with TEC-specific deleted Neu1 using a Cre/loxP-dependent conditional gene-targeted approach. Mice homozygous for the Neu1-loxP (fl)-targeted allele (Neu1fl/fl) were crossed with TEC-specific Ksp1.3 Cre lines (Supplementary Fig. 3a). The mRNA level (Supplementary Fig. 3b), protein expression (Supplementary Fig. 3c, d), and enzyme activity (Supplementary Fig. 3e) confirmed the reduction of NEU1 expression in Neu1fl/fl mice (Neu1CKO) compared with Neu1fl/fl littermates without Cre recombinase activity (control mice). Electron microscopy showed that there were no significant morphological differences between Neu1CKO mice and the control mice in the podocytes, glomerular basement membrane, the blood vessel, and tubular morphology (Supplementary Fig. 3f). A minor change is that a few vacuoles (examples indicated by asterisks) were detected in the cytosol of tubular cells in Neu1CKO mice but not in the control mice (Supplementary Fig. 3f). Next, we performed immunohistochemical assay for LAMP1, an indicator of lysosomal storage and exocytosis17,29. LAMP1 expression was slightly increased but not significantly altered ($$p \leq 0.39$$) in the kidney tissues of Neu1CKO mice compared with control groups (Supplementary Fig. 3g).
In response to UUO (Fig. 3a), NEU1 knockout significantly improved morphology (Fig. 3b, c), reduced collagen deposition (Fig. 3b, d), inhibited tubular necrosis and tubulointerstitial inflammation (Fig. 3b, e, f), suppressed macrophage infiltration (Fig. 3b, g) and nuclear phosphorylated NF-κB (pNF-κB) in tubular cells (Fig. 3b, h). NEU1 deficiency markedly inhibited UUO-induced KIM1 expression (Fig. 3i). Immunofluorescence showed that partial EMT was induced by UUO, as evidenced by the remnants of TEC in the basement membrane and co-expression of epithelial cell marker E-Cadherin and mesenchymal cell marker Vimentin (Fig. 3j, k). This EMT progression was inhibited by NEU1 knockdown (Fig. 3j, k). In line with this, the mRNA and protein of E-Cadherin were dramatically restored (Fig. 3l and Supplementary Fig. 4a, b), while Snail, Slug, α-SMA, and Vimentin were significantly inhibited in the kidneys of Neu1CKO mice (Fig. 3l and Supplementary Fig. 4a, b).Fig. 3TEC-specific deletion of Neu1 alleviates UUO-induced mouse renal fibrosis.a Scheme of the experimental approach. UUO, unilateral ureteral obstruction. b The gross appearance of kidneys (Scale bar, 1 mm), Hematoxylin and eosin (HE, Scale bar, 20 μm), Masson’s trichrome staining (Scale bar, 20 μm), immunohistochemistry staining of CD68, and p-NFκB (Scale bar, 50 μm) from control (Ctrl) and Neu1 CKO mice 10 days after UUO. The red arrow indicates positive cells. $$n = 3$$ mice per group. c The ratio of left renal weight to tibia length (TL). $$n = 12$$ mice per group. ( d) Statistical results for interstitial collagen analyzed by Image Pro-Plus software. $$n = 3$$ mice per group. e, f Morphometric analysis, assessing percentage of tubular necrosis index (e) and tubulointerstitial inflammation index (f). $$n = 3$$ mice per group. g, h Quantitative results of CD68 (g) and p-NFκB (h). $$n = 3$$ mice per group. i Kim1 mRNA levels. $$n = 4$$ mice per group. j Images of immunofluorescence staining. Scale bars, 20 μm. k *Statistical analysis* of staining double-positive cells of E-cadherin and Vimentin. HPF, high power field. $$n = 3$$ mice per group. l, m EMT and extracellular matrix-associated gene mRNA levels. $$n = 3$$ mice per group. Gene expression levels were normalized to Gapdh. All statistic data were presented as mean ± SD, two-way ANOVA followed by Tukey’s multiple comparisons test.
PCR array involving 84 fibrosis-related mRNAs showed that the TGFβ family, MMP members, collagens, inflammatory cytokines, chemokines, and adhesion molecules were significantly stimulated in the kidneys of mice in response to UUO, and were evidently inhibited by NEU1 knockout (Supplementary Fig. 4c–e). Further RT-qPCR confirmed that NEU1 knockout dramatically inhibited UUO-induced gene expressions of inflammatory cytokines (Il1β, Il6, and Il18), chemokines (Ccl1, Ccl2, Ccl3, Ccl4, Ccl5, Cxcr10, and Cxcr12), tumor necrosis factor (Tnfα), interferons (Ifnα, Ifnβ, and Ifnγ), adhesion molecules (Icam and Vcam) (Supplementary Fig. 4f, g) and fibrogenic factors (Col1a1, Col3a1 Fn1, Tgfb1, and Tnc) (Fig. 3m).
In another renal fibrosis model induced by folic acid (Fig. 4a), Neu1CKO mice also showed significantly improved morphology and renal weight (Fig. 4b, c). Renal function-associated creatinine and blood urea nitrogen were considerably improved in Neu1CKO mice compared with littermate control (Fig. 4d, e). NEU1 knockdown markedly inhibited folic acid-induced KIM1 expression (Fig. 4f). HE and Masson staining indicated that the kidneys of the Neu1CKO mice developed relatively less tubular injury and interstitial fibrosis (Fig. 4g–i). Macrophage infiltration (Fig. 4g, j), EMT marker gene expression (Fig. 4l and Supplementary Fig. 5a, b), pro-inflammatory cytokines (Fig. 4g, k and Supplementary Fig. 5c), chemokines (Supplementary Fig. 5d), and fibrogenic factors (Fig. 4m) were noticeably reversed due to NEU1 deficiency in the renal tubular epithelial cells. Fig. 4TEC-specific deletion of Neu1 alleviates folic acid-induced renal fibrosis in mice.a Scheme of the experimental approach. The mice were intraperitoneally injected with folic acid (250 mg/kg). b The gross appearance of whole kidneys from control (Ctrl) and Neu1 CKO mice 4 weeks after folic acid injection. Scale bar, 1 mm. c The ratio of left renal weight to body weight (BW). $$n = 6$$ mice per group. d, e Creatinine (d) and blood urea nitrogen (e) in serum measured by ELISA. $$n = 6$$ mice per group. f Kim1 mRNA levels. $$n = 4$$ mice per group. g Hematoxylin and eosin (HE), Masson’s trichrome staining, and immunohistochemistry staining of CD68 and p-NFκB in kidney sections from Ctrl and Neu1 CKO mice 28 days after folic acid administration. $$n = 3$$ mice per group. Scale bar, 50 μm. The red arrow indicates positive cells. h–k Quantification of tubular injury score (h), fibrosis area (i), staining of CD68 (j), and p-NFκB (k). $$n = 3$$ mice per group. l, m EMT and extracellular matrix associate gene mRNA level. $$n = 4$$ mice per group. Gene expression levels were normalized to Gapdh. All statistic data were presented as mean ± SD. Two-way ANOVA followed by Tukey’s multiple comparisons test. All tests were two-tailed.
## NEU1 overexpression augmented UUO-induced renal fibrosis in mice
Besides loss-of-function, a gain-of-function approach was performed using adeno-associated virus serotype 9 encoding NEU1 (AAV9-NEU1) and AAV9-Ctrl (Fig. 5a). The virus was injected in situ in the cortex of the kidney in mice (Fig. 5a). The mRNA, protein expression, and enzyme activity of NEU1 were significantly increased in the kidneys after injection of AAV9-NEU1 for 5 weeks (Fig. 5b, c and Supplementary Fig. 6a, b). Lysosomal protective protein/cathepsin A (PPCA), a chaperone of NEU1 indispensable for catalytic activation, was also increased at 55 kDa in kidney tissues of NEU1-overexpression mice (Supplementary Fig. 6c). We observed a slight elevation of the mature PPCA subunit at 32 kDa and no significant difference at 20 kDa between the two groups (Supplementary Fig. 6c). The immunofluorescence results showed that AAV9-NEU1 was successfully transduced into the TEC (Supplementary Fig. 6d). As expected, NEU1 overexpression exacerbated the UUO-induced renal shriveled (Fig. 5d, e), tubular expansion (Fig. 5d, f), and collagen deposition both in the cortex and medulla of the kidneys (Fig. 5d, g). RT-qPCR, western blots, and immunohistochemistry indicated that NEU1 overexpression augmented UUO-induced KIM1 expression (Fig. 5h) and EMT progression in the kidneys (Fig. 5i–n and Supplementary Fig. 6e, f). UUO-stimulated macrophage infiltration (Supplementary Fig. 6g), chemokines, pro-inflammatory cytokines, interferons, adhesion molecules, and fibrogenic factors expressions were also aggravated by NEU1 overexpression (Fig. 5o and Supplementary Fig. 6h, i).Fig. 5NEU1 overexpression aggravates UUO-induced renal fibrosis.a Schematic diagram of NEU1 overexpression in mice. Mice were in situ injected with AAV9 encoding NEU1 or scramble. After the injection for 5 weeks, the mice were subjected to UUO surgery. b mRNA levels of Neu1 in the cortices of kidneys. $$n = 3$$ mice per group. c NEU1 protein levels in the kidneys of AAV9-Ctrl and AAV9-NEU1 mice. $$n = 2$$ mice per group. d The gross appearance of kidneys (Scale bar, 1 mm), kidney cross-sections stained with HE (Scale bar, 50 μm), and Masson’s trichrome (Scale bar, 100 μm). $$n = 6$$ mice per group. e The ratio of left renal weight to body weight (BW, mg/g). AAV9-Ctrl, $$n = 11$$; AAV9-NEU1, $$n = 10$.$ f *Statistical analysis* of tubular injury score. $$n = 6$$ mice per group. g Statistical results for interstitial collagen in d analyzed by Image Pro-Plus software. $$n = 6$$ mice per group. h Kim1 mRNA level. $$n = 3$$ mice per group. i mRNA levels of the indicated genes in kidneys. $$n = 3$$ mice per group. Gene expression levels were normalized to Gapdh. j–n Images of immunohistochemical staining using indicated antibodies. Scale bars, 20 μm. $$n = 6$$ mice per group. o mRNA levels of the indicated genes in kidneys. $$n = 3$$ mice per group. All statistic data were presented as mean ± SD. Unpaired two-tailed t-test.
## NEU1 interacted with ALK5 at the 160–200 region
In an effort to decode the underlying mechanism by which NEU1 promoted renal fibrosis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed by mapping differentially expressed genes of PCR array into the website (http://www.genome.jp/kegg) to identify hub signaling pathway. The correlation analysis identified top 20 enriched KEGG pathways that are potential to be involved in NEU1-mediated renal fibrosis (Supplementary Fig. 7a). The top three enriched KEGG pathways are AGE-RAGE signaling pathway in diabetic complications, TGF-β signaling pathways, and proteoglycans in cancers (Supplementary Fig. 7a). Since TGFβ is the predominant pathogenic factor that drives EMT and fibrosis in CKD34, we then centered on the TGF-β signaling pathway.
Upon TGFβ stimulation, the TGF-β type I (activin receptor-like kinases 1 to 7, ALK1-7) and type II receptors (AMHR2, TGFBR2, and ACVR2B) assemble a heterotetrameric receptor complex at the initial stage35. Subsequently, we tested the possibility of direct interaction of NEU1 with TGF-β receptors. Co-immunoprecipitation (Co-IP) assay showed that NEU1 selectively bound to ALK5 with a strong affinity, but could not bind to ALK2, ALK3, ALK6, ALK7, AMHR2, and TGFBR2 among the TGFβ receptor family (Fig. 6a, b). It appeared that NEU1 could also moderately interact with ALK1, ALK4, and ACVR2B. The interaction of NEU1 with ALK5 was confirmed using Co-IP combined with mass spectrometry (Supplementary Fig. 7b and Supplementary Data 1). Immunofluorescence showed that NEU1 was co-localized with ALK5 in human fibrotic kidneys (Fig. 6c), indicating their potential interaction. The interaction between NEU1 and ALK5 was enhanced in response to TGFβ stimulation in HK-2 cells (Fig. 6d, e).Fig. 6NEU1 interacts with GS domain of ALK5.a Schematic representation of the full-length forms of transforming growth factor β family proteins (ALK1, ALK2, ALK3, ALK4, ALK5, ALK6, ALK7, AMHR2, ACVR2B, and TGRBR2 receptors). The red “+” indicates the combination with NEU1. ECD, extracellular domain; TM, transmembrane domain; GS, glycine-serine repeats; STK, serine/threonine kinase domain. b Co-immunoprecipitation of NEU1 with transforming growth factor β family proteins in HEK293T. Two independent experiments were performed. c Confocal images of NEU1 (red) and ALK5 (green) localization in the fibrotic kidney of patients. Scale bar, 20 μm. d, e Co-immunoprecipitation of NEU1 and ALK5 in HK-2 cells stimulated with TGFβ (10 ng/ml) for 24 h. Two independent experiments were performed. f Scheme of NEU1 and ALK5 fusion proteins used for bimolecular fluorescence complementation (BiFC) analysis. g, h BiFC signals were detected in HK-2 cells. Representative fluorescence images of HK-2 cells co-expression of NEU1-VC155 and ALK5-VN173 plasmid without (g) or with (h) TGFβ stimulation. Scale bar, 10 μm. The magnified image scale was 1 μm. i Schematic diagram of in situ proximity ligation assay (NEU1-ALK5 PLA). j Interaction between NEU1 and ALK5 (NEU1-ALK5, red arrow) was analyzed by PLA in the fibrotic kidney of patients. Scale bars, 10 μm. k The interaction between NEU1 and ALK5162-403aa tested by SPR. The frequency response and fitting curves were displayed, two-tailed Pearson’s test. l Co-immunoprecipitation of NEU1 and ALK5160-200aa in HEK293T cells. m Interaction between NEU1 and ALK5 (NEU1-ALK5, red) was analyzed by PLA in HK-2 cells transfected with ALK5160-200aa plasmid. Scale bars, 10 μm. n Co-immunoprecipitation of NEU1 and ALK5 in HEK293T cells transfected with ALK5160-200aa plasmid. c, g, h, j and l–n were repeated three times independently with similar results.
To explore the localization of these interactions, we used bimolecular fluorescence complementation (BiFC) to detect the direct interaction between NEU1 and ALK5 in HK-2 cells. If binding happens, the VC155 fragment fused to NEU1 and the VN173 fused to ALK5 are brought into proximity and produce a fluorescent signal (Fig. 6f). Of note, fluorescent signals were much higher in TGFβ group than in control group, suggesting a strong binding of NEU1 with ALK5 upon TGFβ stimulation (Fig. 6g, h). We then used lyso-tracker, mito-traker, and CM-tracker to observe the localization of the interaction between NEU1 and ALK5. The results showed that fluorescent signals of NEU1 and ALK5 interaction were co-localized with lysosomes, mitochondrion, but less co-localized with cell plasma membrane (Supplementary Fig. 7c). Another technology, in situ proximity ligation assay (PLA), was also performed to validate the interaction of NEU1 and ALK5 in fibrotic kidney of patient. PLA used DNA hybridization and DNA amplification steps with fluorescent probes to visualize interacting proteins (Fig. 6i). In fibrotic kidney of patient, PLA results showed strong fluorescent signals in TECs (Fig. 6j), suggesting a direct interaction between NEU1 and ALK5.
As a further characterization of NEU1-ALK5 interaction, we determined the specific binding domain of ALK5. ALK5 consists of an extracellular domain (amino acids, 1–125), a transmembrane domain (amino acids, 126–174), a GS domain (amino acids, 175–204), and Ser/Thr kinase domain (amino acids, 205–495) (Supplementary Fig. 7d)36. Surface plasmon resonance showed that NEU1 displayed a favorable binding affinity (Fig. 6k) to the amino acid 162–403 region of ALK5 with an estimated equilibrium dissociation constant (KD) of 3.14 nM (Fig. 6k), but could not bind to 1–125 or 200–503 region using commercially available recombinant proteins (Supplementary Fig. 7e, f). It was thus reasonable to predict that the 160–200 amino acid region of ALK5 is the NEU1-binding domain (Supplementary Fig. 7d). Furthermore, we constructed a Flag-Tag plasmid containing 160–200 amino acid sequences, and observed that NEU1 interacted with this domain of ALK5 by Co-IP (Fig. 6l). PLA (Fig. 6m) and Co-IP (Fig. 6n) assays confirmed that 160–200 domain plasmid competed with ALK5 and inhibited NEU1-ALK5 binding.
## NEU1 interacted with and stabilized ALK5 to enhance ALK5-SMAD2/3 signaling pathway
To investigate the effects of NEU1-ALK5 interaction on ALK5, we measured the stability of ALK5 in the presence or absence of NEU1. NEU1 knockdown promoted ALK5 degradation, whereas NEU1 overexpression inhibited ALK5 degradation (Fig. 7a, b), suggesting that NEU1 interacted with and stabilized ALK5. ALK5 is able to phosphorylate its substrates, the SMAD family37. NEU1 silence markedly suppressed TGFβ-induced SMAD$\frac{2}{3}$ activation in a time-dependent manner, while NEU1 overexpression sustained SMAD$\frac{2}{3}$ continuous activation in the presence of TGFβ. ( Supplementary Fig. 8a–d). In HK-2 cells and the mouse kidney, the phosphorylation of ALK5 and the downstream SMAD$\frac{2}{3}$ were also significantly inhibited upon NEU1 silencing (Fig. 7c, d and Supplementary Fig. 8e, f). On the contrary, NEU1 overexpression augmented TGFβ- or UUO-induced activation of ALK5 and SMAD$\frac{2}{3}$ (Fig. 7e, f and Supplementary Fig. 8g, h). Inhibiting the binding of NEU1 and ALK5 by transfecting the cells with ALK5160-200 plasmid significantly suppressed SMAD$\frac{2}{3}$ activation (Fig. 7g, h).Fig. 7NEU1 stabilizes ALK5 and enhances ALK5-SMAD$\frac{2}{3}$ signaling pathway.a, b HK-2 cells transfected with siNEU1 (a) or NEU1 full-length plasmid (b) for 24 h and treated with TGFβ (10 ng/ml) for 24 h. Then the cells were incubated with cycloheximide (CHX, 20 μg/ml) for the indicated periods of time (0, 2, 4, 8, 12, 24 h) (left). Lysates were harvested from the cells and analyzed by western blots. Quantitation of ALK5 protein levels were shown in the right pane. $$n = 3$$ samples per group. Unpaired t-test. c–f Western blots (c, e) and quantitative results (d, f) of p-ALK5, ALK5, p-SMAD$\frac{2}{3}$, and SMAD$\frac{2}{3}$ in HK-2 cells transfected with siNEU1 or NEU1 plasmid for 24 h and treated with TGFβ (10 ng/ml) for 24 h. $$n = 3$$ samples per group. Relative protein levels were shown after normalization to GAPDH. One-way ANOVA followed by Tukey’s multiple comparisons test. g, h Western blots (g) and quantitative results (h) of p-ALK5, p-SMAD$\frac{2}{3}$, and SMAD$\frac{2}{3}$ in HK-2 cells transfected with NEU1 and ALK5160-200 plasmid for 24 h and treated with TGFβ (10 ng/ml) for 24 h. $$n = 3$$ samples per group. Relative protein levels were shown after normalization to GAPDH. One-way ANOVA followed by Tukey’s multiple comparisons test. i Co-immunoprecipitation of NEU1 and ALK5 in HK-2 cells stimulated with TGFβ (10 ng/ml) for 24 h. λpp, Lambda Protein Phosphatase. Two independent experiments were performed. j Western blots of ALK5 in HK-2 cells. siALK5-1, siALK5-2, and siALK5-3 were 3 different siRNA sequences. HK-2 cells were transduced with siALK5 for 48 h. Two independent experiments were performed. k–m mRNA levels of KIM1 (k) and protein level of p-ALK5, p-SMAD$\frac{2}{3}$, and SMAD$\frac{2}{3}$ (l, m) in HK-2 cells transduced with NEU1 full-length plasmid and ALK5 siRNA for 48 h. KIM1 mRNA normalized to GAPDH. Relative protein levels were shown after normalization to GAPDH. k $$n = 3$$ samples per group. l, m $$n = 3$$ samples per group. Two-way ANOVA followed by Tukey’s multiple comparisons test. n The proposed mechanisms of NEU1-mediated renal fibrosis. All statistic data were presented as mean ± SD. All tests were two-tailed.
We added phosphatase to cell lysis buffer to remove phosphate groups of proteins. The Co-IP showed that phosphatase significantly inhibited the interaction between NEU1 and ALK5 (Fig. 7i), suggesting their interaction depends on the phosphorylation state of ALK5. When ALK5 was knocked down (Fig. 7j), NEU1 overexpression-induced KIM1 (Fig. 7k) and ALK5-SMAD$\frac{2}{3}$ signaling pathway activation (Fig. 7l, m) were abolished in HK-2 cells, indicating that NEU1 promoted renal fibrosis in an ALK5-dependent manner. Collectively, these results suggested that NEU1 interacted with the GS domain (amino acids 160–200) of ALK5 in cytoplasm, and then enhanced the ALK5-SMAD$\frac{2}{3}$ signaling pathway, contributing to renal fibrosis (Fig. 7n).
To explore whether NEU1 acts in the cells as an enzyme that removes terminal sialic acid residues of ALK5, we employed biotin-labeled sambucus nigra lectin (SNL) to specifically bind to sialic acid via α-2-6 linkage. Results showed that the SNL level of ALK5 was decreased in response to TGFβ stimulation, and this decrease was aggravated in NEU1-overexpression cells (Supplementary Fig. 9a). Mutation of enzyme active sites (mtNEU1: D103A, Y370A, E394A) reduced the effects of NEU1 overexpression on TGFβ-induced ALK5-SMAD$\frac{2}{3}$ activation (Supplementary Fig. 9b–e).
## Targeting NEU1 by salvianolic acid B protected kidney
To identify candidate compounds targeting mammalian NEU1, SPR was employed to screen the binding affinities of 74 natural products from medicinal plants with recombinant human NEU1 protein (Fig. 8a, Supplementary Data 2 and Supplementary Table 4). The top two compounds with the strongest NEU-binding affinities are salvianolic acid B from *Salvia miltiorrhiza* with KD at 21.57 nM (Fig. 8b) and rosmarinic acid from the *Rosmarinus officinalis* genus with KD at 141 nM (Supplementary Data 2 and Supplementary Table 4). Interestingly, some high-molecule polysaccharides showed the potential to bind to NEU1 such as Glehniae radix polysaccharide IV with KD at 240 nM and *Panax quinquefolium* polysaccharides V with KD at 6.39 μM (Supplementary Data 2 and Supplementary Table 4). Co-IP (Supplementary Fig. 10a) and PLA experiments (Supplementary Fig. 10b) showed that salvianolic acid B significantly inhibited the interaction between NEU1 and ALK5. TGFβ-induced ALK5-SMAD$\frac{2}{3}$ signaling pathway activation was also blocked by salvianolic acid B (Supplementary Fig. 10c, d).Fig. 8Targeting NEU1 by salvianolic acid B alleviates UUO-induced renal fibrosis.a The interactions between 74 compounds with recombinant human NEU1 determined by surface plasmon resonance (SPR). KD, dissociation constant. The compound 3, 9, 11, 12, 18, 19, 20, 21, 41, 53, 58, 60, 61, 62, 67, 72, 73 have no KD value because they do not bind to NEU1. b, c The interaction between NEU1 and salvianolic acid B (SaB) (b) and salvianolic acid A (SaA) (c) was tested by SPR. The frequency response and fitting curves were displayed. Pearson’s test. d Scheme of the experimental approach. UUO, unilateral ureteral obstruction. e Hematoxylin and eosin (HE) and Masson’s trichrome staining from control (Ctrl) and SaA or SaB-treated mice 10 days after UUO. Scale bar, 50 mm. $$n = 3$$ mice per group. f Statistical results for interstitial collagen analyzed by Image Pro-Plus software. $$n = 3$$ mice per group. g–o the mRNA levels of kidney injury molecule 1 (Kim1, g), EMT associate genes (Snai1 and Snai2, h, i), inflammatory cytokines associate genes (Tnfα, Il1, and Il6, j–l) and extracellular matrix associate genes (Vim, Col1a1, and Col3a1, m–o) in kidney samples. All normalized to Gapdh. $$n = 6$$ samples per group. p Western blots (p, left panel) and quantitative results (p, right panel) of p-ALK5 (ser165), p-SMAD$\frac{2}{3}$, and SMAD$\frac{2}{3}$ in kidney from control (Ctrl) and SaA or SaB-treated mice 10 days after UUO. $$n = 3$$ mice per group. All statistic data were presented as mean ± SD, one-way ANOVA followed by Dunnett’s multiple comparisons test. All tests were two-sided.
Subsequently, we investigated the protective effects of salvianolic acid B from renal injury in mouse models (Fig. 8d), and compared its effects with salvianolic acid A, a compound also from *Salvia miltiorrhiza* but showing relatively weak NEU1-binding affinity with KD at 52.30 μM (Fig. 8c). HE and Masson staining data demonstrated that salvianolic acid B (40 mg/kg, tail vein injection) significantly attenuated UUO-induced renal injury and renal fibrosis (Fig. 8e, f). Salvianolic acid B treatment markedly inhibited Kim1 (Fig. 8g), Snai1 (Fig. 8h), and Snai2 expression (Fig. 8i). Salvianolic acid B also suppressed proinflammatory cytokine production (Tnf-α, Il-6, and Il1β) and collage deposition (Fig. 8j–o). In line with these, the phosphorylation of ALK5 and the down-stream phosphorylation of SMAD$\frac{2}{3}$ were inhibited by salvianolic acid B (Fig. 8p). In comparison with salvianolic acid B, salvianolic acid A at the same concentration (40 mg/kg) showed comparable effects on some markers such as Kim1 (Fig. 8g), Col1a1 (Fig. 8n), and Col3a1 (Fig. 8o) expression, but relatively weaker effects on collage deposition (Fig. 8f), Snai2 (Fig. 8i), and Tnfα (Fig. 8j) in UUO-induced mouse model. However, salvianolic acid A failed to suppress UUO-induced Snai1 (Fig. 8h), Il6 (Fig. 8k), Il1β (Fig. 8l), Vim (Fig. 8m), and phosphorylations of ALK5 and SMAD$\frac{2}{3}$ (Fig. 8p). The protective effects of salvianolic acid B from renal injury and its stronger effects than salvianolic acid A were replicated in ischemia/reperfusion-induced mouse model (Supplementary Fig. 11). The obviously better kidney-protective effects of salvianolic acid B than salvianolic acid A are in close agreement with the stronger NEU1-binding affinity of the former than the later.
To test whether NEU1 mediate the protective effects of salvianolic acid B, we employed a Neu1 CKO mouse model (Fig. 9a). In Neu1 CKO mice, treatment of salvianolic acid B (40 mg/kg) failed to further reduce renal injury and renal fibrosis in response to UUO stimulation, as evidenced by HE and Masson staining (Fig. 9b, c). In agreement, salvianolic acid B cannot further inhibit UUO-induced Kim1 expression (Fig. 9d), EMT (Fig. 9b, e–g), inflammation factor elevation (Fig. 9h), and collage production (Fig. 9h) in Neu1 CKO mice. In addition, the inhibition effects on phosphorylations of ALK5 were not further enhanced by salvianolic acid B treatment in Neu1CKO mice (Fig. 9i, j). These data indicated that NEU1 is required for salvianolic acid B in renal protection. Fig. 9NEU1 mediates the renal protective effects of salvianolic acid B.a Scheme of the experimental approach. The Neu1 CKO mice were treated with salvianolic acid B (SaB) at the indicated doses for 10 continuous days after being subjected to UUO surgery. b Representative gross appearance of kidneys (Scale bar, 2 mm), kidney cross-sections stained with HE (Scale bar, 50 μm), Masson’s trichrome (Scale bar, 50 μm), immunohistochemical staining with E-Cadherin and Snail (Scale bar, 50 μm). $$n = 3$$ mice per group. The red arrow indicates positive area. c Statistical results for interstitial collagen in b analyzed by Image Pro-Plus software. $$n = 3$$ mice per group. d Kim1 mRNA level. $$n = 3$$ mice per group. e, f Quantification of staining of E-Cadherin (e) and Snail1 (f). $$n = 3$$ mice per group. g, h mRNA levels of the indicated genes in kidneys determined by qRT-PCR. $$n = 3$$ mice per group. Dotted line represents the expression in sham control tissue. Gene expression levels were normalized to Gapdh. i Representative image of immunohistochemical staining with p-ALK5 (ser165) (Scale bar, 50 μm). $$n = 3$$ mice per group. j Quantification of staining of p-ALK5 (ser165). $$n = 3$$ mice per group. All data were presented as mean ± SD, one-way ANOVA followed by Tukey’s multiple comparisons test. All tests were two-tailed.
## Discussion
This study identified a key role for TEC-located NEU1 in renal injury and renal fibrosis based on the results of genetic, in vivo, in vitro, and pharmacological experiments. The major findings of this study include the following: (i) we observed that NEU1 was significantly elevated in TEC of fibrotic kidneys from human and mice; (ii) we characterized NEU1 as a promotor of renal fibrosis using genetically-engineered mice and epithelial cellular models; (iii) mechanistically, NEU1 interacted with ALK5 at the amino acid 160–200 region and enhanced the ALK5-SMAD$\frac{2}{3}$ signaling pathway; and (iv) salvianolic acid B screened from natural compounds showed high affinity to human NEU1 and effectively prevented renal injury.
The role of neuraminidases in organ fibrosis remains poorly understood, especially in renal fibrosis. In this work, we demonstrated that NEU1 was noticeably elevated in patients with renal fibrosis, in mice subjected to UUO or administered folic acid, and in TGFβ-stimulated human tubular epithelial HK-2 cells. The increased NEU1 was mainly localized in the TECs. Our results were consistent with a previous study that reported an upregulation of NEU1 in lung epithelial cells of patients with pulmonary fibrosis38. However, the expression of NEU1 in the fibroblasts of patients with pulmonary fibrosis was decreased in another publication39. Large multicenter samples are therefore warranted to resolve this inconsistency.
In an effort to investigate the function of NEU1 in CKD, we generated TEC-specific NEU1-knockout mice using a Cre-loxP conditional gene targeting as well as NEU1-overexpressed mice via adeno-associated virus gene transfer. *Conditional* genetic engineering employed in this work is more powerful than conventional knockout/knock-in technology for studying NEU1 function. Tubule-specific NEU1 loss was sufficient to protect kidney from UUO- or folic acid-induced injury in mice and TGFβ-induced injury in HK-2 cells. These altered phenotypes and molecular markers included EMT, inflammatory cytokines, fibrosis-associated genes, and renal morphology. Conversely, NEU1 overexpression aggravated fibrosis-associated phenotypes in vitro and in vivo. Although the influence of neuraminidases in other renal cell types such as myofibroblasts cannot be totally excluded in renal fibrosis, this work sheds light on a clear promotor role for tubular NEU1 in renal injury.
This work centered on the role of NEU1 in renal fibrosis. Aside from NEU1, there are 3 other isoforms (NEU2, NEU3, and NEU4) in the human neuraminidase family. These mainly differ in their protein sequences, subcellular localization, and enzymatic properties. NEU1, the most abundant form, displays less than $10\%$ similarity with the other 3 isoforms40. NEU2, NEU3, and NEU4 share largely common amino acid sequences. Different neuraminidase members may produce variable effects depending on the organ and cell type. We and others have demonstrated the contribution of NEU1 in cardiovascular diseases20,21,24,25,41,42. NEU2 and NEU4 were shown to inhibit cancers43,44. NEU3 was reported to act as a pro-inflammatory mediator in the intestine and lung, but trigger inflammation in brain45–47. Besides NEU1 that has been decoded in this work, the roles of NEU2, NEU3, and NEU4 in CKD progression need to be explored in-depth in the near future.
NEU1 deficiency is associated with sialic acid-rich macromolecular storage, leading to lysosomal disorder, sialidosis17,48. There were no significant morphological differences between Neu1CKO mice and the wild-type mice. Besides, the inflammation, fibrosis, and EMT indicators in the kidney of Neu1CKO mice are almost similar with those in the control mice. In addition, we did not observe any side effects in tubule-specific NEU1-knockout mice compared with the wide-type mice for up to 10 weeks (Figs. 3 and 4). This was in consistent with what was observed in cardiomyocyte-specific NEU1-knockout mice by us and others20,39. In line with this, there is no case report on sialidosis syndrome caused by use of neuraminidase inhibitors such as oseltamivir for up to 6 weeks49. *In* general, pathological changes were observed in mice with systemic knockout of NEU1 but less in tissue-conditional KO mice39,50.
NEU1 was originally described to be localized in lysosomes, where it is involved in breakdown of sialo-glycoconjugates by removing terminal sialic acids15. Emerging evidence has demonstrated that NEU1 can be sorted to the cell surface to desialylate membrane receptors such as TLR4 and insulin receptors19,51–53. We have previously shown that NEU1 translocated to the nucleus to bind with transcriptional factors GATA4, promoting cardiac hypertrophy and remodeling20. In this work, we have demonstrated that NEU1 was activated in the cytoplasm to bind to ALK5, promoting renal fibrosis. ALK5 is composed of small cysteine-rich extracellular parts, single transmembrane regions, and intracellular parts. The intracellular parts can be further divided into GS domain, serine/threonine kinase domain, and C-terminal tail37. *In* general, various ligands activated the TGFβ type I receptor by binding to the extracellular parts of ALK554–56. In contrast, we found that NEU1 was directly bound to the GS domain of the intracellular parts rather than the extracellular parts. Inhibiting the binding of NEU1 and ALK5 significantly suppressed SMAD$\frac{2}{3}$ activation. Actually, the GS domain located at the 160–200 region is responsible for the phosphorylation of the TGFβ I receptor kinase and activation of downstream signaling pathway36. It is thus presumed that the binding of NEU1 with the GS domain promoted the phosphorylation of the serine/threonine kinase domain.
Interestingly, NEU1 affected ALK5 sialylation in the presence of TGFβ, and NEU1 enzymatic activity is required for enhancing the TGFβ pathway. It appears that the level of NEU1 determines the sialyaltion of ALK5 and the NEU1 enzymatic activity affects ALK5 pathway. However, the exact molecular basis remains unknown and depends on the characterization of the crystal structure of NEU1-ALK5 complex.
Development of candidates inhibiting or interacting with NEU1 is still a challenge because of its less investigation and unavailability of crystal structure. We employed a SPR strategy to identify NEU1-interacting compounds from medicinal plants. Salvianolic acid B, the most abundant ingredient in Salvia miltiorrhiza, exhibited the strongest affinity with NEU1 than other compounds. Salvianolic acid B inhibited NEU1 protein expression and enzyme activity (Supplementary Fig. 12), and suppressed the interaction between NEU1 and ALK5. In agreement with the affinity assay, salvianolic acid B showed promising kidney-protective effects and clearly stronger effects than salvianolic acid A in UUO-induced and ischemia/reperfusion-induced mouse models. Several reports have also shown that salvianolic acid B improved kidney dysfunction by inhibiting endoplasmic reticulum stress and PI3K/Akt/Nrf2 pathway, or by activating sirt1-mediated autophagy and Nrf2/NLRP3-mediated pyroptosis57–60. Small-molecule drugs have the potential to interact with multiple targets and exhibit diverse pharmacological activities61–63. It is likely that salvianolic acid B inhibited renal fibrosis through intricate, distinct, and interrelated signaling pathways. Here, we provide evidence that NEU1 is one of direct interacting target for salvianolic acid B, and NEU1 is required for this compound’s effects in renal protection.
This study has some limitations. First, we have shown that NEU1 interacted with the GS domain of ALK5, but the specific binding sites for NEU1 were not characterized because of the paucity of information on this protein. Second, in addition to activating the ALK5-SMAD$\frac{2}{3}$ signaling pathway, NEU1 may also be involved in other signaling pathways that remain to be explored. Thirdly, whether other signaling pathways or targets are involved in salvianolic acid B’s effects against renal fibrosis as well as their complex interrelated mechanisms remain to be explored.
In conclusion, this study identifies a promotor role for NEU1 in renal fibrosis and suggests a potential therapeutic approach by targeting NEU1 to treat CKD.
## Human kidney biopsy studies
This study complied with the ethical guidelines of the 1975 Declaration of Helsinki. Human kidney biopsy samples were obtained from Jiangsu Province Hospital. The protocol concerning the use of human kidney biopsy samples in this study was approved by the Committee on Research Ethics of Jiangsu Province Hospital. The ethic number is 2016-SR-029. The written, informed consent to participate was obtained from all study participants. All the human study participants agreed to participation for free. Eight participants who underwent biopsy procedures because of microscopic hematuria but had no specific pathologic alterations were designated as the non-renal fibrosis group. Eight CKD patients were diagnosed renal fibrosis by biopsy procedures. Detailed information on participants is provided in Supplementary Table 1.
## Mice
Mice were maintained in the center for Experimental Animals at China Pharmaceutical University, Nanjing, China. All procedures involving experimental animals were performed following protocols approved by the Committee for Animal Research of China Pharmaceutical University and conformed to the Guide for the Care and Use of Laboratory Animals. The ethic number is 2021-11-003. Mice of the same genotype were randomly assigned to different treatment groups. C57BL/6J male mice aged 8–10 weeks (body weights of 20–23 g) were used. All animals were maintained under constant humidity and temperature at standard facilities under specific pathogen-free conditions with free access to water and chow ($10\%$ kcal from fat; Xietong Organism, China). Mice were euthanized by cervical dislocation. The investigators were not blinded to treatments, but no subjective assessments were made.
## Renal tubular epithelial cell-specific Neu1 knockout mice
To specifically knockout Neu1 in renal tubular epithelial cells, we generated a mouse line by crossing females of the Neu1-floxed line (Neu1fl/fl) with the Ksp-Cre (B6.Cg-Tg (Ksp1.3-cre) 91Igr/J, male, aged 8–9 weeks) transgenic strain obtained from Shanghai Model Organisms Center, Inc. The filial 1 progeny, mice (male or female), litters with heterozygous deletion of *Neu1* gene (Neu1fl/+) that harbored the Ksp1.3/Cre transgene (Neu1fl/+; Ksp1.3-Cre) were obtained and they were further crossed with the opposite sex of Neu1fl/fl mice to obtain mice expressing complete deletion of Neu1 in the filial 2 progeny (Neu1fl/fl; Ksp1.3-Cre), referred to as Neu1 CKO mice.
## Adeno-associated virus (AAV) infected mice
To overexpression NEU1 in the kidney, AAV9-mediated delivery was employed. C57BL/6J mice were used (male, 8–10 weeks, B&K Universal Group Ltd. Shanghai, China). AAV9 encoding mouse NEU1 was provided by Hanheng Biotechnology (Shanghai, China). Briefly, male mice were anesthetized with pentobarbital sodium (30 mg/kg, Sigma-Aldrich, catalog no. P3761) by intraperitoneal injection, and were injected with 1.5 × 1012 vector genome (vg)/ml HBAAV9-CMV-Neu1-3xflag-ZsGreen and 1.5 × 1012 vg/ml HBAAV9- ZsGreen control into 5 different sites (10 μl at each site) of the renal cortex with a glass micropipette.
## Unilateral ureteral obstruction (UUO)-induced fibrosis model
Male mice were anesthetized with pentobarbital sodium (30 mg/kg) by intraperitoneal injection. The abdomen was opened, and the left ureter was ligated with 5-0 silk. The abdomen was then closed with running sutures and the skin was closed with interrupted sutures. After surgery, the mice were maintained in a temperature-controlled room with a 12-h light-dark cycle and were reared on standard chow and water ad libitum.
## Folic acid (FA)-induced fibrosis model
Male mice (8 weeks old) were intraperitoneally injected with a single dose of folic acid (250 mg/kg, Sigma-Aldrich, 7876) dissolved in 0.3 M sodium bicarbonate. Mice injected with sodium bicarbonate served as vehicle control (Saline).
## IR-induced renal injure (IRI) model
Male mice were anesthetized with pentobarbital sodium (30 mg/kg) by intraperitoneal injection. The abdomen was opened, and bilateral renal pedicles were clipped for 30 min using microaneurysm clamps. During the ischemic period, body temperature was maintained between 37 °C using a heat pad. After removal of the clamps, reperfusion of the kidneys was visually confirmed. Mice were euthanized 24 h after IRI, and kidney tissues were collected for analyses. Sham control animals were subjected to the identical operation without renal pedicle clamping.
## Salvianolic acid A and salvianolic acid B treatment in vivo
Based on the dosage conversion among different species and previous reports, the mice were injected salvianolic acid A and salvianolic acid B at 40 mg/kg/d by a tail vein. C57BL/6J mice (male, 8–10 weeks, B&K Universal Group Ltd. Shanghai, China) underwent UUO or IRI surgery. In UUO-induced model, daily injected of the salvianolic acid A and salvianolic acid B or saline was started on the day of UUO surgery and lasted for 10 days. In IR-induced mouse model, mice were injected salvianolic acid A and salvianolic acid B or a comparable volume of saline continuous 3 days before IRI surgery.
## Histological analysis
Mouse kidneys were fixed in $4\%$ paraformaldehyde (PFA, Sigma-Aldrich, catalog no. P6148) in PBS and embedded in paraffin. Paraffin was cut into sections using a paraffin microtome with stainless steel knives. The sections were mounted on glass slides, deparaffinized with xylene, dehydrated through graded series of ethanol, and stained with hematoxylin-eosin. The tubules were evaluated according to the following scoring system: 0 = no tubular injury; 1 = $10\%$ or fewer tubules injured; 2 = 11–$25\%$ tubules injured; 3 = 26–$50\%$ tubules injured; 4 = 51–$74\%$ tubules injured; and 5 = $75\%$ or more tubules injured. The tubulointerstitial inflammation index were evaluated according to degree of inflammatory cell infiltration: 0 = no inflammatory cell infiltration; 1 = $10\%$ or fewer inflammatory cell infiltration; 2 = 11–$25\%$ inflammatory cell infiltration; 3 = 26–$50\%$ inflammatory cell infiltration; and 4 = $75\%$ or more inflammatory cell infiltration. To evaluate collagen deposition, sections were stained with Masson’s Trichrome. The fibrotic areas were calculated by Image Pro-Plus software (Media Cybernetics, Rockville, MD, USA) as the summation of blue-stained areas divided by total ventricular area. Image acquisition was performed on the Hamamatsu Nano Zoomer 2.0 RS scanner.
## ELISA assay
Creatinine and urea nitrogen concentration in serum were measured using the Creatinine (Cr) Assay kit and Urea Assay (BUN) kit following the manufacturer’s instructions. These two kits were purchased from Nanjing Jiancheng Bioengineering Institute. IL6 and TNFα concentrations in serum were measured using the Mouse IL6 ELISA Kit and Mouse TNFα ELISA Kit following the manufacturer’s instructions. These two kits were purchased from Proteintech Group, Inc.
## Neuraminidase enzyme activity assay
Neuraminidase enzyme activity was measured by a fluorometric assay with substrate 2′-(4-methylumbelliferyl)-α-d-N-acetylneuraminic acid (4-MU-NANA) (Abcam, ab138888). Samples were lysed with $0.1\%$ Triton X-100. After centrifugation at 12,000 × g at 4 °C for 15 min, the supernatants were subjected to a neuraminidase assay at 37 °C for 1 h. Reaction products were measured at 320 nm for excitation and 450 nm for emission with fluorescence microplate reader (SpectraMaxiD5, Molecular, USA).
## Immunohistochemistry analysis
Mouse kidneys were fixed in $4\%$ paraformaldehyde for at least 24 hours and embedded in paraffin. Paraffin sections were deparaffinized and hydrated in graded ethanol series before staining with the peroxidase-antiperoxidase method. Antigens were retrieved by boiling the sections for 20 min in 10 mM citric acid solution (pH = 6) or 10 mM Tris and 1 mM EDTA (pH = 9). Endogenous peroxidase was blocked by incubation in $3\%$ hydrogen peroxide. The sections were incubated overnight at 4 °C with primary antibodies. The sections were incubated with the corresponding secondary antibodies. 3,3’-diaminobenzidine (DAB) (Sigma-Aldrich, St Louis, MO) was used as chromogen. Sections were lightly counterstained with hematoxylin and were dehydrated and coverslipped. Image acquisition was performed on the Hamamatsu NanoZoomer 2.0 RS scanner.
## Immunofluorescence staining
For the kidney tissues immunofluorescence staining, sections were permeabilized with $0.1\%$ Triton X-100 and blocked with $5\%$ BSA in PBS for 20 min at room temperature. Then, sections were incubated at 4 °C overnight with the primary antibodies. Fluorescently labeled secondary antibodies were used. Slides were counterstained with DAPI. Samples were analyzed, and pictures were taken using FV3000 confocal scanning microscope.
## Transmission electron microscopy (TEM)
Renal tubules and glomeruli structure were examined by standard transmission electron microscopy. Fresh kidney was fixed with a mixture of $2.5\%$ glutaraldehyde 2 h, washed, dehydrated, and embedded in resin according to standard procedures. Embedded samples were analyzed by a JEOL 1010 electron microscope (Tokyo, Japan).
## PCR array
Gene expression profiling was analyzed by a mouse fibrosis PCR Array (Wcgene Biotech, Shanghai, China) according to the manufacturer’s protocol. The β-actin and Gapdh were used as endogenous controls. The relative gene expression levels of target genes were calculated using the 2−ΔΔCt method. Data were normalized to the reference gene based on the cycle threshold (Ct) values. The log2 (fold-change) was calculated based on the 2−ΔΔCt method.
## Cell culture, transfection, and treatment
Human proximal tubular epithelial cells (HK-2) and human embryonic kidney cells (HEK293T) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in DMEM/F12 or DMEM medium (KeyGEN BioTECH) containing 4.5 g/L of D-glucose supplemented with $10\%$ fetal bovine serum (FBS, Gibco).
HK-2 cells were transfected with siNEU1/siALK5 at the indicated sequences or transfected with NEU1 expression plasmid (HA-NEU1). HEK293T cells were transfected with HA-NEU1, Flag-ALK5160-200aa, Flag-ALK1, Flag-ALK2, Flag-ALK3, Flag-ALK4, Flag-ALK5, Flag-ALK6, Flag-ALK7, Flag-AMHR2, Flag-ACVR2B, or Flag-TGFBR2 plasmids. siRNA transfected with Lipofectamine 2000 and plasmids transfected with Lipofectamine 3000 according to the manufacturer’s protocol (Thermo Fisher Scientific).
For NEU1 maintain SMAD$\frac{2}{3}$ activation test, the HK-2 cells were transfected with siNEU1 or HA-NEU1 plasmid and then stimulated with or without TGFβ (10 ng/ml, PeproTech) for 0, 6, 12, 24, 36, or 48 h. For ALK5 stability test, HK-2 cells were treated with cycloheximide (CHX; MCE, HY-12320) with or without siNEU1/HA-NEU1 at different time points (0, 2, 4, 8, 12, 24 h), then the cells were stimulated with TGFβ (10 ng/ml) for 24 h. HK-2 cells were treated with salvianolic acid A (10 μM) and salvianolic acid B (5 μM or 10 μM) for 24 h and stimulated with or without TGFβ (10 ng/ml, PeproTech) for 24 h.
For protein dephosphorylation test, HK-2 cells were stimulated with TGFβ (10 ng/ml) for 24 h. Then the cells were lysed with 25 mM Tris-HCl (pH 7.4) lysis buffer containing 150 mM NaCl, $1\%$ NP-40, 1 mM EDTA, $5\%$ glycerol. The cell lysis solution was incubated with lambda protein phosphatase (λpp, 100 U/ml, Beyotime) and MnCl2 (1 mM) for 30 min at 30 °C.
## Plasmids and siRNA
HA-NEU1, Flag-ALK5160-200aa, Flag-ALK1, Flag-ALK2, Flag-ALK3, Flag-ALK4, Flag-ALK5, Flag-ALK6, Flag-ALK7, Flag-AMHR2, Flag-ACVR2B, Flag-TGFBR2, pBiFC-VC155, pBiFC-VN173, pBiFC-ALK5-VN173, pBiFC-NEU1-VC155, and NEU1 (D103A/Y370A/E394A)-HA were generated by cloning the indicated gene into pcDNA3.1. Those plasmids were purchased from Public Protein/Plasmid Library (Nanjing, China). Human NEU1 siRNA (siNEU1-$\frac{1}{2}$/3), human ALK5 siRNA (siALK5-$\frac{1}{2}$/3), and their vector controls were purchased from GenePharma (Beijing, China), siRNA sequences were provided in Supplementary Table 2.
## Real-time fluorescence quantitative PCR (RT-qPCR)
Total RNA was isolated from tissues or HK-2 cells by TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. RNA was reverse transcribed using the Hifair® II 1st Strand cDNA Synthesis SuperMix for qPCR (Yeasen Biotech) according to the manufacturer’s protocol. The cDNA was stored at −20 °C until use. The real-time polymerase chain reaction was performed using Hieff® qPCR SYBR Green Master Mix (Yeasen Biotech) according to the manufacturer’s instructions. The data were normalized to Gapdh and analyzed using the ΔΔCt method. The primers used are listed in Supplementary Table 3.
## Co-immunoprecipitation
HK-2 or HEK293T cells were lysed with 25 mM Tris-HCl (pH 7.4) lysis buffer containing 150 mM NaCl, $1\%$ NP-40, 1 mM EDTA, $5\%$ glycerol, and protease inhibitor (Roche). The cell lysates were mixed with NEU1, ALK5, Flag-tag, or HA-tag antibody that was preincubated with Protein A/G Magnetic Beads (Thermo Scientific) for 12 h and washed three times with cold lysis buffer excluding $0.1\%$ SDS. Coprecipitates with NEU1, ALK5, SNL, Flag, or HA proteins were separated by SDS-PAGE and subjected to western blots with ALK5, NEU1, SNL, HA, or Flag antibodies. The non-heavy chain IgG secondary antibody (Santa Cruz, m-IgGκ BP-HRP, sc-516102) was used.
## Proximity ligation assay (PLA)
Interactions between NEU1 and ALK5 was detected by in situ proximity ligation assay (PLA) in HK-2 cells and human kidney biopsy sample. For HK-2 cells, cells were grown on glass bottom dish and fixed with $4\%$ paraformaldehyde in PBS for 15 min. For paraffin-embedded human kidney biopsy sample, following dewaxing and rehydration of tissue sections, antigen retrieval was performed by heating the slides for 30 min at 95 °C in Tris-EDTA buffer. From this point, the tissue sections and the fixed HK-2 cells were treated identically, and the PLA protocol was followed according to the manufacturers’ instructions. The In-situ PLA (Duolink® in situ Detection Reagents Red Kit, Sigma-Aldrich, DUO92008; Duolink® in situ PLA Probe Anti-Rabbit PLUS, Sigma-Aldrich, DUO92002; Duolink® in situ PLA Probe Anti-Mouse MINUS, Sigma-Aldrich, DUO92004) was performed according to manufacturer’s instruction. Fluorescence signal amplification was observed using the Zeiss LSM800 confocal laser scanning microscopy.
## Bimolecular fluorescence complementation (BiFC)
HK-2 cells were plated on glass bottom dish. Transfections were carried out using the lip3000 reagent, with pBiFC-NEU1-VC155 and pBiFC-ALK5-VN173 plasmid. Cells were stimulated with or without TGFβ (10 ng/ml, PeproTech) for 24 h after transfection 24 h, and were stained with Hoechst 33342 (C1028, Beyotime) to label nuclear DNA or with Lyso-Tracker (C1046, Beyotime), Mito-Tracker (40740ES50, Yeasen), and CM-Tracker (C1036, Beyotime) to label lysosome, mitochondrion, and cell plasma membrane, respectively. For the control group, cells were transfected with pBiFC-HA-NEU1-VC155 and pBiFC-VN173 plasmids or pBiFC-ALK5-VN173 and pBiFC-VC155 plasmids. Fluorescence signal amplification was observed using the Zeiss LSM800 confocal laser scanning microscopy.
## LC-MS/MS
LC-MS/MS analyses were performed on the Q Exactive HF mass spectrometer coupled with UltiMate 3000 RSLCnano system. Coprecipitates with NEU1 peptides were loaded through auto-sampler and seperated in a C18 analytical column (75 μm × 25 cm, C18, 1.9 μm, 100 Å). Mobile phase A ($0.1\%$ formic acid) and mobile phase B ($80\%$ ACN, $0.1\%$ formic acid) were used to establish the seperation gradient. The flow rate for separation was 300 nL/min. For DDA mode analysis, each scan cycle is consisted of one full-scan mass spectrum ($R = 60$ K, AGC = 3e6, max IT = 20 ms, scan range = 350–1800 m/z) followed by 20 MS/MS events ($R = 15$ K, AGC = 2e5, max IT = 50 ms). HCD collision energy was set to 28. Isolation window for precusor selection was set to 1.6 Da. Former target ion exclusion was set for 30 s. MaxQuant (V1.6.6) software was used for data analysis.
## Western blots
Western blots were performed to detect protein expression in kidneys or cells. For denaturing gels, whole-cell lysates were heated (99 °C) for 10 min with SDS-PAGE loading buffer (Beyotime). Proteins were then separated by polyacrylamide gel electrophoresis in acrylamide gels ($10\%$) and transferred using a Bio-Rad western system to polyvinylidene difluoride (EMD Millipore) membranes, which were immediately placed in $5\%$ non-fat milk in Tris-buffered saline (TBS, 50 mM Tris, pH 7.6, 150 mM NaCl)-Tween ($0.1\%$ Tween20) buffer or in 1×Carbo-Free Blocking Solution (for SNL detection) for blocking (2 h at 25 °C). Membranes were then washed in TBS-Tween buffer for 5 min, followed by incubation with specific primary antibodies at 4 °C overnight. Membranes were then washed 4 times for 40 min in TBS-Tween buffer, and incubated with a horseradish peroxidase-conjugated anti-mouse antibody, or anti-rabbit antibody at room temperature for 1 h. The resulting immunoblots were visualized using ECL Western Blotting Substrate (KeyGEN), according to the manufacturers’ instructions.
For non-denaturing gels, cells were extracted in native extraction buffer (25 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, $1\%$ NP-40, and $5\%$ glycerol), and protease inhibitor cocktail (Roche)), prior to centrifugation at 12,000 × g for 10 min at 4 °C. 25 μg of protein was loaded onto $10\%$ Tris-glycine gels (Beyotime) following the manufacturer’s protocol for native gel electrophoresis in Tris–glycine running buffer (25 mM Tris pH ~8.3 and 192 mM glycine), at 120 V for ~2 h. Samples were subjected to electrophoresis at 4 °C to prevent protein denaturation. Proteins were transferred to PVDF membranes then blocked and probed with antibodies (see below).
## Antibodies
The primary antibodies used were as follows: anti-NEU1 (Proteintech, 67032-1-Ig, 1:1000 for IB, 1:100 for PLA), anti-NEU1(Santa Cruz, sc-166824, 1:100 for IP, 1:100 for IF, and 1:100 for IHC), anti-E-cadherin (CST, 3195s, 1:1000 for IB, 1:400 for IF, and 1:400 for ICH), anti-Vimentin (CST, 5741s, 1:1000 for IB, 1:400 for IF, 1:400 for ICH), anti-Snail (CST, 3879s, 1:1000 for IB and 1:400 for ICH), anti-Slug (CST, 9585s, 1:1000 for IB and 1:400 for ICH), anti-GAPDH (CST, 5174s, 1:1000 for IB), anti-α-smooth muscle actin (CST, 19245s, 1:1000 for IB and 1:400 for ICH), anti-HA-tag (Santa, sc-7392, 1:1000 for IB and 1:100 for IP), anti-Flag-tag (Affinity, T0053, 1:1000 for IB and 1:100 for IP), anti-ALK5 (Affinity, AF5347, 1:1000 for IB, 1:300 for IF, and 1:100 for IP, 1:100 for PLA), anti-p-ALK5 (Affinity, AF8080, 1:1000 for IB and 1:100 for IHC), anti-CD68 (CST, 97778, 1:200 for IHC), anti-p-NFκB (CST, 3037S, 1:200 for IHC), anti-Na/K ATPase (Abcam, ab254025, 1:200 for IF), anti-KIM1 (Cloud-Clone Corp, LAA785Mu81, 1:100 for IF), anti-Smad2 (CST, #5339, 1:1000 for IB), anti-Smad3 (CST, #9523, 1:1000 for IB), anti-p-Smad2 (CST, #3108, 1:1000 for IB), anti-p-Smad3 (CST, #9520, 1:1000 for IB), anti-LAMP1 (Abcam, ab24170, 1:200 for IHC), anti-PPCA (Abcam, ab184553, 1:1000 for IB), anti-biotinylated SNL lectin (Vector Labs, B-1305-2, 1:500 for IB).
Anti-rabbit IgG, HRP-linked antibody (CST, 7074, 1:2000 for IB), anti-mouse IgG, HRP-linked antibody (ZSGB-BIO, ZB2305, 1:1000 for IB), Alexa Fluor 488 goat anti-rabbit IgG (Yeasen Biotech, catalog no. 33106ES60, 1:100 for IF), Alexa Fluor 594 goat anti-mouse IgG (Yeasen Biotech, catalog no. 33212ES60, 1:100 for IF), HRP-Streptavidin (Beyotime, A0305, 1:500 for IB). the non-heavy chain IgG secondary antibody (Santa Cruz, m-IgGκ BP-HRP, sc-516102, 1:1000 for IB) and mouse anti-Rabbit IgG HRP (Abmart, #M21006, 1:1000 for IB).
## Surface plasmon resonance (SPR)
A Biacore T200 instrument was used to measure the binding kinetics of human ALK51-125aa, ALK5162-403aa, ALK5200-503aa (Sino Biological Inc) and compounds to the NEU1. Measurements were performed at 25 °C. Samples were dissolved in HBS-EP+, pH 7.4 (GE Healthcare). Human NEU1 recombinant protein (gifted by Dr. Xiao Yibei lab) were immobilized on the sensor chip (CM5, GE Healthcare) using the amine-coupling method according to standard protocols. The recombinant NEU1 proteins coupling solution was injected over the activated chip surface to achieve an immobilization level of 8000–10,000 resonance units (RU) for proteins-NEU1 interaction measurement, and 100–200 Ru for small molecule compound-NEU1 interaction measurement. Human ALK5 recombinant protein were prepared at final concentrations of 500, 250, 125, 62.50, 31.25, 15.60, 7.80, and 0 (blank) nM by dilution into running buffer. The compounds were prepared at indicated concentrations by dilution into running buffer. Data were processed using standard double-referencing and fit to a 1:1 binding model using Biacore T200 Evaluation software to determine the association rate (Kon, M−1 s−1), dissociation rate (Koff, s−1), estimate of error (SE), Chi-Square (Chi2), and statistic and maximum response (Rmax; response units (RU)). The equilibrium dissociation constant (Kd) was calculated from the relationship KD = Koff/Kon (M).
## Statistics
Statistical analysis was performed using the GraphPad Prism software package. Differences among groups were tested by one-way ANOVA or two-way ANOVA, followed by Tukey’s test as appropriate. Differences between the two groups were tested using the unpaired t-test. Results were expressed as mean ± standard deviation (SD) and as the number (percent) for categorical variables. All tests were two-sided, and $p \leq 0.05$ was considered statistically significant.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Reporting Summary Peer Review File The online version contains supplementary material available at 10.1038/s41467-023-37450-8.
## Source data
Source Data
## Peer review information
Nature Communications thanks Alexey Pshezhetsky, Keiji Miyazawa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: Application of machine learning in predicting non-alcoholic fatty liver disease
using anthropometric and body composition indices
authors:
- Farkhondeh Razmpour
- Reza Daryabeygi-Khotbehsara
- Davood Soleimani
- Hamzeh Asgharnezhad
- Afshar Shamsi
- Ghasem Sadeghi Bajestani
- Mohsen Nematy
- Mahdiyeh Razm Pour
- Ralph Maddison
- Sheikh Mohammed Shariful Islam
journal: Scientific Reports
year: 2023
pmcid: PMC10043285
doi: 10.1038/s41598-023-32129-y
license: CC BY 4.0
---
# Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices
## Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with $82\%$, $52\%$ and $57\%$ accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas.
## Introduction
Non-alcoholic fatty liver disease (NAFLD)‒ the hepatic manifestation of metabolic syndrome‒ is the most common chronic liver disease1,2. Worldwide prevalence of metabolic syndrome and NAFLD has increased in parallel with increased obesity prevalence3–5, which is about 20–$30\%$ in developed countries and one-third among American adults6–8.
Obesity is a common metabolic risk factor associated with NAFLD9–11. The prevalence of NAFLD is directly related to increased body mass index (BMI) and central obesity12–14. Most studies have shown that visceral fat is an independent factor in generating hepatic steatosis, independent of BMI15,16. The amount of adipose tissue and its distribution differs between men and women17. Women have higher overall fat tissue with relatively more subcutaneous adipose tissue in the hips and thighs. At the same time, men accumulate visceral and subcutaneous fat mainly in the trunk and abdomen with continuous changes before and after puberty17–19. The increased fat distribution around the waist (i.e. apple-shaped body) is linked to NAFLD in both genders20. In a pear-shaped body, the subcutaneous fat accumulates mainly in the thighs and buttocks21,22, which is typical among females but can increase metabolic syndrome in males, which is a risk factor for NAFLD independent of central obesity23. In support of the role of fat distribution and anthropometric measures in NAFLD, studies have found several contributing factors, including abdomen circumferences, waist, neck and fat accrual in trunk and arms24–29.
Most people with NAFLD, including both children or adults, do not have differential symptoms at the early stages of the disease30. Notably, after the development of cirrhosis, different symptoms such as caput medusa, spider angioma, palmar erythema, ascites, and jaundice appear31. Therefore, early diagnosis is critical to prevent severe complications.
Ultrasonography and laboratory tests are typical diagnostic methods for detecting fatty liver disease. Ultrasound technique has relatively high accuracy in detecting the moderate-to-severe steatosis level and lower accuracy in earlier stages of NAFLD32. Notably, hepatic fibrosis cannot be diagnosed by ultrasonography14,33. Although typically used to detect fatty liver disease, laboratory tests are not useful for all ages and gender groups due to low accuracy34,35. Therefore, a precise, cost-effective, and non-invasive method to analyze symptoms of various stages of the fatty liver for NAFLD diagnosis is desirable. Such an approach is important to help with early diagnosis of NAFLD, which could help prevent hepatic steatosis progression to fibrosis, advanced cirrhosis, and hepatocellular carcinoma.
In recent years, machine learning (ML) models have been used as a novel approach in predicting NAFLD36–39. However, all of these studies have focused mainly on laboratory outcomes and have not considered body composition and anthropometric factors. Therefore, the primary aim of this study is to identify essential ML classifiers of NAFLDs using body composition and anthropometric indices. The secondary aim is to identify feature contributions to the prediction of NAFLDs.
## Study design and participants
This cross-sectional study was conducted to explore NAFLD phenotypes based on body composition and anthropometric indices. Participants were recruited from the eastern (Khorasan Razavi) and southern (Hormozgan) provinces of Iran, through advertisement on the notice boards of the university clinics, as well as via phone or email contact to potential participants. A total of 593 individuals aged above 13 years old were initially recruited. Eighty individuals were excluded from the study and 513 participants were remained. Exclusion criteria were as follows: the presence of underlying liver disease, taking medications (anti-hypertensive and anti-arrhythmic, anti-glycaemic, corticosteroids, nervous system agents, chemotherapy, Methotrexate and Tamoxifen), alcoholic patients with more than twice-a-week consumption, previous history of any type of cancer during the last year, history of surgery during the last 6 months, pregnant women. The study was conducted following the Declaration of Helsinki, and ethical approval was granted by Mashhad University of Medical Sciences following (Code: IR.MUMS.fm. REC.1395.64). All participants provided written informed consent. Also, written informed consent was obtained from guardians of participants aged under 18 years.
## Data collection
At each medical clinic, eligibility, demographics questionnaire, anthropometric, and body composition measurements were assessed by two trained nutritionists. Medical examination and disease diagnosis were performed by a general physician and an internal specialist, respectively. Demographic information, including sex, age, education, disease history and medications, were assessed by researcher using a questionnaire. Weight was measured using a digital weighing scale (Seca 704; Hamburg, Germany), height was measured using a wall height chart, and the body composition measures were assessed using InBody 270 (Inbody Co. Ltd, South Korea) body analyzer to measure per cent (%) body fat, total fat mass, muscle mass, as well as fat mass in the right/left leg, right/left arm and trunk with light clothing and without shoes. The circumferences of neck, chest, arm, wrist, waist, hips, abdomen, thighs, and length of ulna and leg were measured using a flexible tape measure with an accuracy of 0.1 cm. BMI was calculated by dividing weight (kilograms) by the height in meters squared26. Subcutaneous fat in the area below the scapula, arms biceps and triceps and the upper iliac crest was measured using a Saehan calliper (Saehan SH5020, Korea). Participants were also examined for acanthosis in the back of the neck and armpits and the presence of subcutaneous fat under the chin and at the back of the neck. A Fibroscan equipped with the M and XL probes (Echosens 504, Paris, France) was used to assess both controlled attenuation parameter (CAP) (dB/m) and liver stiffness measurement (LSM) (kPa) values simultaneously. A reliable LSM was defined as the median liver stiffness of the 10 measurements (a success rate of greater than $60\%$, and an IQR < $30\%$ of the median LSM value)40. CAP values range from 100 to 400 dB/m and the following cut-off values were used for the diagnosis of steatosis stages: Stage 0, < 238 dB/m, Stage 1, ≥ 238 to 260 dB/m, Stage 2, ≥ 260 to 292 dB/m, and Stage 3, ≥ 292 dB/m41. LSM values range from 1.5 to 75 kPa, and the following cut-off values were used for the diagnosis of liver fibrosis stages: no significant fibrosis or F0 < 6.2 kPa, mild fibrosis or F1 ≥ 6.2 to 7.6 kPa, moderate fibrosis or F2 ≥ 7.6 to 8.8 kPa, severe fibrosis or F3 ≥ 8.8 to 11.8 kPa and cirrhosis or F4 ≥ 11.8 kPa42.
## Statistical analysis
Descriptive and non-predictive data analysis was performed using SPSS version 21 software (SPSS, Inc., Chicago, IL). Data were expressed as mean ± standard deviation or frequencies. Between-group comparisons were performed using an independent sample t test and analysis of variance (ANOVA), followed by Tukey’s post hoc test. A P-value of less than 0.05 was considered statistically significant.
## Machine learning models
Three label variables were considered: fatty liver (stage I, II and III vs. no steatosis), steatosis, and fibrosis stages. Eight ML techniques were applied to the dataset to identify the best modelling approach. To this end, k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), AdaBoost and Naïve Bayes were tested. An extant explanation of these classifiers can be found elsewhere43. Testing of these models was performed using the Scikit-learn library in Python programming language44.
To comprehensively compare different classifiers, we trained and evaluated dataset 50 times. This is because different classifiers sometimes predict slightly different outputs and initial points are different for a specific classifier in each run. Thus, a reliable output can be estimated by averaging each classifier several times. Model accuracy and area under the curve (AUC) are reported for each ML technique. Importance values are reported for individual feature variables.
Pre-processing involved data normalization and segmentation. The few missing values in the numerical results of the experiments were replaced using the Linear Interpolation method45. Principal component analysis (PCA) was used to extract the attribute of the data46,47. Data were divided into two parts, train and test. Processing involved feature selection and classification with the best feature. The model processing involved a variety of models. The model with the highest performance was selected.
## Patient consent
All patients provided written consent for participation in this study. For participants aged under 18 years, written informed consent was obtained from their guardians.
## Ethics approval
Ethical approval was received from the research ethics committee at Mashhad University of Medical Sciences (Code: IR.MUMS.fm. REC.1395.64).
## Results
In total 513 participants (240 males and 273 females) took part in the study, of whom 169 ($74.1\%$) male and 220 ($80.6\%$) female cases had a degree of hepatic steatosis. The mean age, weight, and BMI were 37.04 ± 15.44 years, 77.26 ± 17.31 kg, and 28.15 ± 4.89 kg m2, respectively. Overall demographic characteristics and biochemical measures are presented in Table 1. Significant differences were found in most anthropometric variables between male and female participants (see Tables 2 and 3).Table 1Demographic information of study participants. VariableNPercentage (%)Gender Female27052.6 Male23846.4DiabetesNon-diabetic44285.7Diabetic6613.3Mean ± SDRangeAge (years)37.04 ± 15.449–74Height (cm)165.44 ± 11.18132–189Weight (kg)77.26 ± 17.3127–141BMI (kg/m2)28.15 ± 4.8915–52Triglyceride (mg/dL)130.44 ± 77.1617–563Total cholesterol (mg/dL)174.99 ± 42.293–301LDL-cholesterol (mg/dL)101.28 ± 32.4748–191HDL-cholesterol (mg/dL)43.61 ± 10.1925–86Haemoglobin (g/dL)14.00 ± 1.8410–24AST (U/L)29.98 ± 23.516–308ALT (U/L)38.28 ± 35.455–325GGT (U/L)28.29 ± 27.586–211FBS (mg/dL)96.31 ± 26.4650–327HbA1c (%)5.50 ± 1.803–25SD standard deviation, cm centimetres, kg kilograms, BMI body mass index, kg/m2 kilograms per metres squared, mg/dL milligrams per decilitre, g/dL grams per decilitre, U/L units per litre, AST aspartate aminotransferase, ALT alanine transaminase, GGT gamma-glutamyl transferase, FBS fasting blood glucose, HbA1c glycosylated haemoglobin. Table 2A comparison of the anthropometric variables across different stage of the hepatic steatosis in male and female participants. VariablesGenderHepatic SteatosisP-valueGrade 0Grade ΙGrade ΙΙGrade ΙΙΙNumberF533955129–M81384877–Age, yearsF31.13 ± 15.79#34.64 ± 16.6742.02 ± 15.37#41.79 ± 15.88#0.001M27.54 ± 12.3235.51 ± 19.2939.33 ± 13.91#36.12 ± 13.68#0.001Weight; kgF59.90 ± 11.4067.09 ± 11.08#69.26 ± 11.24#79.93 ± 14.56#&$< 0.001M66.71 ± 13.5975.69 ± 13.90#82.56 ± 10.39#87.84 ± 16.70#&< 0.001Height; cmF156.84 ± 8.46158.64 ± 8.04155.83 ± 7.68159.00 ± 6.940.206M167.50 ± 13.60167.78 ± 13.47171.23 ± 6.68172.15 ± 8.990.31BMI; kg m−2F24.30 ± 4.0026.53 ± 3.1628.45 ± 3.44#31.60 ± 5.21#&$< 0.001M23.83 ± 3.2126.86 ± 3.21#28.19 ± 3.53#29.67 ± 4.03#&< 0.001Arm circumference; cmF27.60 ± 3.5329.74 ± 3.14#31.30 ± 2.58#33.44 ± 4.23#&$< 0.001M28.81 ± 3.0430.51 ± 2.7031.90 ± 2.53#33.27 ± 3.47#&< 0.001Neck circumference; cmF31.68 ± 2.3732.77 ± 1.6533.25 ± 1.89#35.70 ± 2.34#&$< 0.001M36.09 ± 2.8337.80 ± 2.7338.94 ± 2.87#39.56 ± 2.87#&< 0.001Chest circumference; cmF89.04 ± 8.7994.30 ± 8.19#98.78 ± 7.61#106.18 ± 9.56#&$< 0.001M89.32 ± 10.2496.78 ± 10.43#101.34 ± 7.52#104.26 ± 10.86#&< 0.001Waist circumference; cmF81.74 ± 10.8586.69 ± 7.2594.57 ± 7.92#&101.96 ± 10.53#&$< 0.001M85.71 ± 9.0896.42 ± 7.99#99.83 ± 8.75#102.68 ± 10.07#&< 0.001Abdomen circumference; cmF85.82 ± 11.1191.77 ± 7.60#98.15 ± 7.70#105.33 ± 10.52#&$< 0.001M87.61 ± 8.8898.50 ± 7.12#101.27 ± 8.93#104.15 ± 9.83#&< 0.001Hip circumference; cmF98.03 ± 12.80101.87 ± 9.20103.50 ± 8.82106.60 ± 13.60#0.002M96.70 ± 8.88101.31 ± 6.67103.88 ± 7.21#106.57 ± 8.00#&< 0.001Wrist circumference; cmF14.98 ± 0.9415.63 ± 0.74215.78 ± 1.13#16.30 ± 1.44#< 0.001M16.71 ± 0.8717.44 ± 1.11#17.66 ± 0.85#17.92 ± 1.10#< 0.001Subscapular skinfold; mmF15.40 ± 5.4019.05 ± 5.1921.29 ± 4.81#25.33 ± 9.10#&< 0.001M14.01 ± 5.3219.58 ± 7.06#19.56 ± 9.05#22.93 ± 8.56#< 0.001Biceps skinfold; mmF8.02 ± 3.299.47 ± 3.389.94 ± 2.4812.76 ± 5.17#&$< 0.001M6.69 ± 3.658.83 ± 3.809.01 ± 4.14#9.34 ± 3.15#0.001Triceps skinfold; mmF11.22 ± 3.4414.35 ± 6.19#14.02 ± 3.7516.33 ± 6.49#< 0.001M9.02 ± 3.7111.16 ± 5.0510.40 ± 4.3411.48 ± 3.98#0.019Suprailiac skinfold; mmF14.82 ± 5.2717.59 ± 6.0819.26 ± 6.06#21.30 ± 7.54#< 0.001M13.98 ± 5.9618.98 ± 7.35#16.62 ± 7.4119.42 ± 7.99#0.001Abbreviation. BMI: body mass index. Data are presented as means ± standard deviations. P values were obtained from analysis of variance (ANOVA), followed by Tukey’s post hoc test.#Significant difference ($P \leq 0.05$) compared with grade 0; &Significant difference ($P \leq 0.05$) compared with grade 1; $Significant difference ($P \leq 0.05$) compared with grade 2.Table 3A comparison of the anthropometric variables across different stage of the hepatic fibrosis in male and female participants. VariablesGenderHepatic fibrosisP-valueGrade 0Grade ΙGrade ΙΙGrade ΙΙΙ–IVNumberF137823717–M156591415–Age, yearsF34.78 ± 16.3638.11 ± 17.0949.33 ± 10.64#50.12 ± 8.09#0.005M33.05 ± 14.8736.42 ± 14.4736.02 ± 15.0242.15 ± 15.300.119Weight; kgF65.97 ± 12.7372.39 ± 14.79#81.56 ± 22.85#81.77 ± 12.44#< 0.001M76.25 ± 14.8383.92 ± 15.62#90.08 ± 20.44#90.37 ± 18.95#< 0.001Height; cmF157.78 ± 8.53157.48 ± 6.70157.55 ± 3.84156.12 ± 6.490.9150M169.34 ± 11.82171.77 ± 8.39177.08 ± 11.01171.46 ± 9.090.445BMI; kg m−2F26.41 ± 4.2129.07 ± 4.99#32.91 ± 9.21#33.46 ± 4.13#< 0.001M26.55 ± 3.6628.36 ± 4.41#31.28 ± 4.08#&30.43 ± 4.36#< 0.001Arm circumference; cmF29.60 ± 3.8231.66 ± 4.24#33.05 ± 7.3233.25 ± 3.320.001M30.83 ± 3.1032.34 ± 3.49#33.52 ± 4.23#33.53 ± 4.20#< 0.001Neck circumference; cmF32.93 ± 2.2733.89 ± 3.3035.55 ± 3.08#35.00 ± 3.330.003M37.57 ± 2.8438.98 ± 2.78#40.28 ± 2.88#41.15 ± 4.35#< 0.001Chest circumference; cmF94.30 ± 9.7399.46 ± 10.27#110.88 ± 16.56#&108.18 ± 9.60#< 0.001M96.83 ± 11.55100.68 ± 11.14107.04 ± 8.37#106.88 ± 10.37#< 0.001Waist circumference; cmF87.93 ± 11.0694.54 ± 12.68#104.83 ± 17.32#105.37 ± 11.66#< 0.001M94.32 ± 9.7399.82 ± 11.81#106.64 ± 9.77#&104.23 ± 10.25#< 0.001Abdomen circumference; cmF91.79 ± 10.9899.01 ± 12.68#108.27 ± 16.02#108.37 ± 10.72#< 0.001M96.00 ± 9.25101.15 ± 11.56#108.52 ± 9.23#&106.64 ± 10.55#< 0.001Hip circumference; cmF101.34 ± 11.28103.33 ± 12.13108.11 ± 17.07104.62 ± 20.850.346M101.22 ± 8.25104.64 ± 8.24#108.64 ± 8.76#106.15 ± 8.87< 0.001Wrist circumference; cmF15.49 ± 1.1515.71 ± 1.2216.71 ± 1.9716.00 ± 0.700.052M17.44 ± 1.1517.61 ± 0.9718.12 ± 1.2217..36 ± 1.310.075Subscapular skinfold; mmF18.66 ± 6.3421.34 ± 8.2128.86 ± 15.23&23.96 ± 5.390.001M19.10 ± 7.0319.56 ± 9.1026.77 ± 10.79#&22.00 ± 7.690.002Biceps skinfold; mmF9.38 ± 3.2710.88 ± 5.3114.03 ± 8.77#9.92 ± 2.940.013M8.71 ± 3.808.60 ± 3.488.99 ± 3.768.54 ± 3.370.979Triceps skinfold; mmF13.23 ± 4.8513.97 ± 4.6218.40 ± 13.0015.44 ± 3.920.76M10.70 ± 4.3810.23 ± 3.7712.95 ± 4.9610.66 ± 3.160.081Suprailiac skinfold; mmF17.15 ± 5.8518.88 ± 8.2723.97 ± 10.18#19.92 ± 4.050.037M17.32 ± 6.5717.05 ± 7.4423.50 ± 11.31#&15.77 ± 6.700.004BMI body mass index. Data are presented as means ± standard deviations. P values were obtained from analysis of variance (ANOVA), followed by Tukey’s post hoc test.#Significant difference ($P \leq 0.05$) compared with grade 0; &Significant difference ($P \leq 0.05$) compared with grade 1; $Significant difference ($P \leq 0.05$) compared with grade 2.
## Machine learning results
Figures 1, 2, 3 present box plots for each classification method applied to three outcomes. Random Forest (RF) method generated the most accurate ML model for fatty liver (presence of any stage), steatosis stage and fibrosis stage. Average accuracy and AUC values resulted from RF were 0.82 and 0.84 for fatty liver, 0.52 and 0.69 for steatosis stages, 0.57 and 0.58 for fibrosis stages, respectively. Average accuracy and AUC are presented in the Supplemental file (Model Iterations) for all conditions. Moreover, sensitivity, specificity, true positive and true negative measures were presented for fatty liver disease. Figure 1Box plots showing different classification methods applied to the dataset for presence of fatty liver. Box plots are generated by performing 50 individual runs for each classifier. This will assure that the achieved results are reliable. Figure 2Box plots showing different classification methods applied to the dataset for stages of steatosis. Box plots are generated by performing 50 individual runs for each classifier. This will assure that the achieved results are reliable. Figure 3Box plots showing different classification methods applied to the dataset for stages of fibrosis. Box plots are generated by performing 50 individual runs for each classifier. This will assure that the achieved results are reliable.
Feature variables with the highest predictability for fatty liver were abdomen circumference (IV; average importance value = 0.061), waist circumference (IV = 0.061), chest circumference (IV = 0.054), trunk fat (IV = 0.056) and BMI (IV = 0.053); for steatosis, the stage was abdominal circumference (IV = 0.053), waist circumference (IV = 0.052), chest circumference (IV = 0.052), trunk fat (IV = 0.051) and BMI (IV = 0.050); and for fibrosis were abdominal circumference (IV = 0.049), waist circumference (IV = 0.049), chest circumference (IV = 0.043), BMI (IV = 0.045) and weight (IV = 0.045). See Figs. 4, 5, 6 and Tables 4, 5, 6.Figure 4Box plots showing relative feature importance for presence of fatty liver. hx history, cm centimeter, kg kilograms, BMI body mass index, MUAC mid-upper arm circumference. Figure 5Box plots showing relative feature importance for stages of steatosis. hx history, cm centimeter, kg kilograms, BMI body mass index, MUAC mid-upper arm circumference. Figure 6Box plots showing relative feature importance for stages of fibrosis. hx history, cm centimeter, kg kilograms, BMI body mass index, MUAC mid-upper arm circumference. Table 4Variable importance from the random forest method for fatty liver (presence of any stage).VariableImportance valueGender0.009424Age0.032057Diabetes history0.012520Height0.024799Weight0.047842BMI (kg/m2)0.053846Total fat (%)0.038906Fat mass (kg)0.049188Muscle mass(kg)0.029346Right leg fat (kg)0.035543Right leg muscle (kg)0.029538Left leg fat (kg)0.034062Left leg muscle (kg)0.030156Right arm fat (kg)0.039354Right arm muscle (kg)0.031292Left arm fat (kg)0.045013Left arm muscle (kg)0.031422Trunk fat (kg)0.056131Trunk muscle (kg)0.030482Neck circumference (cm)0.038175Chest circumference (cm)0.054649Waist circumference (cm)0.061709Abdominal circumference (cm)0.061626Hip circumference (cm)0.034063Mid upper arm circumference (cm)0.041080Acanthosis nigricans0.015046Neck fat0.016576Sub-chin fat0.016157Table 5Variable Importance from the random forest method for steatosis stage. VariableImportance valueGender0.011916Age0.033425Diabetes history0.018186Height0.026744Weight0.04809BMI (kg/m2)0.050049Total fat (%)0.035433Fat mass (kg)0.043273Muscle mass(kg)0.034973Right leg fat (kg)0.034236Right leg muscle (kg)0.035191Left leg fat (kg)0.033943Left leg muscle (kg)0.033304Right arm fat (kg)0.038543Right arm muscle (kg)0.03686Left arm fat (kg)0.037995Left arm muscle (kg)0.037145Trunk fat (kg)0.051388Trunk muscle (kg)0.035925Neck circumference (cm)0.039854Chest circumference (cm)0.05247Waist circumference (cm)0.052178Abdominal circumference (cm)0.053468Hip circumference (cm)0.03506Mid upper arm circumference (cm)0.040977Acanthosis nigricans0.015976Neck fat0.018529Sub-chin fat0.014871Table 6Variable importance from the random forest method for fibrosis. VariableImportance valueGender0.013453Age0.037938Diabetes history0.020626Height0.032533Weight0.045225BMI (kg/m2)0.045191Total fat (%)0.038413Fat mass (kg)0.039101Muscle mass(kg)0.041035Right leg fat (kg)0.035216Right leg muscle (kg)0.040116Left leg fat (kg)0.034831Left leg muscle (kg)0.039268Right arm fat (kg)0.036479Right arm muscle (kg)0.041001Left arm fat (kg)0.035263Left arm muscle (kg)0.040377Trunk fat (kg)0.041605Trunk muscle (kg)0.040320Neck circumference (cm)0.040930Chest circumference (cm)0.043684Waist circumference (cm)0.049580Abdominal circumference (cm)0.049180Hip circumference (cm)0.037576Mid upper arm circumference (cm)0.037792Acanthosis nigricans0.013215Neck fat0.018472Sub-chin fat0.011579 Further assessment identified gender-specific features (see Supplemental Figs. 1–6; Supplemental Tables 1–6). Important predictor factors for fatty liver disease among females were waist circumference (IV = 0.057), abdomen circumference (IV = 0.056), trunk fat (IV = 0.055), fat mass (IV = 0.052), chest circumference (IV = 0.048), and BMI (IV = 0.048) were the most important features. Among males, waist circumference (IV = 0.053), chest circumference (IV = 0.052), trunk fat (IV = 0.051), BMI (IV = 0.052), abdomen circumference (IV = 0.049) and fat mass (IV = 0.048) had the highest predictive value for fatty liver. Important predictor factors for steatosis among females were abdomen circumference (IV = 0.048), waist circumference (IV = 0.047), weight (IV = 0.046), trunk fat (IV = 0.045), fat mass (IV = 0.044), and BMI (IV = 0.043) were the most important features. Among males, waist circumference (IV = 0.051), chest circumference (IV = 0.050), abdomen circumference (IV = 0.049), trunk fat (IV = 0.048), BMI (IV = 0.048), and fat mass (IV = 0.046) had the highest predictive value for steatosis. Important predictor factors for fibrosis among females were abdomen circumference (IV = 0.048), waist circumference (IV = 0.047), BMI (IV = 0.046), trunk fat (IV = 0.045), chest circumference (IV = 0.043), and muscle mass (IV = 0.043) were the most important features. Among males, abdomen circumference (IV = 0.045), waist circumference (IV = 0.043), weight (IV = 0.043), BMI (IV = 0.043), right arm fat (IV = 0.042) and fat mass (IV = 0.042) had the highest predictive value for fibrosis.
## Discussion
This study applied ML techniques to determine the optimal body composition and anthropometric classifier of NAFLD and identify feature contribution to the prediction of the disease. RF generated the most accurate ML model to predict fatty liver presence, steatosis (stages) and fibrosis. To our knowledge, this is the first study applying ML on body composition and anthropometric data to predict NAFLD. High accuracy ($82\%$) highlights the potential for applying ML techniques for the primary prevention and screening of NAFLD using anthropometric measurements.
Previous studies using ML techniques to predict fatty liver disease have mainly focused on biochemical measurements, with similar levels of accuracy ($83.0\%$) using Bayesian Network38, ($76.3\%$) Logistic Regression37, ($86.4\%$) RF39 and ($80\%$) Classification Tree techniques36. However, we tested the predictive value of body composition and anthropometric measurements rather than biochemical variables. Anthropometry as a lower-cost and more feasible approach can be considered a primary screening method for fatty liver disease.
Abdominal obesity is a significant risk factor leading to NAFLD27. Waist circumference and trunk fat have been shown to be significantly predicting the risk of NAFLD24. Although BMI is one of the risk factors of NAFLD31, it has been argued that BMI is limited compared to other anthropometric measures (e.g., waist circumference) in identifying lean NAFLD individuals25. In a similar vein, the findings of the present study clearly show the importance of these body composition and anthropometric measures and their relative contribution to the prediction of NAFLD.
Neck circumference reflects the amount of subcutaneous fat in the upper body, and is a reliable factor in determining central obesity48. A positive correlation has been shown between neck circumference and hepatic steatosis26,28. Neck circumference showed a positive association with other anthropometric components, such as BMI and waist and waist-to-hip circumference. In the present study, neck circumference contributed almost equally to hepatic steatosis and fibrosis.
A study by Subramanian revealed that the level of arm fat index in both males and females had a negative association with the degree and severity of NAFLD29. In our study, a strong and positive relationship between arm circumference and the severity of steatosis and fibrosis was detected, validated by the ML model. Rafiee et al. showed that the amount of fat in hips and legs and circumference of hip negatively associated with fatty liver and the severity of the disease. In contrast, the waist-to-hip ratio was closely associated with fatty liver. They also showed that the accuracy of this ratio in predicting NAFLD was greater than BMI and waist-to-height ratio49.
Most ML studies for the prediction of NAFLD have used the ultrasonography technique to diagnose fatty liver disease36–39. Ultrasound is a commonly used method for the diagnosis of hepatic steatosis50. Ultrasonography is a safe, well-tolerated, non-invasive and low-cost technique50; however, there are limitations associated with ultrasound use, including limited capability in detecting fatty infiltration (less than $20\%$ steatosis), operator dependency and subjective assessment51,52, and ML is expected to minimise some of these. Application of ML techniques on body composition and anthropometric measures as a less time-consuming and easy to undertake method can help physicians in their clinical decision making.
The presence of liver fibrosis in patients with NAFLD is considered the strongest predictor of long-term outcome53. NAFLD Fibrosis Score (NFS) and Fibrosis-4 (FIB-4) have been recommended as appropriate methods for the initial assessment of fibrosis in NAFLD patients54. Both of these methods use a combination of variables including age, BMI and biochemical measures (i.e. aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelets, etc.). Graupera et al. concluded that NFS and FIB-4 are not optimal for screening as they correlate poorly with liver stiffness55. In their study, waist circumference was found to be the ideal measure for fibrosis screening among high risk people from general population55. However, other studies found that NFS and FIB-4 have the potential to detect advanced fibrosis and the progression of fibrosis among people with NAFLD56. It seems that NFS and FIB-4 are more useful in the diagnosis of fibrosis in NAFLD but not for fibrosis screening among the general populations. The present study showed suboptimal accuracy ($57\%$) in detecting fibrosis using less expensive and non-invasive factors i.e. anthropometric and body composition measures. Further studies might explore a combination of these methods including anthropometric, body composition and biochemical variables altogether.
The proposed algorithm identified in this research can be used by the health systems for several reasons. Screening of the presence or absence of NAFLD with the help of non-invasive anthropometric measurements can be achieved with simple and cheap equipment57. Moreover, performing the measurement task needs less specialty knowledge therefore can be implemented in several health centres (e.g., primary practice) and also remote areas. Once validated, the resulted assistive technology can serve the clinicians in the prevention of liver diseases. There are limitations of the present research that need to be addressed. A small sample size might have potentially limited the results of ML prediction. Although, the small sample size was accounted for by multiple cross-validations, which reduced potential errors. Future studies with larger sample sizes can allocate separate validation sets and evaluate the model. Moreover, even though the most common method for fatty liver diagnosis, the ultrasound technique is not the gold standard. Using liver biopsy outcomes would generate more valid results. Also, to increase the predictive accuracy of the proposed model for NAFLD prediction, future studies should include other body composition and anthropometric measures such as sagittal abdominal diameter (SAD) and peri-renal fat58.
## Conclusion
Present findings show that applying a ML classification model on anthropometric and body composition variables predicted the presence of fatty liver disease. ML-based decision support systems offer potential to assist physicians with screening, diagnosis and prevention of NAFLD. ML-based decision support systems could be of particular value for providing services at a population level and remote health care where there is a lack of trained specialists.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-32129-y.
## References
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|
---
title: Biomechanical stability of hernia-damaged abdominal walls
authors:
- Ali Karrech
- Hairul Ahmad
- Jeffrey M Hamdorf
journal: Scientific Reports
year: 2023
pmcid: PMC10043288
doi: 10.1038/s41598-023-31674-w
license: CC BY 4.0
---
# Biomechanical stability of hernia-damaged abdominal walls
## Abstract
Hernia occurs when the peritoneum and/or internal organs penetrate through a defect in the abdominal wall. Implanting mesh fabrics is a common way to reinforce the repair of hernia-damaged tissues, despite the risks of infection and failure associated with them. However, there is neither consensus on the optimum mesh placement within the abdominal muscles complex nor on the minimum size of hernia defect that requires surgical correction. Here we show that the optimum position of the mesh depends on the hernia location; placing the mesh on the transversus abdominis muscles reduces the equivalent stresses in the damaged zone and represents the optimum reinforcement solution for incisional hernia. However, retrorectus reinforcement of the linea alba is more effective than preperitoneal, anterectus, and onlay implantations in the case of paraumbilical hernia. Using the principles of fracture mechanics, we found that the critical size of a hernia damage zone becomes severe at 4.1 cm in the rectus abdominis and at larger sizes (5.2–8.2 cm) in other anterior abdominal muscles. Furthermore, we found that the hernia defect size must reach 7.8 mm in the rectus abdominis before it influences the failure stress. In other anterior abdominal muscles, hernia starts to influence the failure stress at sizes ranging from 1.5 to 3.4 mm. Our results provide objective criteria to decide when a hernia damage zone becomes severe and requires repair. They demonstrate where mesh should be implanted for a mechanically stable reinforcement, depending on the type of hernia. We anticipate our contribution to be a starting point for sophisticated models of damage and fracture biomechanics. For example, the apparent fracture toughness is an important physical property that should be determined for patients living with different obesity levels. Furthermore, relevant mechanical properties of abdominal muscles at various ages and health conditions would be significant to generate patient specific results.
## Introduction
Hernia is a common pathology that is believed to arise from biomechanical and biochemical alterations within a specific muscle or a group of muscles. More than 20 million surgeries are performed annually around the globe to correct hernias1. While these surgeries might be considered routine procedures, the cumulated recurrence risk can range from 15 to $35\%$ after hernia repair2, according to a large scale study of ventral hernias that covered 2.5 million people over a period of 4 years. Predicting the size of hernia defect that is significant enough to require surgical correction, as well as predicting the hernia defect size requiring mesh reinforcement and determining the optimum positioning of mesh implants within the abdominal wall (AW) for adequate mechanical reinforcement should reduce the risk of hernia recurrence. Experiment-informed three-dimensional (3D) computational modelization can be instrumental to objectively predict the anatomic functioning of abdominal muscles. For example, the finite element method (FEM) has been used to simulate the genesis of inguinal hernia and demonstrate Keith’s long-standing conjectures3 regarding the diminishing risk of hernia with the increase of muscular mass4. In addition, FEM has been used to examine the constitutive behaviour of various tissues in the AW5. More recently, holistic models based on FEM have been developed to simulate the biomechanics of AWs subjected to local weaknesses in various locations seeking to address challenging scenarios of hernia development6–9. For example, Tuset et al.9 concluded that intestinal stoma location has no significant impact on the resulting stress and strain distributions except when it is located in the linea alba. As a predictive modelling tool, FEM can complement clinical research and facilitate the surgeons’ decisions. For example, it can be instrumental to visualise, optimise and test hernia repair scenarios before operation given the large number of patient-personalised solutions that are conceivable. A careful survey of the literature reveals that there are no numerical studies that investigate the physics of hernia repair in a systematic manner. Therefore, this paper showcases the potential of FEM in addressing the reinforcement of hernia-damaged AW.
In the present study, we developed a finite element computational setup to predict the response of a typical abdominal wall (AW) to intra-abdominal pressure (IAP) and investigate the effects of AW integrity on the overall biomechanial behaviour. AW integrity has been considered as an experimental factor by examining abdominal muscles with and without hernia-related damage. Where damage has occurred, the influence of repair mode on the response has been investigated by varying the position of the mesh through the AW. IAP is known to depend on the health conditions of the patient (e.g. obesity, pregnancy). For example, the healthy weight individuals have IAP varying from 0.66 to 0.93kPa while those with clinically severe obesity have IAP measurements ranging from 1.2 to 1.9kPa10,11.
There are four essential sections in this paper. The first section is introductory and includes a brief literature survey on AW modelling using FEM. The second section describes the numerical model and explains the constitutive relations used for integration. The third section is dedicated to the numerical results and their analysis with special focus on the significance of damage induced stress concentration in abdominal muscles. The fourth section draws the conclusions of this study and summarises the main interpretations.
## Description of the model
The geometry of the model is depicted in Fig. 1 representing the reconstructed abdominal wall. This geometry was obtained by superimposing the external oblique (EO), internal oblique (IO), rectus abdominis (RA), transversus abdominis (TA), and linea alba (LA) muscles and aponeuroses (for pragmatic purposes, we combined muscles and aponeuroses functionally), where LA receives the oblique and abdominis muscles and runs down the midline of the abdomen. The geometries of these muscles were based on micro-computed tomography scans available in the BodyParts3D anatomy database12. The 3D models of the muscles were then refined using Meshmixer13 to create smooth triangulated surface geometries and store them in the object file format. Without merging them in Meshmixer, the obtained surface triangulations were exported to the general purpose finite element software Abaqus14 as individual meshes in the standard tessellation format (STL). In abaqus, the individual STL tessellations were imported as orphan surface meshes. A 3D geometry with added volume was then created from each orphan mesh – this geometry can be manipulated as a standard computer-aided engineering part in Abaqus. Extruded cuts were applied to the geometrical parts representing the abdominal muscles to isolate an assembled subdomain of interest for hernia calculation in the umbilical region. Tetrahedral elements were used in the isolated subdomain for subsequent analyses. In the subdomain isolating the umbilical region, 32125, 32205, 14111, 30099, and 2844 elements were used for the EO, IO, RA, TA, and LA muscles, respectively (Fig. 2a).Figure 1Anatomic geometry of AW where the upper regions of the left transversus abdominis, rectus abdominis, internal oblique, and external oblique were removed to better illustrate the assembly.
At the midline of the subdomain of interest, LA was prevented from translating vertically (x-axis) and circumferentially (z-axis) and left free to translate radially to represent the symmetry conditions. The ends of the EO, IO, and TA muscles in the lumbar zone were prevented from moving by applying encastré boundary conditions (Fig. 2b). The bottom and top surfaces of the subdomain were left free to translate radially and circumferentially and to rotate around the vertical axis; all other degrees of motion were prevented (Fig. 2c). These conditions suggest that the superior and inferior surfaces act like planes of symmetry. Initially, the tissues were assumed to be free of active work and pre-stresses. The internal surface of the AW was subjected to an internal pressure of 0.8kPa, representing the IAP of an average normal body group person. Figure 2(a) Representation of the finite element mesh (b) top view of the AW subdomain with boundary conditions, and (c) side view of the AW subdomain with boundary conditions.
Surface-to-surface contact models were used to predict the mechanical interactions between the muscles. There are three interfaces of mechanical interaction, which are between the TA and IO, the IO and RA, as well as the IO and EO. These interfaces were modelled using a finite sliding formulation without geometrical adjustment or surface smoothing. The tangential contact within these interfaces was considered frictionless. The linea alba was connected to the above muscles using tie constraints, where LA plays the role of the master surface. This constraint ensures that the translational and rotational degrees of freedom are equal on the tied interface.
Where a hernia occurs in the AW, it was modelled as a damage zone of diameter 8 mm throughout the wall. The mesh implant used to simulate hernia repair or wall reinforcement was considered to have a *Young modulus* of 8 MPa, based on the experimental data of Kirilova et al.15. Poisson’s ratio of the mesh implant was 0.3 according to the experimental data of Szepietowska and Lubowiecka16. Mesh implants usually come in small size 4.3 cm or medium size 6.4 cm17; given the symmetry boundary conditions, we considered the quarter of a mesh implant of size 3 cm and thickness 0.6 mm. It is modelled with shell elements (S4R: 4 node doubly curved, reduced integrated, hourglass control, and finite membrane strains). Tacks or sutures are used in practice to connect the mesh implant to the AW. In this model, an embedding constraint is used to couple the mesh with the hosting muscles. This constraint ensures that the translational degrees of freedom at the nodes of the embedded elements (lying within a host element) are eliminated. Their values are constrained by interpolating the corresponding translations of the host nodes.
As soft tissues, muscles can be modelled as hyperelastic materials such that the behaviour derives from a Helmholtz free-energy function. In this paper, we opted for an Arruda-Boyce potential, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathfrak {w}$$\end{document}w, which includes a volumetric term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathfrak {u}$$\end{document}u, and an isochoric term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathfrak {e}$$\end{document}e, as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{array}{l} \displaystyle \mathfrak {w}(J, \varvec{C}) = \mathfrak {u} (J) + \mathfrak {e} (J, \varvec{C}) \end{array} \end{aligned}$$\end{document}w(J,C)=u(J)+e(J,C)where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{C} = \varvec{F}^T\varvec{F}$$\end{document}C=FTF is the right Cauchy-Green tensor, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{F}$$\end{document}F is the gradient of deformation, and J is the dimensionless dilatational ratio corresponding to the determinant of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{F}$$\end{document}F. The three eigenvalues of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{F}$$\end{document}F denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{\lambda _X\}_{X=x,y,z}$$\end{document}{λX}X=x,y,z are the principal stretches that verify \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda _x \lambda _y \lambda _z= J = \text {det}\varvec{F} >0$$\end{document}λxλyλz=J=detF>0. For isotropic materials, the principal stretches are sufficient to describe Helmholtz’s free-energy. Isotropy is adequate given that an individual layer of the abdominal wall musculature (e. g. internal oblique or rectus abdominis) tends to work as a single unit to achieve truncal movement in a specific direction. The two terms of Helmholtz’s free-energy (Eq. [ 1]) can be expressed as follows:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{array}{l} \displaystyle \mathfrak {u} (J) = \frac{1}{D} \left(\frac{J^2 - 1}{2} - \text {ln} J\right) \text { and } \mathfrak {e} (J, \varvec{C}) = \mu \sum _{$i = 1$}^{5} \frac{C_i}{\lambda _m^{2i-2}} \left(\bar{I}_1^i -3^i\right) \end{array} \end{aligned}$$\end{document}u(J)=1DJ2-12-lnJande(J,C)=μ∑$i = 15$Ciλm2i-2I¯1i-3iwhere D, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μ, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda _m$$\end{document}λm are parameters that can be calibrated experimentally, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{I}_1$$\end{document}I¯1 is the first invariant (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{I}_1 = \lambda _x^2 + \lambda _y^2 +\lambda _z^2)$$\end{document}I¯1=λx2+λy2+λz2) and the constants are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_1=$\frac{1}{2}$$$\end{document}C1=$\frac{1}{2}$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_2=$\frac{1}{20}$$$\end{document}C2=$\frac{1}{20}$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_3=$\frac{11}{1050}$$$\end{document}C3=$\frac{11}{1050}$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_4=$\frac{19}{7000}$$$\end{document}C4=$\frac{19}{7000}$, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_5=$\frac{519}{673750}$$$\end{document}C5=$\frac{519}{673750.}$ The second Piola-Kirchhoff stress can be derived from Eq. [ 2]:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{array}{l} \displaystyle \varvec{S} = 2 \frac{\partial \mathfrak {u} (J) }{\partial \varvec{C} } + 2 \frac{\partial \mathfrak {e} (J, \varvec{C}) }{\partial \varvec{C} } = 2 \mathfrak {u}' (J) J_{,\varvec{C}} + 2 \frac{\partial \mathfrak {e} }{\partial \bar{I}_1 } \bar{I}_{1,\varvec{C}} \end{array} \end{aligned}$$\end{document}$S = 2$∂u(J)∂C+2∂e(J,C)∂$C = 2$u′(J)J,C+2∂e∂I¯1I¯1,Cand Cauchy stress can be deduced from Eq. [ 3] as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{\sigma } = \varvec{F} \varvec{S} \varvec{F}^T/J$$\end{document}σ=FSFT/J:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{array}{l} \displaystyle \varvec{\sigma } = \mathfrak {u}' (J) \varvec{1} + 2\mu \sum _{$i = 1$}^{5} \frac{i C_i}{\lambda _m^{2i-2}} \bar{I}_1^{i-1} \varvec{b} \end{array} \end{aligned}$$\end{document}σ=u′(J)1+2μ∑$i = 15$iCiλm2i-2I¯1i-1bwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{b} = \varvec{F} \varvec{F}^T$$\end{document}b=FFT is the left Cauchy-Green tensor. Hence, it can be verified that the hydrostatic pressure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = \text {tr} \varvec{\sigma } /3 = \mathfrak {u}' (J) = \frac{1}{D} \left(J - J^{-1}\right)$$\end{document}p=trσ/3=u′(J)=1DJ-J-1, which means that the initial bulk modulus is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K = \frac{2}{D}$$\end{document}$K = 2$D. For an incompressible material, the initial shear modulus can be obtained as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$G = 2$ \frac{\partial \mathfrak {e} }{\partial \bar{I}_1 }$$\end{document}$G = 2$∂e∂I¯1 at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bar{I}_1 = 3}$$\end{document}I¯1=3, which means that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\displaystyle $G = 2$ \mu \sum _{$i = 1$}^{5} \frac{i C_i}{\lambda _m^{2i-2}} 3^{i-1}$$\end{document}$G = 2$μ∑$i = 15$iCiλm2i-23i-1. Consider a quasi-incompressible muscle subjected to a uniaxial that mimics the conditions of Cardoso [2012]18, the direction of loading \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{e}_n$$\end{document}en is one of the principal directions such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda _n =\lambda$$\end{document}λn=λ. From Eq. [ 4], it can be shown that under these conditions, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{nn}$$\end{document}σnn is the only Cauchy stress that does not vanish and its expression is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\displaystyle \sigma _{nn}= 2\mu (\lambda ^2-\lambda ^{-1}) \sum _{$i = 1$}^{5} \frac{i C_i}{\lambda _m^{2i-2}} \bar{I}_1^{i-1}$$\end{document}σnn=2μ(λ2-λ-1)∑$i = 15$iCiλm2i-2I¯1i-1.
## Calibration and validation of the model
From the digitally reconstructed model of the abdominal muscles (Fig. 1), a subdomain around the umbilical area has been isolated, subjected to symmetry boundary conditions, and exposed to an IAP of 0.8kPa. This specific pressure has been selected because it represents an average IAP for the healthy weight group. We used, a non-linear hyperelastic constitutive model to describe the behaviour of the muscles based on the Helmholtz energy potential of Arruda-Boyce (see Eqs. 1 and 2). The material properties used for simulation and obtained by calibrating the hyperelastic model experimentally are summarised in Table 1.Table 1Materials properties used for simulation. SpecimensShear parameter, G (MPa)Locking stretch, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda _m$$\end{document}λmCompressibility parameter, D (MPa\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-1}$$\end{document}-1)External oblique (EO)0.011.0192.3Internal oblique (IO)0.00650.85142.011Linea Alba (LA)0.020.446.15Rectus Abdominis (RA)0.00520.775177.5transversus abdominis (TA)0.01030.9898.61 To calibrate the constitutive model, its theoretical response was compared to the experimental data of Cardoso18 who used cadaveric tissues and assessed the mechanical behaviour of various muscles (external oblique, internal oblique, rectus abdominis, and transversus abdominis). It is known that cadaveric tissues behave differently from living tissues as the post mortem biochemical and environmental changes can stiffen the muscular tissues. However, the difference in mechanical behaviour can be negligible especially if the samples were refrigerated, preserved in a B. Braun medical solution, and tested within 24 hours from collection to avoid degradation.
To validate the constitutive behaviour discussed in the previous section, we developed a simple finite element model where we represented the tissues as incompressible cuboids of length \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0 =$$\end{document}L0= 10 mm having a square cross section of size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a_0$$\end{document}a0 = 1 mm. The base of each cuboid was constrained axially and allowed to roll otherwise. The top was subjected to a surface traction defined per unit of unreformed area (first Piola-Kirchhoff stress or nominal stress). We compared the results of the model with experimental data and found that both the theoretical and numerical models accurately predicted the uniaxial behaviour of the tissues, as shown in Fig. 3. This validation provides evidence that our model can be used to accurately simulate the behaviour of soft tissues. Figure 3Calibration and validation of the model using the uniaxial experimental data of Cardoso conducted on specimens from abdomen muscles18.
## Human and animal rights
No experiments were conducted on humans as part of this study.
## Results and discussion
The FEM has been applied to calculate the response in displacement of the AW with and without hernia damage. The comparison in Fig. 4 indicates that the displacement decreases by $3\%$ when hernia occurs in the AW. This decrease in displacement is explained by the absence of reaction to applied pressure where hernia has developed which leads to an overall lower cumulative force and thereby smaller displacement. This may also explain the protrusion of soft tissues through the hernia weakness as a reflection of pressure gradient. Figure 4Magnitude of displacement in the AW subdomain (a) without hernia, (b) with incisional hernia, and (c) with umbilical hernia.
Hernia repair often involves the placement of a mesh, which confers tensile strength to the abdominal wall by promoting the formation of scar tissue. There has been a proliferation of mesh types with differing mesh materials, mesh weights (dependent on the weight and amount of material used) and porosity of the meshes. Despite the abundance of clinical studies investigating hernia repair with various types of mesh, there are a number of questions that have not been answered including the role of mesh in hernia repair and the optimal placement of the mesh within the layers of the abdominal wall19. Figure 5 describes the mesh insertion for ventral hernia repair according to the international classification of abdominal wall planes20.Figure 5Mesh placements for ventral hernia repairs according to the international classification of abdominal wall planes20.
## Incisional hernia
In this study we focused on small post-surgical (i.e. incisional) or umbilical hernias that are both modelled as circular defects in the muscles of the AW. For incisional hernia, the damage zone is considered to traverse the consecutive muscles of the abdomen: EO, IO, RA and TA. Figure 6 depicts the distributions of stresses in the AW subdomain. These contours show that the horizontal stress \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{zz}$$\end{document}σzz is the most significant component near the plane of bilateral (or midline) symmetry. The stress component \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{yy}$$\end{document}σyy becomes more significant 90\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘ around the vertical axis away from the midline. The shear stresses are all relatively smaller. This stress distribution implies that the hoop stress dictates the most critical state in the AW. In terms of stress distributions in the various muscles, the linea alba experiences the highest stresses in the studied domain. This is because the linea alba is the stiffest tissue in the AW. Another highly solicited muscle according to this numerical analysis is the transversus abdominis, which is directly exposed to internal pressure. Some of this pressure is transmitted to the internal oblique and the rectus abdominis through contact forces but after significant attenuation, as shown in Fig. 8.Figure 6Contours of stress components for intact AW and hernia affected AW when no mesh is used to reinforce the damaged muscles. The sixth shear component stress tensor \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{xy}$$\end{document}σxy has been ignored because it is negligible.
The responses of an intact AW and a hernia affected AW with no reinforcing mesh were compared and the concentration of stresses around the hernia-damage zone and the amplification of stresses in this area were demonstrated, as shown in Fig. 6. The concentration and amplification of stresses prevail despite the smaller displacement that takes place when herniation occurs. Similarly, Fig. 7 depicts the magnitude of the equivalent stress around the hernia zone in rose diagrams (the origin of the polar coordinates corresponds to the centre of the hernia and the reference direction corresponds to the horizontal z-axis as shown in Fig. 4). The subfigures have two different scalings (Fig. 7a is from 0 to 30 kPa and Fig. 7a is from 0 to 20 kPa). The rose curves in Fig. 7a confirm that the highest stress is encountered in the transversus abdominis followed by the external oblique, while the stresses in the rectus abdominis and internal oblique are about five times smaller. This figure also indicates that the equivalent stress is anisotropic in the absence of reinforcement and that the TA experiences an equivalent stress of about 7.5 kPa in the horizontal polar direction and reaches 30kPa around the vertical polar direction. The stress concentration factor resulting from this stress anisotropy is 4 and it is attributed to the presence of hernia. The response in the presence of a surgical mesh implant used to reinforce the transversus abdominis is depicted by Fig. 7b. After hernia repair, no significant stress concentration can be perceived (the ratio of maximum over minimum stress is close to 1). In addition, stress amplification vanishes since the stress magnitude is about 5 kPa around the y-axis. Figure 7Distributions of equivalent stress (expressed in kPa) in the AW muscles affected by hernia (a) without reinforcement and (b) when a surgical mesh implant is used to reinforce the transversus abdominis. Hernia repair by mesh reinforcement reduces the magnitude of stresses and relieves their concentration.
Figure 8 shows the contours of equivalent stresses in the different AW muscles and in the absence of hernia (first row), in presence of hernia without repair (second row), and in the presence of hernia while surgical mesh implants are applied at the onlay plane (third row), anterectus plane (fourth row), retrorectus plane (fifth row), and preperitoneal plane (sixth row). The figure indicates that TA muscles are the most solicited in the AW irrespective of the presence or absence of reinforcement. This is because of their direct exposure to internal pressure. The stress distributions reveal that implanting a surgical mesh in the onlay, anterectus, or retrorectus planes does not improve the response of the AW to the applied internal pressure. However, the last row shows that reinforcing the TA muscle with a surgical mesh implant at the preperitoneal plane reduces the stresses significantly. Along with the results shown in Fig. 7, these contours indicate that for this hernia location, applying a preperitoneal mesh gives an optimum reinforcement and effectively reduces the stress concentration around the hernia damage zone. Figure 8Contours of equivalent stresses in the different AW muscles in the absence of hernia (first row), in the absence of reinforcement and presence of hernia (second row), in the presence of hernia while surgical mesh implants are applied at the onlay (third row), anterectus (fourth row), retrorectus (fifth row), or preperitoneal (sixth row) planes.
Examining the equivalent stress distributions in the surgical mesh implants applied at the onlay, anterectus, retrorectus, or preperitoneal planes (Fig. 9) shows that the maximum stresses occur in the TA mesh. This result is coherent with the conclusion drawn from Fig. 8. The high stress in the mesh suggests that it works effectively to protect its neighbouring muscles from stress concentration and amplification. Note that the maximum stresses obtained in this simulation are below the tensile strengths of commercially available composite and biological meshes reported by See et al.21.Figure 9Equivalent stress distribution in the surgical mesh implants applied at the onlay, anterectus, retrorectus, or preperitoneal planes.
## Paraumbilical hernia
Another significant case that is investigated in this study is paraumbilical hernia, which affects the linea alba (LA) near the region of the umbilicus. Deciding as to where mesh fabrics can be implanted is not well understood, thereby hernia repair can be in a sub-prime position if the mechanical response is unknown. Mesh placement in the retrorectus plane (Fig. 5) has become popular over the last years. This space lies between the rectus abdominis muscle and the posterior rectus sheath, which comprises the posterior leaf of the internal oblique aponeurosis and the transversus abdominis aponeurosis. In a large complex hernia repair, a posterior component separation procedure is sometimes needed22. In this manoeuvre, the posterior rectus sheath and transversus abdominis aponeurosis muscle are incised in order to reduce tension and the mesh is placed in the space between the rectus abdominis and internal and external oblique anteriorly and the posterior rectus sheath and peritoneal sac posteriorly.
The current numerical study sheds light on the optimum position through the abdominal tissues. Figure 10 depicts the contours of equivalent stress when no mesh is applied or when a mesh is applied at one of the inter-muscular interfaces. Although the stresses are high at the contact point between LA and the adjacent muscles, these singularities are not a source of concern as in practice contact areas are larger than the localised node-to-node interactions that concentrate stresses. The area of interest is at the periphery of the hernia and in its close vicinity. The contour plots indicate stress concentrations at the inner and outer surfaces of the LA muscles near the hernia; the magnitudes of the stresses are higher in the external areas. This result is confirmed by Fig. 11a,b indicating the stress distribution around the hernia damage zone at the external and internal faces.
Examining the effect of the mesh implant position on the stress distribution shows that the lowest stress distribution in the external ring is obtained for onlay mesh reinforcement (see Figs. 10 and 11a). Similarly, the lowest stress distribution in the internal ring is obtained when the reinforcement is placed in the preperitoneal plane (see Figs. 10 and 11b). The figures also show that the external stress decreases by about $50\%$ when onlay mesh is implanted. Applying the mesh implant at the retrorectus plane reduces the stress by about $50\%$ on average. Note that reinforcing the preperitoneal interface also reduces the stress in the internal zone but not as much as in the case of retrorectus interface reinforcement.
Other locations have practically no effect on the stress distribution, which means that they do not offer effective reinforcement. While the onlay and retrorectus planes are both suitable for mechanical reinforcement, it is more practical to place the mesh in the retrorectus plane to prevent the protrusion of soft tissues from the abdomen cavity. Clinical research proved that the retrorectus mesh placement is associated with reduced complication and recurrence rates23. For these reasons, placing the mesh implant at the retrorectus plane offers the best reinforcement in the case of umbilical hernia. Figure 10Contours of equivalent stresses in the linea albea affected by hernia when no mesh is applied (first row) and in the presence of a surgical mesh implants at the onlay plane (second row), anterectus plane (third row), retrorectus plane (fourth row), and at the preperitoneal plane (fifth row).Figure 11Distributions of equivalent stress (expressed in kPa) within the hernia damage zone around the (a) external and (b) internal interfaces of the linea albea with various mesh reinforcement positions.
## Hernia growth
To assess the criticality of hernia size, we use the principles of fracture mechanics. Hence, the hernia is treated as a local crack of length a within the muscles. For a very short crack length, failure occurs when the ultimate stress \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _u$$\end{document}σu is reached. However, for long cracks, failure occurs at a stress \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _f$$\end{document}σf that is determined by measuring the fracture toughness. In the absence of a proper measurement of fracture toughness on human muscles, we consider the laboratory measurements of Taylor et al.24 conducted on porcine muscles and indicating that the apparent fracture toughness is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J_c = 2.5$$\end{document}Jc=2.5 kJ/m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}2. Note that for linear materials \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_c = \sqrt{J_c E}$$\end{document}Kc=JcE is known as the critical stress intensity factor. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J_c$$\end{document}Jc can be used for linear and non-linear materials while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_c$$\end{document}*Kc is* meaningless when the behaviour is non-linear. Based on the definition of apparent fracture toughness, the critical hernia length is about5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} a_c = \frac{1}{\pi }\frac{J_c E}{\sigma _f^2} \end{aligned}$$\end{document}ac=1πJcEσf2As shown in the stress contours discussed above, the AW investigated in this study experiences a nominal stress of no more than 0.1 MPa. For failure to occur at this stress in the EO, IO, RA, or TA, stress failure should be considered \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _f = 0.1$$\end{document}σf=0.1 MPa. Table 2 shows the corresponding critical lengths if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J_c = 2.5$$\end{document}Jc=2.5 kJ/m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}2. Using Eq. [ 5], it can be seen that the most critical region is rectus abdominis, where cracks (or hernia damage zones) of critical size 4.1 cm or larger can propagate (i.e. grow). In the internal oblique, slightly larger cracks of sizes 5.2 cm can propagate. As for the external oblique and transversus abdominis, cracks of sizes 8 cm and 8.2 cm, respectively can propagate. The last column of Table 2 indicates the critical distance defined by6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} L = \frac{1}{\pi }\frac{J_c E}{\sigma _u^2} \end{aligned}$$\end{document}$L = 1$πJcEσu2This distance can be interpreted as the size of a damage zone (e.g. hernia) that cannot influence the overall failure of a muscle (can be left in place without risk of crack propagation and therefore will not gain any significant benefit from a surgical repair of the hernia). Applying Eq. [ 6] shows that this critical size varies from 1.5 mm in the transversus abdominis to 7.8 mm in the rectus abdominis. Table 2Critical dimensions of cracks (or hernia defects) in abdominal muscles. Specimen E (MPa) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _u$$\end{document}σu(MPa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a_c$$\end{document}ac (cm)L (mm)MeanStDevMeanStDevExternal oblique1.00.670.570.3282.4Internal oblique0.650.290.390.195.23.4Rectus abdominis0.520.260.230.144.17.8transversus abdominis1.030.750.730.588.21.5
## Conclusion
A finite element model was developed to investigate the mechanical behaviour of a typical AW subjected to local damage due to hernia. The proposed modelling approach took into consideration the large deformation of abdominal muscles by incorporating non-linear geometrical changes and selecting an Arruda-Boyce hyperelastic constitutive model. The resulting stress distributions, hypothetical geometry of hernia and theory of fracture mechanics were used to assess the growth of hernias.
The proposed analyses revealed optimum locations of mesh implants enabling the effective reinforcement of hernia-affected abdominal muscles – locations that were previously selected empirically. The numerical results indicated that the optimum placement of mesh implants for incisional hernia is on the transversus abdominis muscles. As for paraumbilical hernia, retrorectus reinforcement of the linea alba proved to be more effective than preperitoneal, anterectus, and onlay implantations. Furthermore, this study provided an objective predictor of critical hernia size beyond which surgical intervention becomes necessary. In summary, the proposed workflow combined biomechanics and clinical knowledge to predict the response of a typical digitally reconstructed abdominal wall to internal pressure using non-linear geometrical changes, non-linear interactions and non-linear material behaviour.
Despite its potential, the proposed workflow has limitations that are essentially attributed to the lack of experimental data. The mechanical properties of muscles may differ from one patient to another. In addition, the loading constraints (e.g. intra-abdominal pressure) may also vary significantly with the health conditions of the patient (e.g. obesity, pregnancy). A model that takes into account such uncertainty might be more appropriate.
Future challenges in this area would require reliable testing of live and natural muscular tissues to predict the hyperelastic properties with more certainty and the fracture toughness under relevant loading conditions. As such, digitally constructed biomechanical models that are patient-specific have the potential to provide direct guidance to practicing surgeons, which can be instrumental to reduce hernia recurrence. These personalised constructed models will also account for patients’ compounding factors such body habitus and previous surgery.
## References
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---
title: Time-of-day defines NAD+ efficacy to treat diet-induced metabolic disease by
synchronizing the hepatic clock in mice
authors:
- Quetzalcoatl Escalante-Covarrubias
- Lucía Mendoza-Viveros
- Mirna González-Suárez
- Román Sitten-Olea
- Laura A. Velázquez-Villegas
- Fernando Becerril-Pérez
- Ignacio Pacheco-Bernal
- Erick Carreño-Vázquez
- Paola Mass-Sánchez
- Marcia Bustamante-Zepeda
- Ricardo Orozco-Solís
- Lorena Aguilar-Arnal
journal: Nature Communications
year: 2023
pmcid: PMC10043291
doi: 10.1038/s41467-023-37286-2
license: CC BY 4.0
---
# Time-of-day defines NAD+ efficacy to treat diet-induced metabolic disease by synchronizing the hepatic clock in mice
## Abstract
The circadian clock is an endogenous time-tracking system that anticipates daily environmental changes. Misalignment of the clock can cause obesity, which is accompanied by reduced levels of the clock-controlled, rhythmic metabolite NAD+. Increasing NAD+ is becoming a therapy for metabolic dysfunction; however, the impact of daily NAD+ fluctuations remains unknown. Here, we demonstrate that time-of-day determines the efficacy of NAD+ treatment for diet-induced metabolic disease in mice. Increasing NAD+ prior to the active phase in obese male mice ameliorated metabolic markers including body weight, glucose and insulin tolerance, hepatic inflammation and nutrient sensing pathways. However, raising NAD+ immediately before the rest phase selectively compromised these responses. Remarkably, timed NAD+ adjusted circadian oscillations of the liver clock until completely inverting its oscillatory phase when increased just before the rest period, resulting in misaligned molecular and behavioral rhythms in male and female mice. Our findings unveil the time-of-day dependence of NAD+-based therapies and support a chronobiology-based approach.
The timing of NAD + supply determines its efficacy to treat metabolic disease. Here, the authors show that increasing NAD + at the early active phase maximizes weight loss and glucose regulation in mice. NAD + can displace the phase of the liver clock which can cause circadian misalignment.
## Introduction
In the last few decades, the prevalence of obesity has become epidemic through the world and is a major risk factor for type 2 diabetes (T2D)1. The main cause appears as combined inappropriate nutrition and sedentary lifestyles. Overweight, insulin resistance, β-cell dysfunction, increased circulating glucose and lipids and non-alcoholic fatty liver disease (NAFLD) characterize the pathophysiology of T2D2. Countless research efforts have explored pharmacological treatments for T2D and associated pathologies leading to promising compounds, which together with lifestyle interventions constitute first-line treatments3. During the last few years, the circadian system has been increasingly recognized as a key actor for development and treatment of diet-induced metabolic dysfunction. Yet, circadian rhythms in the clinical practice remain largely overlooked and time-of-day is hardly considered in treatment decisions4–8.
Circadian rhythms are evolutionary conserved 24-h cycles in physiology dictated by an intrinsic circadian clock. In mammals, the suprachiasmatic nucleus (SCN), a master timekeeper in the hypothalamus, receives photic cues from the retina to align internal and external time. The SCN distally synchronizes ancillary oscillators in peripheral tissues. Importantly, certain cues such as nutritional inputs effectively synchronize peripheral clocks9. Aligned synchrony between all body clocks maintains homeostasis and health, for example, by adjusting metabolic performance to daily environmental fluctuations. Conversely, persistent circadian misalignment is a cause of severe diseases, including obesity and metabolic syndrome, T2D, or cardiovascular disease, amongst others10–12. At the molecular level, the circadian machinery is expressed in almost all cell types and consists of transcriptional-translational autoregulatory feedback loops. The positive loop is driven by the CLOCK:BMAL1 transcriptional activator, which rhythmically binds to E-box genomic elements, thereby activating transcription of many genes including the circadian repressors, Period (Per1-3) and Cryptochrome (Cry1-2). PER:CRY complexes directly repress CLOCK:BMAL1 leading to transcriptional silencing. A number of interlocked regulatory loops, such as the one governed by RORs/REV-ERBα to regulate Bmal1 expression, intertwine to confer complexity, redundancy, and robustness to circadian rhythms13. Consequently, a set of clock-controlled genes (CCGs) ranging from 5–$25\%$ depending on the tissue or cell type, display transcriptional circadian rhythms14. Notably, rhythmic transcripts are functionally related, including rate-limiting enzymes, hence providing means to adjust the pace of many metabolic pathways around the day and driving rhythms in the tissue metabolome15–18. A paradigmatic example is illustrated by daily rhythms in nicotinamide adenine dinucleotide (NAD+) bioavailability, imposed by circadian oscillations in the clock-controlled gene Nampt, the rate-limiting enzyme for the NAM salvage pathway to NAD+19,20. Several lines of evidence demonstrate that the molecular clock and NAD+ oscillations sustain mitochondrial function and bioenergetics, manifested in daily rhythms in respiration, fatty acid oxidation, or nutrient utilization21–25. Indeed, it is considered that clock-controlled NAD+ biosynthesis occupies a fundamental position connecting circadian metabolic pathways26–28.
NAD+ and its phosphorylated and reduced forms NADH, NADP+, and NADPH, are coenzymes for hydride transfer enzymes, crucial to biological redox reactions. NAD+/NADH ratio is a basic determinant of the rate of catabolism and energy production29,30. In fed state or nutrient overload NAD+/NADH ratio falls, and a prolonged redox imbalance potentially leads to metabolic pathologies, such as diabetes31. Along these lines, extensive research demonstrates that NAD+ levels significantly decline in metabolic tissues of mice and patients with obesity32–37. NAD+ decay itself may contribute to metabolic dysfunction by distinct mechanisms, including increased oxidative stress and ROS production, disbalance in the oxidative-reductive capacity, disrupted Ca2+ homeostasis, or reduced activity of sirtuins38,39; a class of deacetylase enzymes using NAD+ as cofactor and known to influence mitochondrial function and metabolism. In recent years, NAD+ has emerged as a target for the treatment of metabolic diseases, as boosting endogenous NAD+ levels has been proven effective against diet-induced metabolic pathologies, including insulin resistance, hyperglycemia, hypertriglyceridemia and NAFLD32,33,35,36,40–45. All these studies aim to increase NAD+ levels either genetically or pharmacologically, yet they mostly overlook the circadian trait of NAD+ bioavailability. Consequently, the implications of circadian rhythms in the function and effectiveness of NAD+ boosters as a therapy for diet-induced metabolic dysfunction remain largely obscure.
In this work, we aimed to characterize the metabolic consequences of rhythms in NAD+ levels. To approach this question, we used a mouse model of diet-induced obesity (DIO), which is known to present decreased, non-rhythmic levels of NAD+15–17, and pharmacologically recovered daily rhythms of NAD+ with a peak at the onset of the active phase. To do so, we used a daily timed intraperitoneal (IP) injection with NAD+ itself at ZT11. We show that obese mice with enforced NAD+ oscillations improved metabolic health, significantly lost weight, and corrected NAFLD. Our analyses revealed that hepatic transcriptional signatures of inflammation disappeared in these mice. Indeed, hepatic signaling involving AMPK, AKT, mTOR was rewired after restoring rhythmic NAD+ in obese mice, providing increased insulin sensitivity during the active period. Together, we demonstrated that a single daily injection with NAD+ treats the pathophysiology of diet-induced obesity, with comparable efficiency to NAD+ precursors. Remarkably, these metabolic and molecular improvements were not recapitulated by obese mice with antiphase increase of NAD+, at the onset of the rest phase, which showed only selective recovery of metabolic health. Further analyses demonstrated that lipid oxidative pathways and the molecular clock are central mediators for phase-dependent, differential effects of NAD+. Particularly, NAD+ provided at the onset of the rest phase uncoupled oscillations between central and peripheral clocks, by means of inverting the phase of the hepatic clock while food intake and activity remained rhythmic. Collectively, our findings reveal that timed NAD+ supply can shape the oscillatory phase of the hepatic molecular clock in vivo and expose a previously unappreciated time-dependent effect of NAD+ as a treatment for metabolic dysfunction, paving the way for chronotherapy and personalized medicine.
## A timed treatment with NAD+ reverses the metabolic phenotype of diet-induced obesity
To understand whether daily NAD+ administration improves metabolic fitness in obesity, we used a mouse model of diet-induced obesity (DIO) where instead of increasing NAD+ by chronic supplementation with metabolic precursors, we directly supplied the metabolite itself in a daily single IP injection scheduled at ZT11, corresponding to an hour before the normal circadian rise of hepatic NAD+16,17,21,27,46. Hence, after 8 weeks of high-fat diet (HFD) feeding, mice were treated for 22 days with saline solution (HF group) or 50 mg/Kg of NAD+ (HFN group, Supplementary Fig. S1a, see Methods section), at ZT11 (Fig. 1a). Mice fed a chow diet were included as a control (CD group).Fig. 1A NAD+ chronotherapy at ZT11 improves the pathophysiology of diet-induced obesity.a Schematic diagram of the study design. Mice were fed either a normocaloric diet (CD) or a high-fat diet for 11 weeks. At week 8, a subgroup of high-fat-fed mice was supplied a chronotherapy with NAD+, consisting of a daily intraperitoneal injection of 50 mg/Kg of NAD+ at ZT11 for three weeks (HFN). The rest of the mice were injected with vehicle (saline solution). b Weekly body weight ($$n = 20$$ mice for CD and HFN and 17 for HF). Red arrow indicates the period of treatment with NAD+ or saline at ZT11. ( c) Hepatic NAD+ content measured by HPLC along the day at the indicated times for all groups after the experimental paradigm ($$n = 5$$ biological replicates per time point and group, and three technical replicates). d Serum levels of insulin along the day at indicated times ($$n = 5$$ biological replicates per time point, and 2 technical replicates). AUC: area under the curve. e–h Glucose (e, f GTT) and insulin (g,h; ITT) tolerance tests were performed at both the rest (ZT4) and the active (ZT16) period at the indicated days (10 and 20) after the beginning of treatments ($$n = 5$$ mice per point, except for HFN in f, where $$n = 6$$). AUC: area under the curve. CD control diet fed mice, HF high-fat diet fed mice, HFN high-fat diet fed, NAD+ treated mice at ZT11. Data represent mean ± SEM and were analyzed by two-way ANOVA using Tukey posttest, except when comparing AUC, where one-way ANOVA followed by Tukey’s posttest was used. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ p values are provided in Supplementary Data 1. Points at ZT24 are duplicates of ZT0 replotted to show 24-h trends. Symbol key for comparisons: *CD vs HF; +CD vs HFN; #HF vs HFN. See also Supplementary Fig. S1.
At week 8 on HFD, mice displayed expected increase in body weight which was accompanied by significantly higher caloric intake during both light and dark periods47 (Fig. 1b, Supplementary Fig. S1b–d). Notably, after 14 days of NAD+ chronotherapy, a significant decrease in total body weight was observed in obese treated mice (HFN) with respect to their obese non-treated littermates (HF), which was sustained after 22 days (Fig. 1b; $P \leq 0.05$, Two-way ANOVA, Tukey post-test). At the end of the treatment, hepatic NAD+ content was measured by HPLC, showing the expected oscillation with a peak at ~ZT12 in control mice (CD, Fig. 1c) which is mostly disrupted in HFD-fed mice (HF, Fig. 1c, Supplementary Fig. S1e)16,21,36,46. Importantly, in the HFN group, the acrophase of NAD+ was restored to ZT12 (HFN, Fig. 1c, Supplementary Fig. S1e), hence daily rhythms in hepatic NAD+ content was reinstated in obese mice (Supplementary Fig. S1d; $P \leq 0.001$, F test performed with CircWave).
We sought to assess physiological indicators of metabolic health and found that circulating insulin levels were much lower in the HFN group when compared to the HF group, with a major effect during the early active phase (Fig. 1d, ZT12-18, $P \leq 0.001$ Two-way ANOVA, Tukey post-test) and a six-hour phase delay in the oscillatory pattern (Supplementary Fig. S1f). Indeed, circulating insulin in HFN mice appeared largely comparable to their control littermates. Overall, we didn´t find major differences in body temperature between treated and untreated obese mice, suggesting that circadian-controlled thermogenic processes48 are probably not involved in the metabolic benefits observed upon restoring NAD+ oscillations (Supplementary Fig. S1g–j).
It has been extensively demonstrated that glucose tolerance and insulin sensitivity follow daily rhythms imposed by the circadian system49, hence we evaluated them at two different time points, ZT4 and ZT16. As expected, before NAD+ treatment, HFD-fed mice showed impaired glucose tolerance at both ZT (Supplementary Fig. S1k, l). Remarkably, after 10 days, restoring NAD+ oscillations in obese mice significantly ameliorated glucose tolerance, specifically at ZT16 (Fig. 1e, f, AUC HF vs HFN at day 10: $P \leq 0.001$, one-way ANOVA, Tukey post-test). After 20 days of treatment, this improvement was also apparent at ZT4 (Fig. 1e, AUC HF vs HFN at day 20: $$P \leq 0.0084$$, one-way ANOVA, Tukey post-test). As both insulin and glucose levels were lower in NAD+-treated mice, insulin sensitization might occur. Accordingly, glucose clearance upon insulin IP injection was largely enhanced by NAD+ chronotherapy (Fig. 1g, h, Supplementary Fig. S1m, n). Notably, this effect was already evident in the HFN group after 10 days of treatment independently of the time when measurements were performed (Fig. 1g, h, AUC HF vs HFN at day 10: $P \leq 0.005$, one-way ANOVA, Tukey post-test). Interestingly, NAD+ treatment at ZT11 promoted a slight, albeit not significant, improvement in insulin tolerance with respect to control lean mice when tested at ZT16 (Fig. 1h). These results demonstrate that a chronotherapy with NAD+ injected just before starting the active phase improves glucose tolerance by increasing insulin sensitivity in DIO mice. Collectively, the restitution of NAD+ bioavailability at ZT12 recovers its basal hepatic oscillation and reverses the metabolic syndrome associated with diet-induced obesity.
Histological staining with Oil-Red-O (ORO) was used to semi-quantitatively assess hepatic steatosis (Fig. 2a), revealing that obese mice treated with NAD+ significantly decreased hepatic neutral lipid content (Fig. 2b, c, One-way ANOVA, Tukey’s posttest). Furthermore, a quantitative assay specific for hepatic triglycerides, the major form of fatty acids storage, revealed that these were globally reduced in obese mice after restoring hepatic NAD+ oscillations (Fig. 2d, Two-way ANOVA, Tukey’s posttest). Importantly, the NAD+ treatment recovered their oscillatory pattern which is generally disrupted in obese mice16 (Fig. 2d, Supplementary Fig. S2). Additionally, this timed NAD+ therapy reduced the accumulation of carbonylated proteins in liver lysates to normal levels (Fig. 2e) and augmented mitochondrial biogenesis (Fig. 2f). Together, these results indicated that increasing hepatic NAD+ levels at ~ZT12 recovers glucose homeostasis and successfully restrains liver pathology and oxidative stress of HFD-fed mice. Fig. 2NAD+ chronotherapy ameliorates NAFLD.a Representative hepatic histopathology. Upper panel: Oil-red-O stain (ORO). Lower panel: hematoxilin/eosin. Images were acquired at ×20 optical magnification, and a detailed ×100 digital magnification is shown. Data were reproduced in three biological and three technical replicates. b Quantification of ORO signal (arbitrary units). Signal for control mice was set to 1 ($$n = 3$$ biological and three technical replicates). c The length of lipid droplets was compared between groups ($$n = 3$$ biological and three technical replicates). d Distribution of hepatic triglyceride content across the day (left), and comparisons from all measurements (right) ($$n = 5$$ mice per time-point, with 2 technical replicates). e Protein carbonyl levels (PCO) in liver lysates were measured at the indicated times of day ($$n = 5$$ mice per time point, two technical replicates). AUC area under the curve. f Relative mtDNA copies of mtCO1 respect to 18 S DNA measured by Real-time PCR ($$n = 5$$ biological and two technical replicates). Inset area under the curve, content from CD group was set to 1. g Western blot from PPARγ1, 2, and CEBPα proteins in the mouse liver at the indicated times of day (ZT). GAPDH was used as loading control (WB was performed from 3 biological replicates with comparable results). Uncropped blots in Source Data. CD Control diet fed mice, HF High-fat diet fed mice, HFN High-fat diet fed, NAD+ treated mice at ZT11. Data represent mean ± SEM and were analyzed by two-way ANOVA using Tukey posttest, except for bar graphs, where one-way ANOVA followed by Tukey’s posttest was used. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ Exact p values are provided in Supplementary Data 1. Symbol key for comparisons: * CD vs HF; + CD vs HFN; # HF vs HFN. See also Supplementary Fig. S2.
At the molecular level, the master regulator of lipid metabolism PPARγ protein50 was overexpressed across the day in the livers from HFD-fed mice, while those treated with NAD+ showed markedly reduced PPARγ levels (Fig. 2g). A similar trend was evidenced for the transcription factor CEBPα, a known positive regulator of Pparγ gene expression and adipogenesis51,52 (Fig. 2g), further reinforcing the notion that a gene expression program involving lipid metabolism might be modified in NAD+ treated mice.
## Extensive transcriptional reorganization driven by timed NAD+ treatment
To address the extent of the transcriptional rewiring in the liver of NAD+-treated obese mice, we performed a transcriptomic analysis at light (ZT6) and dark (ZT18) phases in mouse livers from CD, HF, and HFN groups. 76 common genes were differentially expressed (DE) between day and night in all groups (Fig. 3a, Supplementary Data 2), with comparable expression levels. Amongst these, a number of transcripts related to circadian control were apparent, including Clock, Arntl (Bmal1), Cry1, Nr1d2 (Rev-Erbβ), Rorc, Tef, Nfil3 or Ciart (Fig. 3a, Supplementary Data 2), suggesting that circadian rhythms were mostly preserved by the NAD+ treatment at ZT11. Accordingly, rhythms in the core clock proteins BMAL1, CRY1, PER2, and REV-ERBα were overall sustained in the HFN group (Fig. 3b, c). Interestingly, a significant reduction in CRY1 protein levels at ZT12 was observed in the HFN group compared to the HF (Fig. 3b, c).Fig. 3Timed NAD+ supply induce a reprogramming of hepatic transcripts DE between day and night without altering the dynamics of clock proteins.a Heatmaps of 76 common differentially expressed (DE) transcripts between day (ZT6) and night (ZT18) in all groups. b Circadian protein expression of BMAL1, REV-ERBα, CRY1, and PER2 in the whole cell extracts from CD, HF, and HFN livers was determined by western blot. Tubulin or GAPDH were used as a loading control. Data were reproduced in three independent experiments. c Quantification of western blots from $$n = 3$$ mice except for BMAL1, where $$n = 4$$ mice. Measurements were normalized to the loading control, and data from CD at ZT0 was set to 1. Means ± SEM are presented. *** $p \leq 0.001$, Two-way ANOVA; n.s. non significative. Points at ZT24 are duplicates of ZT0 replotted to show 24-h trends. Statistical details and p values are provided in Supplementary Data 1. d Overlap of DE transcripts between day (ZT6) and night (ZT18) in all groups (FDR < 0.05; fold-change >1.3). e Heatmaps of distinct groups of DE transcripts between day (ZT6) and night (ZT18). Left: 322 transcripts DE exclusively in CD; center: 1327 DE exclusively in HF; right: 306 transcripts DE exclusively in HFN. f Shared biological processes for DE transcripts between day and night from all groups. g Non-shared biological process for DE transcripts between day and night. f, g The Adj. P value corresponds to the FDR q-value. This is the false discovery rate analog of hypergeometric p value after correction for multiple hypothesis testing according to Benjamini and Hochberg. CD control diet-fed mice, HF High-fat diet-fed mice, HFN High-fat diet fed mice, NAD+ treated at ZT11.
An extensive circadian transcriptional reprogramming is induced by high-fat diet in the liver16,53, hereafter we identified 524 day-to-night DE transcripts in CD mice, 1684 in HF mice and 599 in the HFN mice (Fig. 3d, >1.25 fold-change, $P \leq 0.05$). Out of these, 322 transcripts were exclusively fluctuating in the CD group, 1327 fluctuated solely in the HF, interestingly, 306 newly fluctuating transcripts appeared in the HFN group (Fig. 3d, e, Supplementary Data 2). Functional analyses revealed that indeed, many of these DE genes participated in shared biological processes including transport, metabolic processes and response to oxygen (Fig. 3f, Supplementary Data 2). As expected, day-to-night transitions in gene expression were more evident for genes implicated in lipid metabolism in HFD-fed mice independently of NAD+ treatment (Fig. 3g, Supplementary Data 2). Remarkably, a set of genes functionally related to immune system processes appeared significantly enriched solely in the HF mice (Fig. 3g, Supplementary Data 2). Interestingly, timed NAD+ supply imposed new and specific day-to-night transcriptional fluctuations in genes functionally related to response to stress and starvation (Fig. 3g, Supplementary Data 2). Hence, we reasoned that a time-of-day specific transcriptional response to NAD+ might be responsible for the beneficial effects of rhythmic restitution of this metabolite.
To further dissect the expression program imposed by NAD+, we identified DE genes between groups, examined specifically at day (ZT6) or night (ZT18). At ZT6, 724 hepatic transcripts were significantly DE between CD and HF mice, while 936 transcripts varied when comparing HF and HFN groups, with 182 ($12\%$) overlapping transcripts (Fig. 4a, Supplementary Data 3). At ZT18, 1731 genes were DE in livers from CD and HF mice, and 698 were DE between HF and HFN mice, appearing 118 ($5\%$) common transcripts (Fig. 4a, Supplementary Data 3). Interestingly, most of these DE shared transcripts recovered their expression in the obese NAD+ treated (HFN) mice to control conditions (Fig. 4b). Common DE genes between CD-HF and HF-HFN comparisons at ZT6 were specifically enriched for biological processes related to regulation of the immune response, including both innate and adaptive immune system pathways (Fig. 4c, Supplementary Data 3). Furthermore, a direct assessment for distinctive gene sets between HF and HFN groups at ZT6 using GSEA54 (Gene Set Enrichment Analysis) showed that IL6-JAK-STAT3 and TGFβ signaling were the highest enriched hallmarks (Supplementary Fig. S3a). Indeed, timed NAD+ treatment in obese mice suppressed the hepatic expression of inflammatory markers including Stat3, Stat6, Tgfb1, Il1r1, Il6st, Tnfrsf1a, Tnfrsf1b, Smad3 or Smad6 (Supplementary Fig. S3b). This supports the notion that timed NAD+ treatment just before the onset of the active phase in obese mice abolishes the inflammatory environment associated with insulin resistance in NAFLD55,56 specifically during their resting period. Accordingly, at ZT18, genes recovered to normal conditions by NAD+ appeared mostly enriched for Lipid Metabolic Processes, particularly fatty acid biosynthesis and storage (Plin2, Abhd5, Acsm1, Hsd17b12, Chka) (Fig. 4d, Supplementary Data 3). Furthermore, a GSEA comparing HF and HFN groups at ZT18 revealed highest enrichment in the hallmarks Cholesterol Homeostasis and MTORC1 Signaling (Supplementary Fig. S3c), with significant downregulation of transcripts coding for major regulatory proteins and rate-limiting enzymes triggered by de novo NAD+ oscillations (Supplementary Fig. S3d; Cholesterol Homeostasis: Hmgcr, Hmgcs1, Sqle, Acss2, Lss or Stard4; MTORC1 signaling: Acaca, Acly, Me1, Adipor2, Psma3, Psma4, Psmd14 or Psmc6). As shown, these hepatic expression changes at ZT18 were accompanied by improvement of hyperlipidemia and fatty liver traits after restoring NAD+ oscillations in obese mice (Fig. 2a–d). Pathway analyses revealed transcriptional mechanisms restituted by NAD+, with significant enrichment of NFkB, HIF1 and HNF3 transcriptional networks (Fig. 4d); and de novo motif discovery in the promoters of genes whose expression appeared dysregulated only in the HF group identified strong similarities to NFkB-p65/RELA and FOXA1/FOXA2 (HNFα/HNF3β) binding sites (Fig. 4e). Accordingly, out of 83 transcripts recovered by NAD+ at ZT18, 11 ($13\%$) are previously described direct targets of FOXA257 (Supplementary Fig. S3e); interestingly, FOXA2 is a key regulator of lipid metabolism which becomes dysregulated in diabetic, insulin resistant mice57,58. Together, these data indicate that the inflammatory transcriptional signature related to NAFLD is abolished after timed NAD+ treatment, possibly through coordinating the action of transcription factors such as NFkB or FOXA2, and intracellular signaling involving the MTORC1 pathway. Fig. 4A NAD+ chronotherapy at ZT11 corrects abnormal gene and protein expression from crucial molecular effectors of liver disease and triggers a specific transcriptional signature.a, b Overlap (a) and heatmap (b) of DE genes when comparing CD-HF and HF-HFN groups either at day (ZT6) or ant night (ZT18) ($$n = 3$$). c, d Functional annotation of CD-HF and HF-HFN shared genes at daytime (c) or nighttime (d). e Homer de novo motif discovery analyses from promoters of genes DE exclusively in the HF group. f, g Circadian protein expression of AKT, p-AKT(S473), AMPK, and p-AMPK(T172) (F) or the mTOR pathway (g) in whole cell extracts from CD, HF, and HFN livers determined by western blot. Tubulin or p84 were used as a loading control. Images represent 3–4 independent experiments. Uncropped blots in Source Data. h, i Overlap (h) and heatmaps (i) of DE genes when comparing CD-HFN and HF-HFN groups either at day (ZT6) or at night (ZT18) ($$n = 3$$). j Functional annotation of shared genes DE in analyses CD-HFN and HF-HFN at nighttime. k Homer de novo motif discovery analyses from promoters of genes whose expression is altered exclusively in the HFN group. Adj. P value corresponds to the FDR q-value. This is the false discovery rate analog of hypergeometric p value after correction for multiple hypothesis testing according to Benjamini and Hochberg. CD control diet fed mice, HF high-fat diet fed mice, HFN High-fat diet fed, NAD+ treated mice at ZT11. See also Supplementary Figs. S3, S4 and Supplementary Data 4.
## Insulin signaling and rhythmicity in nutrient sensing pathways are rescued by NAD+ oscillations in obese mice
Transcriptional networks uncovered in these analyses together with measurements of metabolic parameters are strongly suggestive of restored insulin sensitivity and nutrient-sensing molecular pathways after reestablishing NAD+ oscillations in obese mice. To confirm this at the molecular level, we first evaluated phosphorylation of AKT1, a key kinase effector of insulin signaling59, along the day. As previously described, AKT1 phosphorylation at Ser 474 (p-AKT(S473)) appeared cyclic in CD fed mice60, with a peak at ZT18 (Fig. 4f, Supplementary Fig. S4a), coincident with highest food intake (Fig. S1C). In contrast, in HFD-fed mice, p-AKT(S473) was constitutively low, suggestive of insulin resistance in the liver of obese mice (Figs. 4F, S4A). Remarkably, we found restoring hepatic NAD+ oscillations in obese mice specifically increased p-AKT(S473) at ZT12 (Fig. 4f, Supplementary Fig. S4a. $P \leq 0.0001$; two-way ANOVA, Tukey post-test), hence imposing daily oscillations to insulin signaling. Furthermore, diurnal rhythms in AMPK phosphorylation at T172 were also restored by NAD+ treatment in obese mice, although with a unique peak at ZT12, which was six hours phase delayed compared to their control, lean littermates (Fig. 4f, Supplementary Fig. S4b). This is in keeping with our previous observation of a reduction of CRY1 protein in the HFN group specifically at ZT12 (Fig. 3b, c), as AMPK rhythmically phosphorylates and destabilizes CRY161. Concomitantly, the AMPK substrate ULK62 appeared hyperphosphorylated in the livers of NAD+ treated mice at ZT12 (Supplementary Fig. S4c). Following the lead from our transcriptomic analyses, we also explored mTORC1 function. mTOR S2448 phosphorylation and activity appear rhythmic in the mouse liver, coordinating a number of functions around the day, including ribosome biogenesis63,64 (Fig. 4g). High-fat feeding constitutively induced mTOR phosphorylation, and timed NAD+ treatment in obese mice downregulated it (Figs. 4G, S4D). We investigated the phosphorylation of p70-S6K (S6K) as a readout of the activity of mTORC165, and found that the diurnal profile of activation of S6K-Thr389 phosphorylation was completely restored by NAD+ chronotherapy in the livers of HFD-fed mice (Fig. 4g, Supplementary Fig. S4e). Additional mTOR downstream signaling revealed by phosphorylation of 4EBP1(Thr$\frac{37}{46}$) was reduced in obese, NAD+ treated mice (Fig. 4g, Supplementary Fig. S4f). We also observed that the mTORC1 agonist p90-S6K (RSK)65,66 and its activity as monitored by its phosphorylation in the Thr359 were significantly downregulated in the HFN group respect to the HF (Fig. 4g, Supplementary Fig. S4g). These results reinforce our pathway and gene set enrichment analyses comparing HF and HFN groups, consistent with reduced function of mTORC1 pathway after recovering NAD+ oscillations in obese mice.
## A unique NAD+ transcriptional signature identifies new pathways linked to metabolic improvement
We sought to explore the transcriptional signature induced by oscillatory NAD+, by identifying DE genes specifically in the HFN group. We found just 74 genes changing their expression at ZT6, and 196 at ZT18 (Fig. 4h, i, Supplementary Data 4). Functional analyses did not retrieve any significant enrichment for these genes at ZT6; however, it became very evident that at ZT18, a large part of the DE genes after NAD+ treatment were overexpressed and functionally involved in intracellular vesicle transport and catabolic processes (Fig. 4i, j, Supplementary Data 4). Indeed, five members of the Rab family of small GTPases, know regulators of membrane trafficking67, were specifically overexpressed after NAD+ treatment, including Rab1b, Rab7a, Rab10 which are largely involved in mediating autophagy68–71, and Rab6a, Rab8a, which also mediate receptor trafficking in response to insulin signaling72,73. Additional overexpressed genes by NAD+ known to regulate autophagy were Psen174, Vps2875.
A search for de novo motif enrichment within the promoters of NAD+-induced genes yielded matrices with high similarity to the binding sites for NR2E1 (TLX) and HNF4α TFs, both implicated in maintaining lipid homeostasis in the liver76,77. Also, a motif recognized by IRF3 and NR4A1 (Nur77) appeared significantly enriched ($$P \leq 1$$e-5), and interestingly, Nur77 has been shown to regulate the cytoplasmic shuttling of LKB1, hereby phosphorylating and activating AMPK78. Together, these data indicate that oscillatory NAD+ in obese mice activates a gene expression program favoring processes highly demandant for vesicle trafficking, such as translocation of membrane receptors or autophagy, and reinforce the idea of pharmacological supply of NAD+ preferably targeting activation of AMPK even in the context of high caloric feeding.
## Time-of-day determines the efficacy of NAD+ as a treatment for diet-induced metabolic dysfunction
To investigate if the beneficial effects of pharmacological restitution of NAD+ oscillations depend on the time of the day, we supplied NAD+ in opposite phase to its natural rhythmicity, hereby at the end of the active phase in mice, ZT23 (HFN23 group). In these HFN23 mice, oscillations of hepatic NAD+ were induced with antiphase respect to CD and HFN mice, showing a peak at ZT0 and decreasing at ZT12-18 (Supplementary Fig. S5a, b). As shown in mice treated at ZT11 (HFN), these also showed mild, albeit non-significant, weight loss after one week of treatment (Fig. 5a, week 9). Contrary to the HFN group, mice supplied at ZT23 gained weight during weeks 10 and 11 (Fig. 5a). Instead, after three weeks of treatment, mice treated with NAD+ at ZT11 had lost ~$5\%$ of body weight, while those treated at ZT23 were ~$2\%$ heavier (Fig. 5b), illustrating significant differences on the efficacy of the treatment depending on the time of administration. Notably, total food intake was comparable for all high-fat-fed mice, and before and after the treatment no significant differences were found (Fig. 5c, Supplementary Fig. S5c, Two-way ANOVA with post-test). Serum insulin was significantly higher in mice injected at ZT23 particularly during the dark phase (Fig. 5d, Supplementary Fig. S5d), indicating insulin resistance in these mice, although NAD+ therapy was effective to reduce fasting serum glucose independently of the time of supply (Supplementary Fig. S5e). These results indicate that in obese mice treated with NAD+ at ZT23, insulin clearance or the feedback inhibition of insulin secretion are impaired, which is a sign of persistent metabolic dysfunction in these mice79. Along these lines, we performed GTT and ITT at ZT4, because the effects of NAD+ supply at ZT11 tended to be more pronounced during the light phase (Fig. 1e–h). We found that at the end of the treatment with NAD+ at ZT23 (day 20, HFN23), glucose and insulin tolerance showed non-significant improvement compared to the HF-fed mice. Actually, the NAD+ treatment at ZT11 was significantly more favorable to improving glucose homeostasis than at ZT23 (Fig. 5e, f, Supplementary Fig. S5f, g; One-way ANOVA followed by Tukey’s posttest). Quantification of the relative improvement to the obese non-treated mice showed that after 10 days of treatment, NAD+ was effective to improve GTT and ITT only when supplied at ZT11, but not at ZT23. At the end of the treatment (day 20), NAD+ supply at ZT11 showed still a significantly better performance than at ZT23 (Fig. 5g, Two-way ANOVA followed by Tukey’s posttest).Fig. 5Time-of-day dependent response to NAD+ treatment in obese mice determines the efficacy of the chronotherapy to improve glucose homeostasis.a Weekly body weight ($$n = 15$$). Red arrow indicates the period of NAD+ or saline treatment at ZT23. b Percent change in body weight between weeks 8–11 ($$n = 24$$ CD, 23 HF, 19 HFN HFN23). c Weekly food intake three weeks before and after treatment ($$n = 33$$ mice). d Serum insulin ($$n = 9$$ mice per group, 7 for HFN23). e, f Glucose (GTT) and insulin (ITT) tolerance tests before and after (days 0–20) NAD+ treatment $$n = 8$$–16, see Supplementary Data 1 for exact n g *Relative delta* for the AUC from GTT and ITT respect to HF group. ( $$n = 5$$). h *Daily serum* triglycerides ($$n = 10$$ mice per time point, 9 for HFN23) (left), and comparisons from all measurements (right). i Representative hepatic histopathology. Upper: Oil-red-O stain. Lower: Hematoxilin/Eosin. Images acquired at ×20 optical magnification, detailed ×100 digital magnification is shown j ORO signal in arbitrary units. CD data were set to 1 k Length of lipid droplets ($$n = 3$$ biological, three technical replicates in j and k) l Relative pro-inflammatory cytokines in livers from 3 mice at ZT6 (2 CD mice were assayed for basal references). m RT-qPCR determined hepatic mRNA. $$n = 5$$ CD, HF; 6 HFN, HFN23, at ZT6. n Western blots from mouse liver o Quantification of WB from $$n = 4$$ HFN, 5 HFN23. Measurements were normalized to GAPDH, and data from CD at ZT0 was set to 1. Two-way ANOVA with Bonferroni post-test. CD control diet, HF High-fat diet, HFN High-fat diet, NAD+ treated at ZT11; HFN23: High-fat diet, NAD+ treated at ZT23 mice. For circadian plots, points at ZT24 are same as ZT0, replotted to show 24-h trends Data represent mean ± SE analyzed by one-way or two-way ANOVA using Tukey posttest. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$; ns: non-significant. Exact p values are provided in Supplementary Data 1. * CD vs HF; °CD vs HFN23; XHF vs HFN23. See also complementary Fig. S5.
Circulating triglycerides, largely known to be reduced by the NAD+ precursor niacin80,81, were decreased along the day to normal levels by NAD+ only when the treatment was performed at ZT11, but not at ZT23 (Fig. 5h). Interestingly, serum triglycerides were rhythmic for all groups; yet specific to the HFN23 group was that highest levels appeared at daytime, thus presenting antiphase daily oscillations (Fig. 5h). We found a very significant reduction in serum triglycerides only when NAD+ was injected at ZT11, while injection at ZT23 kept serum triglyceride levels significantly higher than injection at ZT11 (Fig. 5h, $P \leq 0.05$; One-way ANOVA with Tukey’s post-test). Opposite, hepatic steatosis was reduced to a similar extent in HFN and HFN23 groups (Fig. 5i–k, Supplementary Fig. S5g–i), while inflammatory cytokines known to be increased in the liver from HFD-fed mice82,83, appeared overall reduced in obese mice treated with NAD+, accompanied by decreased expression of pro-inflammatory cytokine genes (Tgfb1, Il6, Il1a, Il1b, Ifng), the macrophage recruiter gene Csf2, (also known as GM-CSF) and macrophage markers (Cd11b, Cd11c) (Fig. 5l–m), evidencing that NAD+ ameliorates these obesity-associated physiological parameters independently of the time of treatment. However, we observed opposite daily dynamics in hepatic PPARγ protein levels and its transcriptional activator CEBPα (Fig. 5n–o), which together with the serum triglycerides analyses, suggest that lipid metabolism might be distinct between HFN and HFN23 groups.
## NAD+ chronotherapy at ZT11 effectively coordinates hepatic intracellular signaling and gene expression driving lipid oxidation
To further disentangle the molecular pathways responsible for the physiological differences in glucose and insulin tolerance, and circulating triglycerides, between HFN and HFN23 groups of obese mice, we compared nutrient-sensing signaling in the liver from these mice. Western blot experiments showed that providing NAD+ at ZT23 to obese mice did not recapitulate hepatic AKT phosphorylation and activity, as did at ZT11 (Fig. 6a–b), hereby confirming that insulin signaling remains defective in mice treated at ZT23, as suggested by the ITT (Fig. 5f, Supplementary Fig. S5b). Additionally, the response to starvation signaling converging into AMPK-T172 phosphorylation and subsequent activation triggered at ZT12 after reinstating NAD+ oscillations was not induced in the livers of the HFN23 group (Fig. 6a–s). Furthermore, nutrient sensing by mTOR pathway appeared active through the day in livers from HFN23 group, as shown by persistent phosphorylation of p70-S6K-T389 (Fig. 6c–d), and contrasting with the rhythmic pattern observed in the HFN11 group. Moreover, RSK-T359 appeared hyperphosphorylated in the HFN23 group, also showing antiphase dynamics compared with the HFN group (Fig. 6c–d). These data clearly show that increased NAD+ levels at the end of the activity period are less efficient in synchronizing mTOR signaling pathway than high NAD+ at the onset of activity, and reinforce the notion of a chronotherapeutic approach as the best therapy for the treatment of metabolic diseases by NAD+ boosters. Fig. 6Distinct impact of NAD+ treatment at ZT11 versus ZT23 on hepatic nutrient and insulin sensing pathways.a Expression of AKT, p-AKT(S473), AMPK and p-AMPK(T172) along the day in the liver from mice treated with NAD+ either at ZT11 (HFN) or at ZT23 (HFN23) determined by western blot. Uncropped blots in Source Data. b Quantification of western blots from $$n = 5$$ biological samples, normalized to GAPDH loading control. Data from CD at ZT0 was set to 1. c Expression of proteins and phosphor-proteins in the mTOR pathway along the day in the liver from mice treated with NAD+ assessed by western blot. d Quantification of western blots from $$n = 5$$ biological samples. Measurements were normalized to p84 or GAPDH loading controls. Data from CD at ZT0 was set to 1. e–g RT-qPCR determined expression of rate-limiting and regulatory enzymes involved in mitochondrial (e) or peroxisomal (f) β-oxidation, and (g) ω-oxidation ($$n = 5$$ mice for CD and HF, 6 for HFN and 5 for HFN23 except at ZT6, where $$n = 6$$). h 14C-palmitic acid oxidation was assessed by quantifying 14CO2 release ex vivo from livers of HF and HFN mice ($$n = 4$$ mice per group with three independent measurements. Two experimental replicates were performed with comparable results). Two-way ANOVA i Oxygen consumption rate (OCR) was determined using Seahorse XF analyzer to assess CPT-1-dependent mitochondrial respiration from $$n = 6$$ mice with seven technical replicates each. j Mitochondrial bioenergetic parameters were calculated from extracellular flux analyses. One-way ANOVA with Siddak´s posttest. CD control diet-fed mice, HF high-fat diet-fed mice, HFN high-fat diet fed, NAD+ treated mice at ZT11, HFN23 high-fat diet fed, NAD+ treated mice at ZT23. Data points at ZT24 are duplicates from ZT0, replotted to show 24-h trends. The data represent means ± SE. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, Two-way ANOVA followed by Bonferroni’s (b, d) or Tukey’s (e–g) posttest. Statistical details and exact p values are provided in Supplementary Data 1.Symbol key for multiple comparisons: *HFN vs HFN23; #HF vs HFN. See also Supplementary Fig. S6.
It is widely accepted that AMPK regulates lipid metabolism through phosphorylation of acetyl-CoA carboxylase 1 (ACC1) at Ser79 and ACC2 at Ser212. These in turn downregulate the production of malonyl-CoA, the major substrate for fatty acid synthase (FAS) and a strong inhibitor of carnitine palmitoyl transferase 1 (CPT-1). Consequently, fatty acid synthesis is suppressed in favor of lipid oxidation, partially through activation of the rate-limiting step sustained by CPT-184. Additionally, increased fatty acid oxidation has been largely recognized as a major metabolic outcome after pharmacological increase of NAD+40,85, and this process appears rhythmic in mouse liver with increased rate near the end of the rest period21. Also, our gene expression data revealed a unique NAD+ transcriptional signature involving genes pertaining to catabolic processes at ZT18 (Fig. 4j). Hence, we sought to explore the diurnal transcriptional profile of genes involved in lipid oxidation. Selected transcripts from the microarray data and the key rate-limiting enzymes Cpt1a, Cpt2, Acox1, Abcd1 were quantified in the livers from all groups (Fig. 6e–g). As expected, we found that genes involved in β-oxidation, either mitochondrial (Cpt1a, Cpt2, Acot2, Crat, Acaa1b, Acsm1, Echs1; Fig. 6E) or peroxisomal (Acox1, Abcd1, Slc27a2; Fig. 6f), and in ω-oxidation (Cyp4a10, Cyp4a14, Cyp4a31; Fig. 6g) were globally overexpressed in HFD-fed mice compared to lean mice86. Interestingly, almost all genes were significantly overexpressed specifically at ZT18 in obese mice treated with NAD+ at ZT11 (HFN), but not at ZT23 (HFN23). Indeed, fatty acid oxidation-related genes were highly expressed at the end of the rest period (~ZT12)86; yet, unique to the HFN group was that the breadth of transcriptional activity further extended through the active period, reaching significantly higher levels than in the non-treated, obese mice (HF) at ZT18 (Fig. 6e–g, P < *0.05, **0.01, ***0.001 Two-way ANOVA with Tukey post-test). Hereby, expression of these genes at ZT18 was altered depending on the time of NAD+ treatment, in a way that the treatment at ZT11 significantly enhanced their expression, whereas in mice treated at ZT23, expression was significantly reduced to levels largely comparable to the CD littermates (Supplementary Fig. S6c, One-way ANOVA with Tukey posttest). Accordingly, housekeeping genes Tbp and Rplp presented no significant variations (Supplementary Fig. S6d). To address whether these transcriptional changes in HFN mice are functional, we measured global fatty acid oxidation rates and CPT-1-dependent mitochondrial respiration in liver explants. Notably, we found a significant induction of fatty acid oxidation in HFN mice (Fig. 6h), which was accompanied by increased CPT-1-mediated mitochondrial respiration when palmitoyl-CoA was supplied as a substrate (Fig. 6i). Accordingly, a significant rise in maximal respiration and ATP production was evident in mitochondria from HFN mice (Fig. 6j), demonstrating functional implications for transcriptional variations from fatty acid oxidation genes in HFN mice. Together, these data indicate that increased hepatic NAD+ levels at the beginning of the active phase induce AMPK phosphorylation and activity, favoring a transcriptional program of genes involved in fatty acid oxidation which extends through the active phase, increasing mitochondrial respiration and fatty acid consumption capacities, and possibly contributing to weight loss and decreased hepatic and circulating triglycerides specifically in HFN mice.
While obese mice treated with NAD+ at ZT23 presented some metabolic ameliorations mostly consisting of improved basal circulating glucose levels and reduced hepatic steatosis and inflammatory markers, we did not find consistent changes in gene expression or nutrient sensing signaling. Intriguingly, our microarray data showed that transcripts with highest fold-change after NAD+ treatment were Metallothionein 1 and 2 (Mt1 and Mt2), two antioxidants and longevity regulators known to protect from HFD-induced obesity87–89, and these transcripts were significantly more overexpressed in obese mice treated with NAD+ at ZT23 (Supplementary Fig. S6e, $P \leq 0.001$ HFN vs HFN23; Two-Way ANOVA with Tukey´s posttest). A similar case was found for the gene lipocalin 2 (Lcn2), which encodes for a secreted protein protective against NAFLD90. Hence, while NAD+ chronotherapy works optimally at ZT11, its supply at ZT23 induces distinct protective pathways responsible for a mild, albeit noticeable, improvement of HFD-induced metabolic disease.
## Timed NAD+ treatment reorganizes the hepatic clock
Chronotherapy with NAD+ at ZT11 and ZT23 led to significantly different consequences in metabolic fitness and daily gene expression in the liver of obese mice. Hence, we reasoned that the molecular clock might be responsible for daily variations in the effectiveness of the treatment. Thereby, we compared the hepatic clock protein expression along the day between obese mice treated at ZT11 and at ZT23 (Fig. 7a). Western blot analyses revealed a remarkable impact of NAD+ treatment at ZT23 in the dynamic expression of the clock proteins CRY1, PER2 and REV-ERBα, which displayed an almost complete antiphase dynamic (Fig. 7a, b). Concomitantly, BMAL1 phosphorylation was also 6–10 h phase-shifted by NAD+ treatment at ZT23, being higher at ZT0 (Fig. 7a, b, Two-Way ANOVA). Subsequently, we explored the expression of clock genes across the day (Fig. 7c). Strikingly, NAD+ treatment at ZT23 in obese mice induced a transcriptional rewiring of clock genes, whose expression almost perfectly mirrored that of the other groups, demonstrating that at ZT23, NAD+ synchronizes the hepatic clock genes’ expression. Consequently, the average acrophases of the oscillations, defined as the highest point of the fitted wave by CircWave, were displaced by 10–12 h in the HFN23 group in all tested clock genes, both activators (Bmal1, Clock) and repressors (Cry1, Per1, Per2, and Rev-Erbα) (Fig. 7c, Supplementary Fig. S7a). Indeed, NAD+ chronotherapy did not compromise rhythmicity in clock gene expression (Supplementary Fig. S7a, $P \leq 0.05$; CircWave F test). To determine whether the observed antiphase dynamic of the clock transcriptional regulators was functional, we selected the genes Dbp, Tef, Nfil3, and Noct, whose expression is directly and mostly controlled by the core clock machinery, and analyzed their hepatic expression around the day (Fig. 7d). Coincident with the clock gene expression, the daily transcriptional profile of clock-controlled genes appeared rhythmic for all groups, and phase-inverted specifically in the obese mice treated with NAD+ at ZT23, with a significant phase shift of 11–13 h for Dbp, Tef, and Noct, and ~8 h for Nfil3 expression (Fig. 7d, Supplementary Fig. S7e). To further reinforce the notion that NAD+ supply displaces the phase of circadian oscillations from the hepatic clock, we analyzed livers from obese mice treated at ZT5 (HFN5) or at ZT17 (HFN17). We found expected phase advance in clock protein oscillations in livers from HFN5 mice, while treatment at ZT17 resulted in phase delayed oscillations (Supplementary Fig. S7b, c) of distinct extents with respect to oscillations detected in HF mice (Supplementary Fig. S7c). This was paralleled by coherent phase shifts in clock and clock-controlled genes expression (Supplementary Fig. S7a, d). As expected, the phase shift was more evident in HFN17 than in HFN5, because the mammalian clock is reluctant to phase advances and more susceptible to phase delays91–94. Overall, these analyses show that timed NAD+ supply reshapes hepatic circadian oscillations of the clock machinery, adjusting their phase to the time of treatment. Fig. 7NAD+ synchronizes the hepatic circadian clock.a Circadian clock protein expression from liver whole cell extracts of obese mice treated with NAD+ at ZT11 (HFN) or ZT23 (HFN23). Tubulin or GAPDH were used as loading control. Uncropped blots in Source Data. b Quantification of western blots from $$n = 3$$ mice, except for BMAL1 where $$n = 4$$ for HFN and 5 for HFN23. Measurements were normalized to the loading control, and data from CD at ZT0 was set to 1 c RT-qPCR determined circadian clock gene expression in the liver ($$n = 5$$ mice for CD and HF, 6 for HFN and 5 for HFN23 except at ZT6, where $$n = 6$$). d RT-qPCR determined rhythmic expression of clock-controlled genes in the liver (n as in c). e, f Chromatin immunoprecipitation (ChIP qPCR) was performed in the liver from mice at ZT6 or ZT18, using anti-BMAL1 antibodies. ( $$n = 4$$ biological and two technical replicates). g, h RT-qPCR determined rhythmic expression of genes related to NAD+ metabolism (g) and genes regulating lipid metabolism (h) in the liver (n as in c). BMAL1 ChIPs at ZT6 or ZT18 were analyzed by performing qPCR on BMAL1 binding sites at selected regulatory elements of these genes. CD Control diet-fed mice; HF High-fat diet-fed mice; HFN High-fat diet-fed, NAD+ treated mice at ZT11; HFN23 High-fat diet fed, NAD+ treated mice at ZT23. b–d Data points at ZT24 are duplicates from ZT0, replotted to show 24-h trends. The data represent means ± SE. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, Two-way ANOVA followed by Sidak’s posttest. p values are provided in Supplementary Data 1. See also Supplementary Figs. S7, S8.
Because redox rhythms regulate DNA binding of CLOCK:BMAL1 heterodimers in vitro95, and the NAD+ precursor NR increases BMAL1 recruitment to chromatin in livers from aged mice46, we hypothesized that inverted expression of clock genes in HFN23 might be driven by time-specific recruitment of BMAL1 to chromatin. To test this, we performed ChIP analyses to measure BMAL1 binding to regulatory E-boxes of clock and clock-controlled genes (Fig. 7e, f). As described96, we observed increased recruitment of BMAL1 at ZT6 in livers from CD, HF, and HFN groups of mice for all tested E-boxes. Notably, in livers from HFN23 mice, BMAL1 binding appeared significantly increased at ZT18, consistent with inverted expression (Fig. 7e, f; $P \leq 0.05$, Two-way ANOVA with Sidak´s post-test). A non-related region at the 3’ UTR region of *Dbp* gene was used as a negative control. We further evaluated the effect of NAD+ supplementation in the expression of NAD+ biosynthesis and salvage genes Nmrk1, Nampt, Nmnat3, and Nadk which also showed inverted phase specifically in HFN23 mice (Fig. 7g). Accordingly, BMAL1 binding to their regulatory elements was increased at ZT18 in HFN23 mice, yet specific to these group of genes was that NAD+ treatment significantly potentiated BMAL1 recruitment to chromatin. Finally, we explored the expression from TFs collaborating with the clock machinery to sustain a rhythmic transcriptional reprogramming in obesity16,86: Pparg2, Ppara, and Srebf1c. Transcription for these genes was phase-inverted specifically in HFN23 mice, which was also accompanied by differential BMAL1 chromatin recruitment (Fig. 7h). Also, expression levels of additional TFs related to hepatic lipid metabolism Hnf4a, Foxa2, Foxo1, and Cebpa were altered to a similar extent (Supplementary Fig. S8a). Antiphase expression of key transcription factors regulating hepatic lipid metabolism might underlie the inverted pattern of circulating triglycerides in HFN23 mice (Fig. 5h), but other lipids synthetized in the liver might be affected. Accordingly, hepatic cholesterol levels also showed a phase-inverted pattern in the liver of HFN23 mice (Supplementary Fig. S8b, c), reinforcing the idea that NAD+-mediated synchronization of transcriptional rhythms in the liver inverts hepatic lipid metabolism. Together, this data demonstrates a time-dependent transcriptional response to NAD+ therapy in the liver of obese mice, through the synchronization of BMAL1 recruitment to chromatin and rhythmic transcription of clock and clock-controlled genes. Hereby, BMAL1 plays a pivotal role translating fluctuations in NAD+ levels to shape circadian transcription.
## Feeding and locomotor activities remain largely aligned to light in NAD+-treated mice
A phase-inverted hepatic clock has been previously shown for mice subjected to inverted feeding rhythms, where the SCN clock remains aligned to light-dark cycles97,98. At this regard, in all tested groups of mice, clock gene expression in the SCN remained largely intact after NAD+ treatment at two selected circadian times (Fig. 8a, Two-way ANOVA), and locomotor behavior analyses showed that overall, NAD+ treatment preserved alignment between light-dark and rest-activity patterns (Fig. 8b). Quantification of locomotion in 30 min bins revealed that after NAD+ treatment, mice became significatively less active for either 90 min (HFN) or 30 min (HFN_23) windows (Fig. 8c, Supplementary Fig. S8d, Two-way ANOVA followed by Sidak’s posttest). We next questioned whether feeding cycles might be altered by NAD+ treatment because as previously reported, this is a cause for uncoupled central and peripheral clocks97,98, while NAD+ itself can influence feeding behavior through implicated hypothalamic circuits99,100. Notably, daily food intake appeared rhythmic and aligned to light-dark cycles for all groups of HF diet fed mice (Fig. 8d, e), showing a more robust day-to-night difference the obese mice treated with NAD+ at ZT11 (Fig. 8e). Furthermore, we found similar observations when applying a therapy with the NAD+ precursor nicotinamide (NAM), which was previously described to boost hepatic NAD+ after IP injection in one hour101. Hence, three weeks with NAM chronotherapy performed best when applied at ZT11 to improve body weight, GTT and ITT (Supplementary Fig. S9a–d). As shown for NAD+, the NAM treatment at ZT23 inverted the expression of the hepatic molecular clock (Supplementary Fig. S9e), while keeping behavioral locomotor activity in phase with light/dark cycles (Supplementary Fig. S9f). This reinforces the notion that NAD+ can potentially synchronize the hepatic molecular clock, by reorganizing clock gene expression to adjust its phase to the time of the day when NAD+ bioavailability is higher. Collectively, these data support that boosting NAD+ levels is an effective treatment for HFD-induced metabolic disease, and demonstrate that a chronotherapeutic approach is significantly more beneficial when NAD+ increases at the onset of the active phase. Fig. 8Time-of-day dictates efficiency of NAD+ treatment of diet-induced metabolic disease through synchronizing the hepatic circadian clock and adjusting coordination between intracellular signaling and gene expression.a RT-qPCR from clock genes in the SCN at ZT0 and ZT12 ($$n = 3$$ biological replicates and 2 technical replicates). b Representative double-plotted actograms of locomotion measured using infrared sensors in a 12-h light/12-h dark cycle. c Average 24-h activity profile from the indicated groups of mice. Average was calculated for five days after NAD+ treatment. $$n = 7$$ mice for HF and HFN, and 6 for HFN23. d Food intake was measured over 24 h e Average food intake during light phase (day) and dark phase (night). For the boxplots, the top and bottom lines of each box represent the 75th and 25th percentiles, respectively. The line inside each box represents the median. The lines above and below the boxes are the whiskers f At ZT12, NAD+ sustains the alignment of the hepatic molecular clock while reinforcing circadian oscillation in the activity of nutritional sensors such as AMPK, AKT or mTOR. Transcriptional responses are adjusted accordingly to suppress inflammation probably through inhibition of NF-κB transcription factor, and to increase amplitude in lipolytic gene expression and fatty acid oxidation, with a peak during the active phase. Membrane trafficking and ULK activity are indicative of active autophagy as a specific response to NAD+ treatment. At ZT0, increased NAD+ in the liver inverts the phase of the molecular clock, imposing antiphase rhythms in clock gene and protein expression. This in turn inverts the phase of clock-controlled genes and uncouples transcriptional responses. CD control diet fed mice, HF high-fat diet-fed mice, HFN high-fat diet fed, NAD+ treated mice at ZT11, HFN23 high-fat diet fed, NAD+ treated mice at ZT23. The data represent means ± SE. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, Two-way RM ANOVA followed by Sidak’s posttest. Symbol key for multiple comparisons: #HF vs HFN, X HF vs HFN23, $HFN vs HFN23. See also Supplementary Fig. S8.
## Discussion
In the past decade, therapies oriented to increase endogenous NAD+ levels have received much attention as treatments for metabolic disorders. Mounting research in rodents demonstrate that pharmacological approaches using “NAD+ boosters” treat the physiopathology of diet and age-associated diabetes in mice, and reverse cardiovascular disease or muscle degeneration85. In humans, the NAD+ precursor niacin has been largely used to treat dyslipidemia, and a number of clinical trials are ongoing for other NAD+ precursors102. However, all these studies and clinical protocols mostly disregard the reciprocal interactions between circadian rhythms and NAD+ metabolism. Here, we demonstrate that NAD+ can shift the phase of the hepatic molecular clock while preserving the SCN clock largely intact, and concomitantly, the efficacy of increasing NAD+ levels to correct metabolic diseases depends on the time-of-day (Fig. 8f).
NAD+ and its phosphorylated and reduced forms, NADP+, NADH and NADPH, are fundamental compounds in intermediary metabolism as hydride-accepting or -donating coenzymes in redox reactions103. NAD+ is produced in all tissues from the salvageable metabolite NAM, or from precursors including nicotinamide riboside (NR), nicotinic acid (NA) or nicotinamide mononucleotide (β-NMN), while some tissues such a liver produce NAD+ de novo from tryptophan, in a much less efficient biosynthetic pathway30,104. At this regard, the NAD+ precursors NAM, NMN and NR have been preferentially used as NAD+ boosters; however, we set up a therapy with NAD+ because the limited data tracing metabolic fluxes suggest distinct, tissue-specific effects of NR and NMN105. Moreover, NAD+ uptake appears fast and effective in cells, and a mitochondrial active transporter has been recently described106–109. Yet, to gain insights into the bioavailability of NAD+ precursors in our study, it would be necessary to unravel the hepatic NAD+ metabolome in all tested conditions, as for example, the possibility that time-dependent decline in NADPH and NADP+ levels in livers from obese mice27,35,110 contributes to differences between HFN and HFN23 mice cannot be ruled out, constituting a limitation of our study. However, we demonstrated that hepatic NAD+ levels raised within an hour after IP injection in obese mice, and followed a circadian turnover when administered at ZT11, at the onset of the active phase (Fig. 1b). This chronotherapy recapitulated the metabolic improvements to a similar extent to the previously reported for the NAD+ precursors NMN36,111,112 and NR32,35,40,43,44,113–115, mostly consisting of decreased weight gain, improved insulin sensitivity and glucose tolerance, decreased circulating leptin and triglycerides, and amelioration of NAFLD with decreased hepatic pro-inflammatory transcriptional signature (Figs. 1–3). At the molecular level, we demonstrated that, upon NAD+ chronotherapy, daily rhythms were restored for hepatic insulin and nutrient signaling. This was evidenced by rhythms in AMPK-T172 and AKT-S473 phosphorylation, and mTORC1-directed pS6K phosphorylation, which became oscillatory with peaks during the active phase (ZT12-18; Fig. 4f, g). Accordingly, we also observed decreased phosphorylation of p90RSK-T359 (Fig. 4g), a positive effector of mTORC1 signaling and driver of NFκB activity116,117. It appears conflicting that the AMPK response to starvation and the mTORC1 nutrient sensing pathways became active at concurrent times during the day after restoring NAD+ oscillations in obese mice, as they usually signal opposed nutritional states and engage into regulatory negative feedback loops118. However, recent research shows that specific activation of AMPK exists which does not lead to mTORC1 inhibition, but instead sustains ULK1 activity and autophagy to preserve protein homeostasis119, which is in keeping with our findings (Fig. 4f, g, Supplementary Fig. S4c). Notably, a hepatic NAD+-specific transcriptional signature emerged in treated mice related to intracellular trafficking, consisting of overexpression of the Rab GTPase network regulator of autophagy70, further reinforcing the notion that NAD+ preferably targets AMPK signaling to activate autophagy and possibly, translocation of membrane receptors. Along these lines, AMPK has been largely recognized as a therapeutic target for metabolic diseases120,121, yet the well-known circadian fluctuations in its activity61 have been fully overlooked for treatment.
We have demonstrated time-of-day dependent and independent responses to NAD+ therapy. Clearly, as previously reported122,123, rising NAD+ levels elicit positive responses including correction of hepatic steatosis and the inflammatory environment in obese mice, and we found that these positive effects occur independently of time of NAD+ supply (Fig. 5i–m). In fact, these two processes are interconnected, since during obesity, inflammation in the liver happens and increased cytokines lead to overexpression of genes involved in de novo lipogenesis and ceramide biosynthesis124,125. However, we also observed different responses to NAD+ between obese mice treated at ZT11 or at ZT23, where the latter did not fully recapitulate certain metabolic parameters, such as improvement in glucose tolerance, insulin sensitivity or circulating triglycerides. Concomitantly, NAD+ therapy at ZT23 did not trigger AMPK phosphorylation neither corrected mTORC1 signaling in the liver of obese mice. Strikingly, the expression dynamics of the molecular clock were completely phase inverted in livers from HFN23 and HFNAM_23 mice, showing that at the onset of the active phase, NAD+ can efficiently invert the phase of the hepatic clock (Figs. 7, 8, Supplementary Figs. S7–S9). These findings support earlier evidence that specific nutritional cues are potent zeitgebers for peripheral oscillators16,47,97,126, and reinforce the existing notion of autonomous regulation of hepatic NAD+ metabolism closely linked to the clock function26. Together with our findings, this suggests that the molecular clock acts as a key interface to induce timing-specific modulation of nutrient and insulin signaling by NAD+.
Our analyses revealed substantial differences in expression from genes involved in fatty acid oxidation, with marked downregulation in obese mice treated with NAD+ at ZT23 (Fig. 6e–g, Supplementary Fig. S6c). In mouse liver, these genes are oscillatory with a peak of expression at the end of the rest phase86. Their expression is to some extent clock-controlled; however, their transcriptional regulation mostly relies on nutritional cues integrated by intracellular signaling, multiple nuclear receptors and transcription factors such as PPARγ, PPARα or SREBP1, epigenetic regulators including MLL1 or SIRT1, and even neural circuits16,86,127–129. Untimed NAD+ rise, through reorganizing the circadian machinery and the subsequent misalignment from feeding rhythms, might hinder the coordinated action between the clock and cooperative transcriptional regulators on chromatin, hereby obstructing the adequate control of specific transcriptional programs. At this regard, BMAL1 recruitment to chromatin was adjusted by timed NAD+ treatment, and when administered at ZT23 leaded to phase-inverted transcription of direct CLOCK:BMAL1 targets, as expected for a pioneer-like transcription factor130,131. In this scenario, we found that several master regulators of rhythmic hepatic lipid and cholesterol metabolism including Pparα, Pparγ, Srebp1c, Cebpa, or Hnf4a16,86,132,133 were subjected to this mechanism, and their phase inversion in HFN23 mice was accompanied by inverted rhythms in hepatic cholesterol and circulating triglycerides (Fig. 7, Supplementary Fig. S8). These results demonstrate that NAD+ modulates BMAL1 recruitment to chromatin and shapes rhythmic transcription and metabolism.
NAD+ is a coenzyme in redox reactions, but also serves as a substrate of NAD+ consuming enzymes which cleave NAD+ to produce NAM and an ADP-ribosyl product, such as ADP-ribose transferases, cADP-ribose synthases and sirtuins (SIRT1-SIRT7)103,134,135. Indeed, both NAD+ consumers SIRT146,136,137 and SIRT321 provide reciprocal regulation to the clock machinery to modulate circadian transcription and metabolism in the liver. Furthermore, recent research shows that a NAD+-SIRT1 interplay mediates deacetylation and nuclear translocation of PER2 and, in line with our results, shapes BMAL1 function, while this control is altered in livers from aged mice46. Through activation of SIRT1 and SIRT3, it is also possible that rising NAD+ at ~ZT12 might contribute to rhythmic lipid oxidation and mitochondrial function driven by protein acetylation, including PPARγ27,138, while keeping the hepatic clock aligned to the external time. Yet, the regulation of the circadian system by sirtuins in health and disease remains to be fully disentangled. Circadian misalignment imposed by antiphase NAD+ in our HFN23 and HFNAM_23 mice might obstruct metabolic improvements, through uncoupling of the central light-synchronized and peripheral NAD+-synchronized clocks. Although hepatic neutral lipid content was reduced independently of time-of-treatment (Fig. 5i–k), significant improvement of glucose homeostasis and hepatic insulin signaling were apparent only in HFN mice. Indeed, circadian misalignment has been extensively reported to drive metabolic dysfunction both in mouse and humans139–141. In this scenario, expression of clock genes in the SCN at two circadian times appeared largely intact upon NAD+ injection, and concomitantly, locomotor activity remained aligned to the light-dark cycles also in HFN23 mice (Fig. 8a–c). Although further analyses with higher resolution are essential to fully disentangle the extent of NAD+ influence on cyclic gene expression in the SCN, uncoupled liver and SCN clocks have been previously reported in mice when access to food is restricted to the light period97,142,143. However, our HFN23 mice did not show significant variations in eating behavior (Fig. 8d–e), evidencing that uncoupling the central and hepatic clocks is a time-dependent effect of NAD+ supply. Notably, abnormal metabolic signaling triggered by high-fat diets uncouples body clocks15; thus, it would be interesting to define which extra-hepatic oscillators are influenced by NAD+. At this regard, recent reports suggest that the brain blood barrier might be permeable to NAD+144,145 in which case hypothalamic neurons could be influenced. Yet, further research is necessary to decipher the extent of the modulation of brain clocks by increased circulating NAD+ precursors. Additionally, our study is limited by the cellular heterogeneity in fatty liver, with for example, infiltration of pro-inflammatory macrophages which have been recently shown to limit NAD+ bioavailability through high expression of the NAD-consuming enzyme CD38146,147. Hence, it is possible that time-dependent cellular heterogeneity in liver148 could contribute to the NAD+-dependent improvement of the metabolic phenotype.
In humans, clinical trials aiming to boost endogenous NAD+ for treatment of metabolic diseases are increasing, showing promising results; for example, in postmenopausal women with prediabetes, a daily dose of NMN increases muscle insulin sensitivity45,134,149. However, all these studies mostly overlook the time of drug intake, which is selected based on practicalities or attempting to displace side effects from the patient’s active phase. Considering our results, we propose that time of treatment dictates the amplitude of metabolic benefits from rising NAD+ levels, which ideally outlines the basic strategy of chronobiology-based NAD+ therapy.
## Animals and diets
Four-week-old C57Bl/6 J mice were obtained from the Biological Models Unit at the Instituto de Investigaciones Biomédicas (UNAM, Mexico). The mice were kept under a 12:12-h light:dark cycles. Food and water were provided ad libitum. Temperature and humidity were constantly monitored. Mice were randomly distributed to four groups. The control group was fed during 11 weeks with normal chow (CD, 2018S Teklad, ENVIGO), bearing $24\%$ calories from protein, $18\%$ calories from fat, and $58\%$ calories from carbohydrates. The other three experimental groups were fed a high-fat diet (HFD, based on TD.160547 Teklad, ENVIGO), consisting of $15\%$ calories from protein, $53\%$ calories from fat and $38\%$ calories from carbohydrates, and customized to match NAD+ dietary sources content to that of the CD ($0.2\%$ tryptophan and 115 mg/kg nicotinic acid). Food intake and body weight were measured once a week. For daily food intake measurements, mice were single housed, and measurements were recorded for 1 week. Female mice were used for the experiments using NAM as a NAD+ precursor, and the rest of the experiments were performed in male mice.
All animal experimental procedures were reviewed and approved by the Internal Committee for the Care and Use of Laboratory Animals (CICUAL) at the Instituto de Investigaciones Biomédicas, (UNAM, Mexico), and are registered under protocol no. ID240.
## Chronotherapy with NAD+ and NAM
NAD+ and NAM were purchased from SIGMA (cat. no. N7004, N0636) and were dissolved in $0.9\%$ NaCl isotonic saline solution and filter sterilized. To determine the NAD+ dose, we wanted to keep two premises: [1] to keep NAD+ levels into the physiological range, and [2] avoid undesirable secondary effects of high doses. To do so, we chose the range of tested doses based on previous reports100,150,151, and treated mice with IP injection of 800, 100, 50, or 10 mg/kg body weight, while keeping a constant volume of approximately 180 μl. Control mice were injected with isotonic saline solution. C57Bl/6 J male mice ($$n = 3$$) were IP injected, and sacrificed one hour later. NAD+ was measured by HPLC as described below. Because we planned on a chronic treatment, the minimum dose inducing a statistically significant increase in hepatic NAD+ with respect to control livers was selected as the experimental dose (Supplementary Fig. S1a, 50 mg/Kg of body weight: $P \leq 0.0001$, One-way ANOVA with Tukey’s posttest). Hence, for all experiments, mice were IP injected with 50 mg/kg of NAD+ for 20 consecutive days, either at ZT11 (one hour before lights off), or at ZT23 (one hour before lights on). Of note, we didn´t find differences in hepatic NAD+ at a dose of 10 mg/kg, a reason why we did not try lower concentrations. The dose for NAM treatment in female mice (200 mg/kg) was selected based on previous reports101,152–154.
## Detection and quantification of NAD+ by HPLC
NAD+ measurements were performed according to Yoshino and Imai 2013155, with subtle modifications. 100 mg of frozen tissue were processed in a final volume of 2 ml of $10\%$ HClO4 with a Polytron homogenizer (Kinematica CH-6010 Kiriens-Lu) and centrifuged at 12,000 × g for 5 min at 4 °C. The supernatant was neutralized adding a one-third volume of 3 M K2CO3, and vortexed. After 10 min of incubation on ice, samples were cleared by a 12,000 g centrifugation at 4 °C during 5 min. The supernatant was diluted at $30\%$ with 50 mM phosphate buffer ($3.85\%$ of 0.5 M KH2PO4, $6.15\%$ of 0.5 M K2HPO4, $90\%$ HPLC grade water -v/v/v-, pH 7.0, filtered through a 0.22 μm filter and degassed). 50 μl of the samples were analyzed using a 1260 infinity quaternary LC VL HPLC system (Agilent) attached to a diode array detector. Analytes were separated on a ZORBAX Eclipse XDB-C18 4.6 × 150 mm, 5 μm column (Agilent p/n 993967-902). For the HPLC, the gradient mobile phase was delivered at a flow rate of 1 ml/min, and consisted of two solvents: (A) 50 mM phosphate buffer pH 6.8 and (B) methanol $100\%$. The initial concentration of A was $100\%$, the solution was held into the column for 5 min and then B was progressively increased to $5\%$ over 1 min, held at $5\%$ B for 5 min, followed by an increase to $15\%$ B over 2 min, held at $15\%$ B for 10 min and returned to starting conditions of $100\%$ A in 1 min, and held at $100\%$ A for 6 min. NAD+ was detected using a sample wavelength of 261 nm and reference wavelength of 360 nm. Adequate standards including NAD+ were used for calibration and identification of the retention/migration time of NAD+ within the samples. Instrument control, data acquisition, and analysis were performed using the Agilent ChemStation system for LC, according to manufacturer’s instructions. NAD+ levels were quantitated based on the peak area in the chromatograms compared to a standard curve and normalized to tissue weight.
## Glucose tolerance test (GTT) and insulin tolerance test (ITT)
At 8, 10, and 11 weeks of experimental paradigms, mice were subjected to either 12 h or 5 h of fasting, followed by a glucose tolerance test (GTT) or an insulin tolerance test (ITT) respectively. For the GTT, IP injection of D-glucose (SIGMA cat no. G7021) at 2 mg/kg was used, while for ITT, human insulin (Eli Lilly cat. HI0210) at 0.6 U/kg was IP injected. Circulating glucose was measured from a tail-tip blood drop, using an ACCU CHEK active glucometer (ROCHE) at time points 0 (before injection) and 15, 30, 60, and 120 min after IP injection of either glucose (GTT) or insulin (ITT). Experiments were performed per triplicate, using 5–6 mice per experiment.
## Metabolites and hormone analyses
Blood serum was collected postmortem by cardiac puncture. Triglycerides (TG) in serum and liver were measured using the Triglyceride Colorimetric Assay Kit (Cayman Chemical, cat. no. 10010303). Free fatty acid content was determined with the Free Fatty Acid Fluorometric Assay Kit (Cayman Chemical, cat. no. 700310). Serum insulin and leptin levels were measured by ELISA, using the Ultra-Sensitive Mouse Insulin ELISA Kit (Crystal Chem Inc, cat. no. 90080) and the Mouse Leptin ELISA Kit (Crystal Chem Inc, cat. no. 90030) according to the manufacturer’s instructions. Hepatic cholesterol was determined using a Cholesterol Quantitation Kit (Sigma-Aldrich cat. no. MAK043, colorimetric). Absorbance/fluorescence was measured using a Synergy H1 microplate reader (BioTek).
## Temperature measurements
Rectal temperature in mice ($$n = 10$$ mice, and three technical replicates) was registered using a portable digital thermometer (BIOSEB) every 3 h throughout 24 h. For the acquisition of infrared thermography, mice were placed inside an acrylic box in darkness. Thermal images were acquired at ZT12 using an Inframetrics C2 Thermal Imaging System Compact Pocket-*Size camera* (FLIR Systems) with a frequency of 9 Hz, thermal sensitivity <0.10 °C, resolution 80 × 60 (4800 pixels) and temperature range of 14 to 302 ° F. ($$n = 4$$, with three technical replicates). Images processing was performed using FLIR-Tools software v 5.13.17214 (2015 FLIR® Systems).
## Oil-Red-O staining
Frozen OCT-embedded liver tissues were cut into 10-μm sections using a Leica cryostat and air dried for 10 min at room temperature. Slides were briefly washed with PBS and fixed for 2 min with $4\%$ fresh paraformaldehyde. Preparation of Oil Red O (SIGMA, cat. no. O1391) working solution and staining of slides was performed according to Mehlem et al.156. Oil Red O working solution (3.75 mg/ml) was applied on OCT-embedded liver sections for 5 min at RT. Slides were washed twice during 10 min. in water, and mounted in vectashield mounting media (Vector Labs, cat. no. H-1000). The images were captured with the *Olympus camera* DP70 system using the DPController v 1.1.1.65 software, coupled to a Olympus BX51 microscope with the DPManager software v. 1.1.1.71, using a ×40 magnification. The background was corrected by white balance and was selected as a blank area outside the section. For representative images, some sections were stained with Gil I haematoxylin. Surface of lipid droplets was quantified using the ImageJ software (v 1.53), by converting RGB to 8-bit grayscale images, and then using the “analyze particles” plug-in to measure the area and size of the lipid drops157. Three frames per biopsy were used for image analyses and quantification ($$n = 3$$ biological replicates with three technical replicates).
## SCN dissection
*For* gene expression analysis from the SCN, frozen brains were placed on ice, and the 1 mm3 region above the optic chiasm was dissected out using microscissors. Tissues were placed in microcentrifuge tubes in 100 µl of Trizol and kept at −80 °C until use. Total RNA was subsequently extracted and resuspended in 12 µl of water.
## Total RNA extraction
20 mg of liver tissue or the dissected SCN, were homogenized (Benchmark Scientific, D1000 homogenizer) for 30 seconds with 0.5 ml of Trizol (TRIzolTM Reagent, Invitrogen, cat. no. 15596018). The homogenate was incubated for 5 min at RT, then 0.1 ml of chloroform was added, shaken, and incubated at RT for 3 min followed by a centrifugation during 15 min at 12,000 × g at 4 °C. The upper phase was extracted, and 0.25 ml of isopropanol was added. After a 10 min incubation at RT, RNA was precipitated by centrifugation for 10 min at 12,000 × g and 4 °C. The RNA was washed with 1 ml of $75\%$ ethanol and resuspended in 20 µl of molecular biology grade water (Corning, cat. no. 46-000). 2 µl of the sample were used to quantify its concentration and assess its quality in a NanoDrop (Thermo Scientific).
## cDNA synthesis
It was performed using the kit iScriptTM cDNA synthesis (Bio-Rad, cat. no 1708890). 500 ng of RNA were mixed with 2 µl of 5X iScript Reaction Mix and 0.5 µl of the enzyme iScript Reverse transcriptase in a volume of 10 µl. The thermal cycler (Axygen MaxyGeneTM II) was programmed as follows: Alignment for 5 min at 25 °C, reverse transcription for 20 min at 46 °C and inactivation for 1 min at 95 °C. The reaction was cooled to 4 °C and diluted to 5 ng/µl.
## Quantitative real-time polymerase chain reaction
The reactions were performed in a final volume of 10 µl, adding 5 µl of the Universal SYBR Green Super Mix reagent (Bio-Rad, cat. No. 1725121), 1 µl of 2.5 µM forward and reverse primers, and 7.5 ng of cDNA per reaction. The thermal cycler (Bio-Rad, CFX96 Touch Real-Time PCR Detection System) was set to the following program: 30 s at 95 °C followed by 40 cycles of 5 s at 95 °C and 30 s at 65 °C. Single-product amplification was verified by an integrated post-run melting curve analysis. Values were normalized to the housekeeping genes B2m, Ppia, and Tbp. The geometric mean was used to determine Ct values of the housekeeping genes and expression values for the genes of interest were calculated using ΔCT methodology. Primer sequences are available in Supplementary Data 5.
## mtDNA quantification by quantitative real-time PCR
10 mg of liver were used to extract DNA with the DNeasy Blood & Tissue Kit (QIAGEN, cat. no. 69506), according to the manufacturer’s instructions. Quantitative PCR was performed using 7.5 ng of DNA and 2.5 μM of S18 and mtCOX1 primers as described for cDNA quantification, with a program of 20 min at 95 °C, followed by 50 to 55 cycles of 15 s at 95 °C, 20 s at 58 °C and 20 s at 72 °C. Single-product amplification was verified by an integrated post-run melting curve analysis. 5–6 mice were analyzed for each time point and condition, with two technical replicates. mtDNA content using the formula: 2 × 2(ΔCT),where ΔCT is the difference of CT values between S18 gene and mtCOX1 gene158.
## Transcriptional profiling from mouse livers
Liver RNA samples for microarray analysis were prepared using our previously described procedures, with slight modifications. Briefly, total RNA was first extracted with TRIzol Reagent (Invitrogen), then cleaned with RNeasy Mini purification Kit (QIAGEN cat. no. 74106) according to the manufacturer’s RNA CleanUp protocol. RIN values (≥7.0) were validated with an Agilent Bioanalyzer 2100. 900 ng of total RNA per sample was used as a template to obtain cDNA with the GeneChip cDNA synthesis Kit (Affymetrix, Santa Clara, CA). Microarray experiments were conducted by the Microarray Unit at the National Institute of Genomic Medicine (INMEGEN, Mexico City) using the mouse Clariom™ D Assay (Applied Biosystems™), as per manufacturer’s instructions. Microarray experiments were performed in triplicate ($$n = 3$$ biological replicates). The Clariom™ D Array consists of 66100 genes (transcript clusters), 214900 transcripts, 498500 exons and 282500 exon-exon splice junctions from Mus musculus. Sequences are mapped to the National Center for Biotechnology Information (NCBI) UniGene database. The arrays were scanned in the GeneChip Scanner 3000 7 G (Affymetrix) and the GeneChip Command Console Software (v 4.0.3) was used to obtain the. CEL intensity files. *Normalized* gene expression data (.CHP files) were obtained with the Transcriptome Analysis Console (TAC v4.0.1.36) software using default parameters. Changes in gene expression (±1. fold-change; FDR-corrected p value ≤0.05) were subjected to functional analyses using the “Compute Overlaps” tool to explore overlap with the CP (Canonical Pathways) and the GO:BP (GO biological process) gene sets at the MSigDB (molecular signature database) v7.0. The tool is available at: https://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp, and estimates statistical significance by calculating the FDR q-value. This is the FDR analog of the hypergeometric P value after correction for multiple hypothesis testing according to Benjamini and Hochberg. Gene set enrichment analysis (GSEA) was performed using GSEA v. 4.0.3.54 to determine the enrichment score within the *Hallmark* gene set collection in MSigDB v7.0159, selecting the Signal2Noise as the metric for ranking genes. The findMotifs.pl program in the HOMER software v 2.0160 was used for motif discovery and enrichment, searching within the genomic regions encompassing 300 Kb upstream and 50 Kb downstream the TSS, and selecting 6–8 bp for motif length. Motif enrichment is calculated by findMotifs.pl using the cumulative hypergeometric distribution.
All raw and processed data can be accessed at the GEO database, number: GSE163865.
## Protein carbonyl (PCO) content
The determination of the carbonyl content was performed from total hepatic protein extracts (0.5 mg/ml), following a previously published protocol161. PCO present in the samples were derivatized by reaction with a working solution of 2,4-dinitrophenylhydrazine (DNPH 10 mM diluted in 0.5 M H3PO4; SIGMA) for 10 min at RT. The reaction was stopped by adding a NaOH (6 M) for 10 min. The absorbance of the samples was read in a spectrophotometer (Jenway, 6305) at 370 nm and the mean absorbance of control tubes (RIPA buffer) was then subtracted. To calculate the PCO concentration expressed as nmol PCO/mg protein, we used the following equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{PCO}}}}}}\; {{{{{\rm{concentration}}}}}}=\frac{{10}^{6}\times \left(\tfrac{{{{{{{\rm{Abs}}}}}}}_{366{{{{{\rm{nm}}}}}}}}{22000{{{{{\rm{M}}}}}}^{-1*{{{{{{\rm{cm}}}}}}}^{-1}}}\right)}{{[{{{{{\rm{protein}}}}}}]}_{{{{{{\rm{mg}}}}}}/{{{{{\rm{ml}}}}}}}}$$\end{document}PCOconcentration=106×Abs366nm22000M−1*cm−1[protein]mg/ml
## Western Blot
Livers were lysed in 1× RIPA buffer supplemented with a protease/phosphatase inhibitor cocktail (cOmplete mini ROCHE 1:25 v/v, PMSF 1 mM, Na3VO4 1 mM, NaF 0.5 mM). Total protein was quantified with Bradford reagent (SIGMA, cat. no. B6916), and 25 μg of extract were suspended 1:6 (v/v) in 6× Laemmli buffer (60 mM Tris HCl pH 6.8, $12\%$ SDS, $47\%$ glycerol, $0.03\%$ bromophenol blue, 1 M DTT), separated on sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and transferred onto PVDF membranes (Merck-Millipore), using the Mini-PROTEAN electrophoretic system (Bio-Rad). Membranes were blocked using non-fat milk in PBST buffer for one hour and incubated with the corresponding primary antibody overnight at 4 °C. Membranes were washed three times with PBST and incubated with the secondary antibody for 5 hrs at RT. Antibodies used in this study were: From Cell Signaling: PPARγ [2443], AKT [9272], Phospho-AKTSer473 [9271], AMPKα [5831], Phospho-AMPKαThr172 [50081], mTOR [2893],), Phospho-mTORSer2448 [5536], Phospho-p70 S6KThr389 [9234], Phospho-4E-BP1Thr$\frac{37}{46}$ [2855], RSK1/RSK2/RSK3 [9355], Phospho-p90RSKSer359 [8753], REV-ERBα [13418], ULK1 [8054], Phospho-ULK1Ser555 [5869], all diluted 1:1000; from Santa Cruz: C/EBPα (SC-365318, 1:500); from Abcam: BMAL1 (Ab3350, 1:1000); from Alpha Diagnostics International: PER2 (PER21-A 1:2000); from Bethyl Laboratories: CRY1 (A302-614A 1:1000); from Sigma: α-Tubulin (T5168, 1:80000); from Genetex: GAPDH-HRP (GTX627408-01, 1:120000) and P84 (GTX70220-01, 1:1000) The secondary antibodies were Anti-rabbit IgG (Cell Signaling, 7074, 1:150000 for BMAL1, 1:10000 for Pparγ and 1:80000 for the rest) or Anti-mouse IgG (Sigma I8765, 1:80000), conjugated to horseradish peroxidase. For detection, the Immobilon Western Chemiluminescent HRP Substrate (Millipore, cat. no. WBKLS0100) was used and luminescence was visualized and documented in a Gel Logic 1500 Imaging System (KODAK). Protein bands were quantified by densitometric analysis using Image Studio Lite Version 5.0 software (LI-COR biosciences). 3–5 biological replicates were used for each quantification. Uncropped, unprocessed scans of the blots are available in the Source Data file.
## Determination of inflammatory cytokines in mouse liver
The Mouse Inflammation ELISA Strip (Signosis, cat. No. EA-1051) was used for profiling inflammation cytokines, following the manufacturer’s instructions. Absorbance at 450 nm was measured using a Synergy H1 microplate reader (BioTek).
## Fatty acid oxidation assay
Fatty acid oxidation was quantitated ex vivo following our previous protocols48, with subtle modifications. Briefly, 20–60 mg of fresh liver from mice sacrificed at ~ZT16 were weighed, and samples were minced and homogenized in 300 μl homogenization buffer (DMEM, 1 mM pyruvate, $1\%$ BSA free fatty acid, and 0.5 mM palmitate) at 4 °C. Then, 5 μl palmitic acid [14 C] 100 μC/ml (Perkin Elmer) was added, and these lysates were incubated for 2 hr at 37 °C. Eppendorf tubes were prepared to contain small pieces of Whatman paper in the cap of the tube, which were wet with 20 μl NaOH 3 M, while 150 μl $70\%$ perchloric acid was placed inside the tube. Lysates were added to these tubes and incubated 1 h at 37 °C. The trapped 14CO2 was determined transferring the filter discs to a scintillation vial with 4 ml of scintillation liquid and measuring in a Beckman LS6500 scintillation counter.
## Palmitoyl-CoA oxidation assay in isolated liver mitochondria
The Palmitoyl-CoA oxidation depends on the activity of Carnitine palmitoyltransferase-1 (CPT-1), the rate-controlling enzyme for long-chain fatty acid oxidation. Measurements of CPT-1-mediated mitochondrial respiration was determined in mitochondria freshly isolated from mouse liver using the Seahorse XFe96 Extracellular Flux Analyzer (Agilent Technologies), as previously described162 with modifications as follows. Mice were sacrificed by cervical dislocation at ~ZT16, and ~100 mg of liver was dissected and placed on ice on mitochondrial isolation buffer (MIB1, 210 mM d-Mannitol, 70 mM sucrose 5 mM HEPES, 1 mM EGTA and $0.5\%$ free fatty acid BSA) and centrifuged at 800 g for 10 min at 4 °C. The tissue was homogenized using the Polytron tissue homogenizer at low potency for 8 seconds and centrifuged at 800 g (10 min at 4 °C). The supernatant was collected in 15 ml falcon tubes and centrifuged at 8000 × g (10 min at 4 °C). The resulting pellet containing the mitochondria was washed three times with 1 ml of MIB1 and resuspended on 0.5 ml of mitochondrial assay solution (MAS1, 220 mM d-Mannitol, 70 mM sucrose, 10 mM KH2PO4, 5 mM MgCl2, 2 mM HEPES, 1 mM EGTA, $0.2\%$ BSA, pH 7.2) with the addition of substrates (40 µM palmitoyl-CoA, 0.5 mM malate, and 0.5 mM carnitine). Total protein was determined using the Qubit® 3.0 Fluorometer and 14 µg of isolated mitochondria were diluted in MAS1 buffer with substrates with or without the presence of the CPT-1 inhibitor, etomoxir (3 µM) and loaded per well in the XFe96 plate. The plate containing mitochondria was centrifuged at 2000 g for 20 min at 4 °C. The oxygen consumption rate (OCR) was measured with seven technical replicates for each mouse, as the following compounds were injected to final concentrations per well: ADP (4 mM), oligomycin (2.5 µM), carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone known as FCCP (2.0 µM) and antimycin A (1 µM) / rotenone (1 µM). OCR was measured in the absence or presence of etomoxir, with sequential addition of ADP (ATP precursor), oligomycin (complex V inhibitor), FCCP (a protonophore), and Rotenone/antimycin A (Rot/AA; complex III inhibitor). Four mitochondrial respiration states were calculated: basal respiration (respiration of mitochondria with substrates but without ADP), ATP production or phosphorylating respiration (rate of ATP formation from ADP and inorganic phosphate), proton leak or non-phosphorylating respiration (rate of oxygen consumption while ATP synthase is inhibited with oligomycin), and Maximal respiration state after the addition of FCCP. The instrument control, data analysis and file management were performed with the Agilent Seahorse Wave v2.6 software.
## Chromatin immunoprecipitation (ChIP)
100–200 mg of liver tissue were homogenized with a pestle in PBS. Dual crosslinking was performed in a final volume of 1 ml using 2 mM of DSG (Disuccinimidyl glutarate, ProteoChem, CAS: 79642-50-5) for 10 min at RT on a rotary shaker. DSG was washed out and a second crosslink was performed using $1\%$ formaldehyde (Sigma-Aldrich, F8775) in PBS for 15 min at RT on a rotary shaker. Crosslinking was stopped with 0.125 M glycine for 5 min at 4 °C. After two washes with ice-cold PBS, nuclei were isolated by resuspending the tissue in 600 μL of ice-cold nuclei preparation buffer (NPB: 10 mM HEPES, 10 mM KCl, 1.5 mM MgCl2, 250 mM sucrose, $0.1\%$ IGEPAL CA-630) and incubating at 4 °C for 5 min in rotation. Nuclei were collected by centrifugation at 1,500 g for 12 min at 4 °C and, and resuspended in 600 μL of cold nuclear lysis buffer (10 mM Tris pH 8, 1 mM EDTA, 0.5 mM EGTA, $0.3\%$ SDS, 1× cOmplete™ Protease Inhibitor Cocktail, Roche) for 30 min on ice. Nuclear lysates were stored at −80 °C. 300 μL of lysates were sonicated using a Bioruptor Pico Sonicator (Diagenode) for 15 cycles (30 s ON/30 s OFF). Chromatin fragments (100–500 bp) were evaluated on agarose gels using 10 μL of sonicated chromatin for DNA purification using the phenol method. 600 μL of ice-cold ChIP-dilution buffer ($1\%$ Triton X-100, 2 mM EDTA, 20 mM Tris pH 8, 150 mM NaCl, 1 mM PMSF, 1× cOmplete™ Protease Inhibitor Cocktail, Roche) was added to the fragmented chromatin, and $10\%$ volume was recovered as the Input. Immunoprecipitation was set up overnight at 4 °C, by adding 20 μL of magnetic beads (Magna ChIP Protein G Magnetic Beads C #16-662, Sigma-Aldrich) and a combination of two anti-BMAL1 antibodies: 1.25 μL rabbit anti-BMAL1 (ab3350, Abcam) and 2.5 μL rabbit anti-BMAL1 (ab93806, Abcam) in 900 μL final volume. Immunoprecipitations with 4 μL of normal mouse IgG (Sigma-Aldrich, Cat. No. 18765) were performed simultaneously in 900 μL final volume. Sequential washes of the magnetic beads were performed for 10 min at 4 °C, as follows: Wash buffer 1 (20 mM Tris pH 8, $0.1\%$ SDS, $1\%$ Triton X-100, 150 mM NaCl, 2 mM EDTA), Wash buffer 2 (20 mM Tris pH 8, $0.1\%$ SDS, $1\%$ Triton X-100, 500 mM NaCl, 2 mM EDTA), Wash buffer 3 (10 mM Tris pH 8, 250 mM LiCl, $1\%$ IGEPAL CA-630, $1\%$ sodium deoxycolate) and TE buffer (10 mM Tris pH 8, 1 mM EDTA). Chromatin was eluted by adding 400 μL of fresh elution buffer (10 mM Tris pH 8, $0.5\%$ SDS, 300 mM NaCl, 5 mM EDTA, 0.05 mg/mL proteinase K) to the magnetic beads and incubating overnight at 65 °C. A treatment with RNase A at 0.1 mg/ml for 30 min at 37 °C was performed. The DNA was purified from the IPs and Inputs by adding one volume of phenol:chloroform:isoamamyl alcohol (25:24:1). After mixing and centrifugation, the aqueous phase was recovered, and DNA was precipitated by adding $\frac{1}{10}$ volumes of sodium acetate (0.3 M pH 5.2), 20 μg of glycogen (10901393001, Roche) and 2 volumes of ice-cold ethanol, at −80 °C overnight. DNA was pelleted by centrifugation at 12,000 × g for 30 min at 4 °C. The DNA was washed with $70\%$ ethanol, and resuspended in 50 μL of molecular-grade water. 1.5 μl were used for subsequent qRT-PCR reactions with specific primers designed using Primer3web v4.1.0, within regulatory regions previously identified as BMAL1 binding sites in mouse liver, as reported in the ChIP-Atlas database163. Primer sequences are available in Supplementary Data 5.
## Assessment of locomotor behavior
Mice were individually housed in a light-tight, ventilated cabinet, under a 12 h light:12 h dark cycle, and ad libitum access to food and water. At the appropriate time for each treatment, animals were removed from their cages to receive IP injections for less than 2 min each. Cages were equipped with two infrared motion sensors (OASPAD system, OMNIALVA). Beam break data were continuously recorded and compiled with the OASPAD20 (OMNIALVA) software, v2019, and files containing the number of beam breaks per 6-min bin were exported. Double-plotted actograms were generated using RhythmicAlly164. Activity profiles were obtained averaging 5 consecutive days prior to the NAD+ treatment, and 5 consecutive days after the start of the treatment. Activity profile data from 30 min were averaged for statistical comparisons.
## Statistics and reproducibility
All data were presented as the mean ± standard error of the mean, and two-way analysis of variance (ANOVA) followed by Tukey’s test for multiple comparisons was used for statistical analyses except when otherwise noted in the Figure legends. Differences between groups were rated as statistically significant at $P \leq 0.05.$ GraphPad Prism version 8.4.2.679 for Windows (GraphPad Software Inc., San Diego, CA, USA) and Excel (Microsoft Office 360, v2301) were used for statistical analyses and plotting. 24-h period rhythms were assessed employing CircWave version 1.4165, CircWave uses a forward linear harmonic regression to calculate the profile of the wave fitted into a 24 h period. Daily rhythms were confirmed when the null amplitude hypothesis was rejected by running an F test that produced a significant value ($P \leq 0.05$). CircWave provides the calculation of the Centre of Gravity (CoG), representing the acrophase of the curve, with SD. Double-plotted data (ZT24) for visualization proposes are indicated in Figure legends, and were not included in the statistical analyses. Data from live mice were replicated in two independent experiments with similar results. Figures were assembled using Adobe Illustrator CC 2015 (Adobe Inc., San José, CA, USA).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37286-2.
## Source data
Source Data
## Peer review information
Nature Communications thanks Charles Brenner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: 'The cholesterol-lowering effect of statins is modified by LILRB5 intolerance
genotype: Results from a recruit-by-genotype clinical trial'
authors:
- Aleksi Tornio
- Margherita Bigossi
- Moneeza K. Siddiqui
- Gwen Kennedy
- Ala’a Melhem
- Mehul K. Chourasia
- Cyrielle Maroteau
- Roberto Pola
- Daniel I. Chasman
- Alexander S. F. Doney
- Colin N. A. Palmer
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10043296
doi: 10.3389/fphar.2023.1090010
license: CC BY 4.0
---
# The cholesterol-lowering effect of statins is modified by LILRB5 intolerance genotype: Results from a recruit-by-genotype clinical trial
## Abstract
Background/Aims: Statin intolerance leads to poor adherence to statin therapy, resulting in a failure to achieve desired cholesterol reduction and adverse outcomes. The LILRB5 Asp247Gly genotype has been identified as being associated with statin intolerance and statin-induced myalgia. We conducted a randomized clinical trial to examine its role in immune response through T regulatory cell aggregation and in achieving cholesterol reduction targets.
Methods: A double-blind, cross-over, recruit-by-genotype trial was undertaken. A total of 18 participants who had either the Asp247Asp (T/T) genotype or the Gly247Gly (C/C) genotype were recruited to the study. Participants were randomised to receive placebo or atorvastatin 80 mg daily for 28 days. Following a washout period of 3 weeks, they were then switched to the opposite treatment. Biochemical and immunological measurements as well as interviews were performed prior to and after both treatment periods. Within genotype group comparisons were performed using repeated measures Wilcoxon tests. Two-way repeated measures ANOVA with genotype and treatment as factors were used to compare changes in biochemical parameters between groups during placebo and atorvastatin periods.
Results: Individuals with the Asp247Asp genotype had a greater increase in creatine kinase (CK) compared to those with Gly247Gly genotype in response to atorvastatin ($$p \leq 0.03$$). Those with Gly247Gly genotype had a mean non-HDL cholesterol reduction of 2.44 ($95\%$ CI:1.59 – 3.29) mmol/L while in Asp247Asp genotype group the mean reduction was 1.28 ($95\%$CI: 0.48 – 2.07) mmol/L. The interaction between the genotype and atorvastatin treatment for total cholesterol ($$p \leq 0.007$$) and non-HDL cholesterol response was significant ($$p \leq 0.025$$). Immunological assessment showed no significant changes in aggregation of T regulatory cells by genotype.
Conclusion: The Asp247Gly variant in LILRB5, previously associated with statin intolerance, was associated with differential increases in creatine kinase and total cholesterol and non-HDL cholesterol-lowering response to atorvastatin. Taken together, these results suggest that this variant could have utility in precision cardiovascular therapy.
## Introduction
Statins, 3-hydroxy-3-methylglutaryl-coenzyme-A reductase inhibitors, are the most widely used lipid-modifying agents worldwide for prevention of cardiovascular diseases. Even though statins are generally well-tolerated, they are associated with muscle symptoms, ranging from common muscle pain (myalgia) to rarely muscle damage (myopathy) (Alfirevic et al., 2014), both typically reversible. Even mild muscle symptoms can negatively impact adherence to statin-therapy, limiting the benefit in real-world settings (Guyton et al., 2014; Rannanheimo et al., 2015). Poor adherence to statins limits efficacy and results in higher risk of major adverse cardiovascular events (MACE) through limited reduction in cholesterol.
Leukocyte immunoglobin-like receptor subfamily-B 5 (LILRB5) is a member of the leukocyte immunoglobin-like receptor (LILR) family, a group of receptors expressed on the surface of immune cells and exerting activating or inhibitory function. rs12975366T>C is a common (mean allele frequency 0.40) missense variant in LILRB5 resulting in an aspartate to glycine amino acid substitution (p.Asp247Gly). Asp247Gly was reported to be associated with circulating levels of Creatine kinase (CK) and lactate dehydrogenase (LDH), both markers of muscle breakdown. In 2017, the variant was reported to be associated with statin intolerance in a case-control study with increased odds of having raised creatine kinase (CK) and being non-adherent to statin therapy. The effect of the genotype was also found to be dominant. Those with the LILRB5 Asp247Asp (T/T) genotype had 1.34 times the odds of statin intolerance compared to those with the Gly247X (T/C or C/C) genotype (Siddiqui et al., 2017). The study suggested a potential role for the immune system in the development of statin intolerance and myalgia using both observational data and post hoc analyses of a clinical trial. The proposed hypothesis was that carriers of the Asp247Asp genotype had reduced expression of Foxp3+ T regulatory cells, which resulted in poorer muscle repair and regeneration compared to non-carriers (Fontenot et al., 2003; Rodriguez-Perea et al., 2015; Kuswanto et al., 2016). This effect was recently replicated in the ODYSSEY outcomes trial of statin-related adverse drug reactions (ADRs) (Murphy et al., 2022). While the observed effect on statin intolerance was replicated in observational, clinically adjudicated, and clinical trial datasets, the studies were retrospective in nature and mechanistic explanation for the association was not directly provided.
Therefore, we sought to examine the association of LILRB5 genotype with statin tolerance and non-HDL-lowering effect in a pilot prospective, recruit-by-genotype trial in healthy volunteers. Furthermore, we examine if there is a differential aggregation of CD4+/Foxp3+ T cells in response to treatment across genotypes.
## Study participants
Potential study participants, who had given their written informed consent to be contacted for research purposes, were identified in the Scottish Health Research register (SHARE) and genotyped for the LILRB5 Asp247Gly variant (rs12975366) (McKinstry et al., 2017). The main inclusion criteria were age between 40 and 69 years, being non-hypercholesterolemic and statin treatment naïve, having White European ethnicity, and being in generally good health. The main exclusion criteria were inability or unwillingness to consent to the study or comply with the protocol, significant disease, regular drug therapy, recent involvement (<30 days) in a clinical trial with investigational medicinal product, premenopausal females, and being a carrier of the rare variant of the CKM polymorphism rs11559024 (Wallace et al., 2016; Siddiqui et al., 2017).
A SHARE administrator first contacted subjects tentatively matching the inclusion and exclusion criteria via telephone and contact information of those interested to participate were passed on to the research group. A total of 19 participants were enrolled to the trial after giving a written informed consent. The health of the subjects was confirmed during a screening visit by clinical examination, laboratory tests, and medical history. None of the participants used any continuous medication.
## Study design and ethical approval
The study protocol was approved by the East of Scotland Research Ethics Service (record number 16/ES/0128). The study was a randomized, double-blind (to both genotype and treatment), cross-over study consisting of two phases with a minimum of 3 weeks washout period. In each phase, participants ingested either 80 mg atorvastatin (two over-encapsulated 40 mg atorvastatin tablets (Teva Pharmaceuticals Europe B.V) or matching placebo once daily for 28 days. The study drugs were manufactured, and randomisation performed by Tayside Pharmaceuticals (Ninewells Hospital & Medical School, Dundee, United Kingdom). The use of grapefruit juice and grapefruit containing products was prohibited for the duration of study. The participants were asked to avoid moderate to vigorous physical exercise 72 h prior to study visits. Both phases consisted of two visits to the study site, first on day 0 (baseline) and second on day 29. The visits included blood sampling, muscle symptoms questionnaire, and questions regarding any possible concomitant medications or adverse effects. Unused capsules were collected after each phase and counted. Study design is graphically presented in Supplementary Figure S1. The trial was registered at clinicaltrials.gov (NCT02984293).
## Biochemistry and flow cytometry
In both study phases, fasting serum and EDTA whole blood samples were collected on the visits on days 0 and 29. On both visits, full blood count, CK, cholesterol and triglycerides were assayed at NHS Tayside Blood Sciences routine clinical laboratory at Ninewells Hospital and Medical School, Dundee, United Kingdom. In addition, 10 ml K2-EDTA tubes were drawn for flow cytometry and plasma separation. Flow cytometry was performed from fresh whole blood samples only on days 29.
The proposed hypothesis that the Asp247Gly genotype differentially affects T regulatory cell aggregation in response to insults (including statin-induced muscle damage) was tested. For flow cytometry, we used the same definition for T regulatory cells as Rodríguez-Perea et al., i.e., CD4+ and FoxP3+ lymphocytes (Rodriguez-Perea et al., 2015). Anti-Human FoxP3 Staining Kit (cat. No 560131) and all other reagents were purchased from BD Biosciences and flow cytometry was performed per manufacturer’s instructions. In short, K2-EDTA whole blood was first lysed with Lysing solution (cat. No 349202) and Human BD Fc Block™ (cat. No 564220) was added. Samples were then stained for up to 30 min with APC-labeled anti-human CD4 (cat. No 555349). For intracellular staining, the cells were fixed and permeabilized using the Human FoxP3 Buffer Set (cat. No 560098) and stained with Alexa Fluor 488 –labelled anti-human FOXP3 (cat. No 560047). The cells were acquired on LSRFortessa Flow Cytometer (BD). Lymphocyte gate was defined by forward and side scatter parameters and 20,000 CD4 positive lymphocytes were acquired from each sample.
## Replication
Replication of findings was sought from the Tayside Bioresource, a large observational cohort based in Tayside, Scotland (McKinstry et al., 2017; Hebert et al., 2018; Siddiqui et al., 2022). This resources links community prescribing records, electronic medical records, and genetic biobanks in the Tayside region of Scotland. We used frequency of prescription encashment in an average of 9 years of follow-up as a proxy of adherence to therapy.
## Statistical methods
Baseline characteristics of genotype T/T and genotype C/C groups were compared using Fisher’s exact test for binary data and t-test for continuous data. Mean (SD) are reported for continuous variables with normal distribution, median (IQR) for continuous variables with non-parametric distribution, and n (%) for categorical variables.
Within-group comparison were evaluated with repeated measures Wilcoxon test. Two-way repeated measures ANOVA with genotype and treatment as factors was used to compare changes in cholesterol levels between groups during placebo and atorvastatin periods. Holm-Šidák methods were used to correct for multiple comparisons. Data from participants who did not complete both phases of the trial were excluded from all statistical analyses. Patients with missing values in either CK levels, total cholesterol, or HDL cholesterol at any of the prespecified measurement time points were not included in the corresponding statistical analysis. All analyses were conducted using R Core Team 2019 and GraphPad Prism 8 for Mac OS X version 8.2.1.
## Results
The characteristics of the subjects in the two genotype groups are shown in Table 1. Of the 19 subjects enrolled in the trial one withdrew due to personal reasons after screening, and one was lost in follow up after completing phase 1. There were no withdrawals due to adverse events.
**TABLE 1**
| Variables | Genotype C/C Gly247Gly (n = 9) | Genotype T/T Asp247Asp (n = 8) | p-Value |
| --- | --- | --- | --- |
| Sex, female (%) | 3 (33.3%) | 0 (0%) | N.S. |
| Age, years [mean (SD)] | 53.78 (3.87) | 56.88 (5.22) | N.S. |
| Weight, kg [mean (SD)] | 74.6 (11.30) | 80.9 (13.9) | N.S. |
| BMI, kg/m2 [mean (SD)] | 26.08 (4.61) | 27.61 (3.15) | N.S. |
| Systolic blood pressure, mm Hg [mean (SD)] | 128.11 (6.37) | 141.9 (14.05) | 0.018* |
| Diastolic blood pressure, mm Hg [mean (SD)] | 80.0 (2.92) | 84.88 (8.04) | N.S. |
| CK levels at screening, U/L [median (IQR)] | 113.0 (68.0, 168.5) | 194.5 (65.75, 292.3) | N.S. |
| Total cholesterol at baseline, mmol/L [mean (SD)] | 5.40 (1.18) | 5.09 (0.75) | N.S. |
| Non-HDL cholesterol at baseline, mmol/L [mean (SD)] | 3.96 (0.99) | 3.54 (0.96) | N.S. |
| Smoking status | | | N.S. |
| Current smoker | 2 (22.2%) | 2 (25%) | |
| Previous smoker | 4 (44.4%) | 2 (25%) | |
| Never smoker | 3 (33.3%) | 4 (50%) | |
| Alcohol Intake per Day | | | N.S. |
| <1 drink per day | 5 (55.6%) | 3 (37.5%) | |
| 1–5 drinks per day | 3 (33.3%) | 3 (37.5%) | |
| >6 drinks per day | 1 (11.1%) | 2 (25%) | |
| Physical Exercise on a Weekly Basis | 6 (66.7%) | 7 (87.5%) | N.S. |
| Medical history | | | |
| Cardiac disease | 0 | 0 | |
| Respiratory disease | 0 | 0 | |
| Gastrointestinal disease | 2 (22.2%) | 1 (12.5%) | |
| Musculoskeletal disease | 0 | 2 (25%) | |
A total of 17 individuals successfully completed the trial of which 9 belonged to the Gly247Gly (C/C) LILRB5 genotype and 8 to the Asp247Asp (T/T) genotype. The average age, weight and body mass index (BMI) were comparable across the two genotypes. Individuals with the Asp247Asp genotype had significantly higher systolic blood pressure (when not corrected for multiple testing), however diastolic blood pressures were not different across the two genotype groups. At baseline, CK levels were not significantly different, and neither were total cholesterol or non-HDL cholesterol levels. None of the other features tested were different across the two genotype groups. Physical activity defined as weekly exercise was $67\%$ in the Gly247Gly group and $87\%$ in the Asp247Asp group. Two individuals in the Asp247 group compared to zero in the 247Gly group had a history of musculoskeletal disease. A history of gastrointestinal disease was observed in two individuals with 247Gly genotype and one with 247Asp genotype. Overall, there was no history of cardiac or respiratory disease in the trial population.
## Changes in CK and compliance in response to atorvastatin therapy
We considered three indicators of intolerance or adverse reactions to statin therapy: elevations in CK, poor compliance according to returned capsule count, and reported muscular adverse events. CK levels were increased significantly during treatment compared to placebo in participants with the T/T or Asp247Asp genotype (p-value = 0.04, one-tailed p-value 0.03), while these were not significantly increased in those with the Gly247Gly genotype (Figure 1A; Supplementary Table S1). When comparing number of capsules returned, more atorvastatin than placebo capsules were returned overall, however, a non-significant trend was observed where those with the T/T genotype returned more atorvastatin capsules compared to placebo capsule in the same genotype group, and in comparison, to the number returned by those with the C/C genotype group (Figure 1B). There were no differences in reported intolerance that differed from baseline complaints of muscular pain. 5 out of 7 participants with the T/T genotype had complaints of non-treatment specific myalgia, while only 3 out of 8 participants with the C/C genotype had the same. However, reports of statin-specific myalgia were made by 2 participants, each belonging to different genotype groups.
**FIGURE 1:** *CK changes (left) and number of capsules returned by genotype and treatment group.*
## Gly247 carriers have better reduction in total cholesterol and non-HDL cholesterol compared to Asp247
Total cholesterol response to atorvastatin therapy differed by genotype. Average reduction in those with Gly247Gly (C/C) genotype was 2.70 ($95\%$ CI:1.85 – 3.55) mmol/L while in T/T it was 1.25 ($95\%$CI: 0.45 – 2.04) mmol/L. Statistical interaction between treatment and genotype was also significant ($$p \leq 0.007$$) (Figure 3; Supplementary Tables S2, S3). Similarly, non-HDL cholesterol response to treatment differed by genotype (Figure 2). Average reduction in the Gly247 genotype group was 2.44 ($95\%$ CI:1.59 – 3.29) mmol/L while in Asp247 group the average response was 1.28 ($95\%$CI: 0.48 – 2.07) mmol/L. The interaction between treatment and genotype was also significant ($$p \leq 0.025$$) (Supplementary Tables S4, S5).
**FIGURE 2:** *Total cholesterol (left) and non-HDL cholesterol response to placebo and atorvastatin therapy by rs12975366 genotype (Asp247Asp corresponds to T/T and Gly247Gly to C/C).*
## Changes in immune response—T regulatory cells
There was no significant difference in the CD4+/Foxp3+ T cells by genotype following statin therapy. While the results were inconclusive, the average number of cells observed for those with the Gly247 genotype were higher following treatment with statin compared to treatment with placebo. Conversely, the average number of cells observed for the Asp247 genotype group was lower following statin therapy compared to placebo (Figure 3).
**FIGURE 3:** *Flow cytometry results for CD4+/Foxp3+ T cells showing non-significant effects of T regulatory cell aggregation differentially by genotype and treatment groups.*
## LILRB5 Asp247Gly is associated with compliance to statin therapy in observational studies
To replicate the observed effects in large observational data, we used the Tayside bioresource where over $80\%$ of statin prescriptions are for simvastatin or atorvastatin. In a cohort of 8591 statin users, Gly247X (T/C or C/C) carriers were more likely than non-carriers to be adherent to their statin therapy (beta: 0.023, $$p \leq 0.04$$). This model was adjusted for adherence to statin therapy, changes in dosing regimens, duration of therapy, type 2 diabetes status and a history of cardiovascular disease.
## Discussion
We present the results of a recruit-by-genotype trial to prospectively characterise the effect of LILRB5 Gly247Asp genotype in healthy volunteers in terms of lipid response, muscle symptoms and T regulatory cell response to high dose atorvastatin therapy. The main finding of the current study was the differential response to atorvastatin in total cholesterol and non-HDL cholesterol. The Gly247Gly genotype had been previously shown to be protective against statin intolerance and myalgia (Siddiqui et al., 2017) compared to the Asp247Asp genotype. In our study the Gly247Gly genotype group similarly showed no significant post-statin increase in CK, whereas the Asp247Asp group did. Interestingly, participants with the Gly247Gly genotype also had better total and non-HDL cholesterol-lowering effect than the group with Asp247Asp genotype. There was also a significant interaction between treatment and genotype. The results were replicated in observational cohort of patients treated with statin therapy from the Tayside bioresources.
The two main limitations of this study were the limited sample size and the short study duration. Even though there was a trend of differential response in the average number of CD4+/Foxp3+ T cells between the genotype groups following treatment with statin compared to treatment with placebo, this effect was not significant, likely/possibly due to limited sample size. Further, the study participants were healthy and not suffering from cardiovascular diseases, which may influence the level of low-grade inflammation and modulate the immunological response to statin therapy as to attenuate genetic effects. While this might limit the generalizability of our findings, the age of the study subjects represents the typical age of patients prescribed statins (Melhem et al., 2021). Statin ADRs, and statin-induced myopathy among them, are more common in aged individuals and senescent animal models (Camerino et al., 2016; Arrigoni et al., 2017; Ward et al., 2019; Belch et al., 2021). The potential mechanism for the effect of LILRB5 on statin tolerance is based on the reduced expression of Foxp3+ T regulatory cells in Asp247Asp carriers, resulting in poorer muscle repair. The reduced expression of T-regulatory cells is also a known effect of aging (Ward et al., 2019). It is therefore possible that in an aging body the dual impact of reduced T regulatory cells and the interaction between this genotype and statin use has an even more profound effect. Given the duration of treatment in this trial, we are not able to make any assessment of the long-term effect of atorvastatin by genotype. The lack of a mechanistic pathway for the LILRB5 variant to show this effect is a limitation.
One of the main strengths of the current study is that it was performed in a prospective, placebo-controlled double-blind setting. The subjects and the investigators were blinded to both the genotype and the treatment phase during the trial. Moreover, we used a cross-over design, thus allowing to compare both subjective and objective measurements of atorvastatin effects within the same subjects compared to placebo. In order to limit interindividual variability, we recruited only white subjects of European ancestry who were healthy and not on any concomitant medications. The dose used in the current study was 80 mg atorvastatin, which is clinically relevant and represents the current guidelines in the UK for maximum therapeutic dose (National Institute for Health and Care Excellence, 2016). Moreover, the duration of 4 weeks of statin administration was long enough to reach steady state and to achieve significant changes in lipid levels. Thus, the current study can be considered to represent the clinical use of statin therapy initiation.
Functional characterization of the Gly247Asp genotype is limited. Previous evidence showed a dominant effect of the variant on CK levels [Asp247Asp (T/T) vs. Gly247Asp (T/C) + Gly247Gly (C/C)]. Gly247Asp is a common variant with a mean allele frequency of about $40\%$ in White Europeans; for a person to be homozygous for this allele, the likelihood is 0.16, i.e., $16\%$ of the population should have the Gly247Gly (C/C) genotype. On the other hand, $36\%$ should have the Asp247Asp (T/T) genotype and $48\%$ should have the Gly247Gly (T/C) genotype. We recruited only those homozygous for Gly247Asp [Asp247Asp (T/T) or Gly247Gly (C/C)], excluding heterozygous carriers, to increase the probability of detecting the effect of LILRB5 on statin tolerance and changes in T regulatory cells aggregation. Further studies are required to characterize the functional implications of this genetic variant.
Our observed effect of LILRB5 genotype on non-HDL cholesterol response is supported by previous findings from the Tayside bioresource. In the study by Melhem et al. non-HDL -cholesterol response was defined as lowest measured non-HDL cholesterol, after a minimum of 4 weeks of statin therapy up to a maximum of 6 months after commencement of statin therapy (Melhem et al., 2021). Individuals with the C/C genotype had an absolute reduction of 0.05 mmol/L (0.01,0.08, $p \leq 0.05$) greater than carriers of the T/T genotype. In a previous study, the LILRB5 Asp247Asp genotype was associated with increased risk of statin intolerance defined based on prescription patterns and raised CK levels or prescription patterns only (Siddiqui et al., 2017). Summary statistics from the UK Biobank show an effect of lower total, LDL and HDL-cholesterol for C variant carriers (Elsworth et al., 2020). However, these estimates are unadjusted for medication use. Our results are robust to the possibility of lower cholesterol at baseline as we have performed paired tests to control for intra-individual variability.
The role of this variant and indeed other immunological variants has not been explored in the context of pharmacokinetics and pharmacodynamics of statins. A combination of large observational studies and clinical trials would be required to confirm our findings. Further research is needed to establish if the effect of the LILRB5 genotype is driven by non-adherence to statin therapy or by a more direct effect on lipid response.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by 16/ES/0128. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AT, MKS, AD, CNAP designed the study, collected and interpreted the data. MB, CM, GK, collected, analysed and interpreted the data, AM, MKC cleaned and analysed data, RP, DC helped with data interpretation. MKS and MB wrote the first draft and revised subsequent drafts. All authors approved the manuscript. AT and MB contributed equally and are joint first authors while AD and CNAP contributed equally and are joint last authors.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1090010/full#supplementary-material
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|
---
title: Comparison of anthropometric parameters and laboratory test results before
and after the COVID-19 outbreak among Chinese children aged 3–18 years
authors:
- Wen-Hong Dong
- Tian-Miao Gu
- Bing-Quan Zhu
- Ying Shen
- Xin-Yu He
- Guan-Nan Bai
- Jie Shao
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043305
doi: 10.3389/fpubh.2023.1048087
license: CC BY 4.0
---
# Comparison of anthropometric parameters and laboratory test results before and after the COVID-19 outbreak among Chinese children aged 3–18 years
## Abstract
### Objective
To compare the physiological health of Chinese children around the COVID-19 lockdown.
### Methods
We extracted data on children's anthropometric and laboratory parameters from May to November in both 2019 and 2020 from the Health Checkup Center, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China. Overall, 2162 children aged 3~18 years without comorbidities in 2019 and 2646 in 2020 were assessed. Mann Whitney U tests were used to compare differences between the above health indicators before and after COVID-19 outbreak. Quantile regression analyses adjusted for age, sex and body mass index (BMI) were also used in analysis. Chi-square tests and Fisher's exact tests were used for comparing differences of categorical variables.
### Results
Compared with children examined in 2019 before the outbreak, children in 2020 had a higher median z score of BMI for age (−0.16 vs. −0.31), total cholesterol (TC, 4.34 vs. 4.16 mmol/L), low density lipoprotein cholesterol (LDL-C, 2.48 vs. 2.15 mmol/L), high density lipoprotein cholesterol (HDL-C, 1.45 vs. 1.43 mmol/L) and serum uric acid (290 vs. 282 μmol/L), and a lower hemoglobin (Hb, 134 vs. 133 g/L), triglycerides (TG, 0.70 vs. 0.78 mmol/L) and 25(OH)D (45.8 vs. 52.2 nmol/L), all $P \leq 0.05.$ No differences were identified for waist height ratio, blood pressure and fasting glucose (both $P \leq 0.05$). However, in regression models after adjusting, BMI, TC, LDL-C, blood glucose and sUA were positively correlated with year; while Hb, TG and 25(OH)D were negatively correlated with year (all $P \leq 0.05$). Accordingly, children in 2020 had a higher prevalence of overweight/obesity (20.6 vs. $16.7\%$, $P \leq 0.001$), hypercholesterol ($16.2\%$vs. $10.2\%$, $P \leq 0.001$), high LDL-C (10 vs. $2.9\%$, $P \leq 0.001$), hyperuricemia (18.9 vs$.15.1\%$, $$P \leq 0.002$$), vitamin D deficiency (22.6 vs. $8.1\%$, $P \leq 0.001$) and a lower prevalence of high TG (4.3 vs. $2.8\%$, $$P \leq 0.018$$) compared with children in 2019.
### Conclusion
In this real-world study, we found that long-term lockdown due to COVID-19 outbreak might cause adverse impact on children's metabolic health, which might increase their future risk of cardiovascular diseases. Thus, parents, health professionals, educationists, and caregivers should pay more attention to children's dietary pattern and lifestyle, especially in this new normal against COVID-19.
## Introduction
In December 2019, a global health crisis of a highly infectious respiratory disease caused by SARS coronavirus 2 (SARS-CoV-2), also known as Coronavirus Disease 2019 (COVID-19) broke out worldwide [1]. According to WHO's latest situation report, more than 754 million individuals were confirmed cases of COVID-19, and over 6.8 million deaths were attributable to the disease [2]. Due to the severity of the epidemic and changing variants of the virus, rigorous and intensive measures are taken globally to control the transmission and mortality of COVID-19. Though social distancing and restriction measures such as home quarantine, travel ban, and even lockdown of the whole city have effectively suppressed the spread of COVID-19 [3], the potential adverse impacts on the individual's health, especially on children's health warrants more attention.
People may change their dietary and lifestyle during the COVID-19 lockdown period (4–6). In an Italian survey [4, 5], the youths had lower adherence to the Mediterranean diet compared with people aged 18–30 years old, and $70.5\%$ of participants reported a reduced level of physical activity. In a study performed among 3,052 adults in the United States [6], social isolation induced the largest reduction in physical activity and significant increase in sedentary and screen time among adults who were previously physically active.
Similarly, children who were asked to stay at home and study online also experienced significant changes of lifestyle (7–9). In China, all kindergartens and schools were closed from January to the end of Autumn in 2020 during the lockdown [10], which may resulting lifestyles changes in three aspects. First, nutrition-balanced food provided by schools was discontinued and diverse family-cooked food was provided to children during the lockdown. Second, regular outdoor physical activity in school days were canceled after school closure. Third, children and adolescents were asked to study online at home, more screen time and more sedentary lifestyle are inevitable (7–9). However, how would the long-term COVID-10 lockdown influence children's physiological health, especially objective indicators of health such as anthropometric parameters and laboratory biochemical results, remains unknown.
To address the afore-mentioned issue, we therefore used real-world data extracted from the electronic health records from the Health Checkup Center of Children's Hospital, Zhejiang University School of Medicine, to investigate the impact of COVID-19 lockdown on children's physiological health, including bodyweight, blood pressure, fasting blood glucose, 25(OH)D, and lipid indicators, etc.
## Study design and participants
The present study was a retrospective study. Considering that the health impacts of COVID-19 lockdown would not take effects soon after the beginning of lockdown, and would not disappear the moment school re-opened, we extracted data between May (3 months after the beginning of lockdown) and November (3 months after the re-open of schools) in 2020 from the database of the Health Checkup Center. For comparison, data from the same period in 2019 was also retrieved. In analysis, children who stayed at home constantly regardless of the pandemic (aged <3 years), or had missing data on basic physical examination records such as height or weight, or with self-reported history of chronic diseases that would prohibit them from attending schools and being active regularly such as cancer, congenital heart disease, genetic metabolic disease, or diabetes mellitus were excluded. Eventually, 2,162 children from 2019 and 2,646 from 2020 were included. The study was approved by the Medical Ethics Committee of Children's Hospital, Zhejiang University School of Medicine [review number: 2021-IRB-185]. Since data used in this study was retrieved from electronic records in the hospital, and no additional physical harm were added to the participants due to this study, informed consent was waived by the Medical Ethics Committee.
## Measurements of health indicators
Children's age, sex, anthropometry parameters and laboratory test results were extracted from the hospital information system. Anthropometric parameters including height, weight, waist circumference, and blood pressure were measured by well-trained nurses. Blood samples were sent to the Department of Clinical Laboratory within 1 h after collection and tested according to established standards. Hemoglobin (Hb) was determined by colorimetry (Mindray BC-5310, Shenzhen, China); blood glucose and triglycerides (TG) was analyzed using hexokinase method and glycerol phosphate oxidase-peroxidase method, respectively (Beckman Coulter AU5800, USA); enzymatic color test methods were used for quantification of total cholesterol (TC), low density lipoprotein-cholesterol (LDL-C), and serum uric acid (sUA) (Beckman Coulter AU5800, USA). 25(OH)D was tested manually using ELISA (ImmnoDiagnosticSystem, UK). Results of all above-mentioned laboratory results were retrieved.
Children's weight status was evaluated by the Z score of BMI for age (zBMI). For children under 5 years, zBMI ≤-2, −2 <zBMI ≤2, 2 <zBMI ≤3 and zBMI>3 were defined as underweight, normal weight, overweight (OW) and obesity (OB), respectively. For children older than five, zBMI ≤-2, −2 <zBMI ≤1, 1 <zBMI ≤2 and zBMI>2 were defined as underweight, normal weight, OW and OB, respectively [11]. According to Meng's study [12], waist to height ratio (WHtR)>0.48 was referred as central obesity. Hypertension was defined if systolic and/or diastolic blood pressure was higher than $95\%$ of Chinese children of the same sex and age (see Table 1) [13].
**Table 1**
| Age, years | Male | Male.1 | Female | Female.1 |
| --- | --- | --- | --- | --- |
| | SBP, mmHg | DBP, mmHg | SBP, mmHg | DBP, mmHg |
| 3~ | 105 | 69 | 104 | 68 |
| 4~ | 107 | 70 | 105 | 69 |
| 5~ | 110 | 72 | 107 | 71 |
| 6~ | 112 | 74 | 110 | 73 |
| 7~ | 115 | 77 | 112 | 75 |
| 8~ | 117 | 78 | 115 | 77 |
| 9~ | 119 | 79 | 117 | 78 |
| 10~ | 120 | 80 | 118 | 80 |
| 11~ | 122 | 81 | 121 | 80 |
| 12~ | 124 | 81 | 122 | 81 |
| 13~ | 125 | 82 | 123 | 81 |
| 14~ | 127 | 83 | 123 | 82 |
| 15~ | 129 | 84 | 123 | 82 |
| 16~ | 130 | 85 | 123 | 82 |
| 17~ | 132 | 85 | 124 | 82 |
According to the WHO criteria, anemia was defined when Hb ≤110 g/L for children under 5 years' old; for children aged 5~11 years, anemia was defined when Hb ≤115g/L; for children aged 12~14 years and girls older than 15 years, Hb ≤120g/L was anemic; and for boys older than 15 years, Hb ≤130g/L was defined as anemia [14]. Vitamin D status was categorized as adequate (serum 25(OH)D >50.0 nmol/L), insufficiency (>37.5 and ≤50.0 nmol/L) and deficiency (≤37.5 nmol/L) [15]. TG≥1.70 mmol/l, TC≥5.18 mmol/L, LDL-C≥3.37 mmol/L, and HDL-C <1.04 mmol/L would be hypertriglyceridemia (hTG), defined as hypercholesteremia (hTC), high LDL-C (hLDL-C), and low HDL-C (lHDL-C) according to the Chinese Experts' Consensus [16]. Since there is a lack of uniform standards for hyperuricemia (hUA), sUA above 357 μmol/L was defined as hyperuricemia, as illustrated in an U.S. National Health and Nutrition Examination Survey finding [17].
## Statistical analysis
Continuous variables were described as Median and interquartile ranges since they were non-normally distributed; categorical variables were presented as numbers and percentages. The Mann Whitney U test was used to compare differences in health indicators between 2019 and 2020. Quantile regression models were applied to assess the independent influence of lockdown after the COVID-19 outbreak on children's health indicators by adjusting for children's age, sex and BMI when appropriate. We have also transferred continuous variables into categorical variables to understand the prevalence of OB, abdominal obesity, dyslipidemia, and vitamin D deficiency in children before and after the COVID-19 outbreak. Chi-square tests were used for detecting differences in the prevalence before and after the outbreak, and Fisher's exact tests were used when needed. All analyses were performed using SAS 9.2 and a two-sided $P \leq 0.05$ was considered as statistically significant.
## Results
Table 2 presents the physical measurements and laboratory test results of the study population. In total, 2,162 children ($60.5\%$ males) aged 3–18 years between May and November in 2019 and 2,646 children ($60\%$ males) from the same period in 2020 were compared and children who were examined in 2020 were slightly younger ($$P \leq 0.001$$). Compared with children examined between May and November in 2019, children examined in 2020 had a higher zBMI and HDL-C (both $P \leq 0.05$), especially in boys (both $P \leq 0.05$) than their counterparts examined in 2019. Higher TC, LDL-C, HDL-C and sUA level and lower hemoglobin, TG and 25(OH)D levels were observed in children examined 2020 (all $P \leq 0.05$). No significant difference of WHtR, SBP, DBP, or fasting glucose levels was observed ($P \leq 0.05$).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Year | Year.1 | Year.2 | Year.3 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Characteristics | 2019 | 2019 | 2020 | 2020 | P | |
| | N (n*/%) | M (P25–P75) | N (n*/%) | M (P25–P75) | | |
| Total | Age, years | 2,162 (0) | 8.9 (6–12) | 2,646 (0) | 8.3 (5.7–11.8) | 0.001 |
| Total | Males, yes | 1,307 (60.5) | / | 1588(60) | / | 0.758 |
| Total | zBMI | 2,612(0) | −0.31 (−1.07–0.59) | 2,646 (0) | −0.16 (−0.9–0.82) | <0.001 |
| Total | WHtR | 2,121 (41) | 0.43 (0.4–0.47) | 1,784 (862) | 0.43 (0.4–0.47) | 0.129 |
| Total | SBP, mmHg | 2,080 (82) | 101 (93–110) | 2,579 (67) | 101 (93–109) | 0.416 |
| Total | DBP, mmHg | 2,080 (82) | 62 (56–69) | 2,579 (67) | 62 (55–68) | 0.136 |
| Total | Hb, g/L | 2,146 (16) | 134 (128–141) | 2,508 (138) | 133 (127–139) | <0.001 |
| Total | TG, mmol/L | 1,672 (490) | 0.78 (0.62–1.04) | 2,020 (626) | 0.70 (0.55–0.95) | <0.001 |
| Total | TC, mmol/L | 1,672 (490) | 4.16 (3.73–4.68) | 2,020 (626) | 4.34 (3.89–4.87) | <0.001 |
| Total | LDL-C, mmol/L | 1,603 (559) | 2.15 (1.85–2.52) | 1,983 (663) | 2.48 (2.12–2.88) | <0.001 |
| Total | HDL-C, mmol/L | 1,603 (559) | 1.43 (1.26–1.62) | 1,983 (663) | 1.45 (1.28–1.65) | 0.007 |
| Total | 25(OH)D, nmol/L | 1,674 (488) | 52.2 (45.2–60) | 1,880 (766) | 45.8 (38.4–57.1) | <0.001 |
| Total | Fasting glucose, mmol/L | 1,614 (548) | 4.79 (4.54–5.04) | 1,953 (693) | 4.81 (4.56–5.05) | 0.175 |
| Total | sUA, μmol/L | 1,672 (490) | 282 (245–329) | 1,989 (657) | 290 (250–340) | 0.003 |
| Males | Age, years | 1,307 (0) | 9.0 (6.1–12) | 1,588 (0) | 8.6 (5.8–11.8) | 0.01 |
| Males | zBMI | 1,307 (0) | −0.25 (−1.06–0.76) | 1,588 (0) | 0.01 (−0.79–1.12) | <0.001 |
| Males | WHtR | 1,285 (22) | 0.3 (0.4–0.47) | 1,056 (532) | 0.44 (0.4–0.48) | 0.07 |
| Males | SBP, mmHg | 1,254 (53) | 102 (94–111) | 1,545 (43) | 102 (93–110) | 0.465 |
| Males | DBP, mmHg | 1,254 (53) | 62 (56–68) | 1,545 (43) | 62 (55–68) | 0.302 |
| Males | Hb, g/L | 1,299 (8) | 135 (129–142) | 1,518 (70) | 134 (127–141) | 0.013 |
| Males | TG, mmol/L | 1,047 (260) | 0.76 (0.6–1.02) | 1,252 (336) | 0.68 (0.53–0.92) | <0.001 |
| Males | TC, mmol/L | 1,047 (260) | 4.12 (3.69–4.64) | 1,252 (336) | 4.32 (3.87–4.87) | <0.001 |
| Males | LDL-C, mmol/L | 1,003 (304) | 2.12 (1.82–2.46) | 1,234 (354) | 2.44 (2.09–2.83) | <0.001 |
| Males | HDL-C, mmol/L | 1,003 (304) | 1.45 (1.27–1.64) | 1,234 (354) | 1.47 (1.29–1.68) | 0.038 |
| Males | 25(OH)D, nmol/L | 1,047 (260) | 52.8 (46.4–60.3) | 1,168 (420) | 47.3 (39.2–57.7) | <0.001 |
| Males | Fasting glucose, mmol/L | 1,003 (301) | 4.85 (4.59–5.08) | 1,221 (367) | 4.84 (4.61–5.1) | 0.68 |
| Males | sUA, μmol/L | 1,047 (260) | 286 (248–335) | 1,240 (348) | 293 (253–349) | 0.006 |
| Females | Age, years | 855 (0) | 8.6 (6–12) | 1,058 (0) | 8.1 (5.6–11.9) | 0.009 |
| Females | zBMI | 855 (0) | −0.41 (−1.08–0.36) | 1,058 (0) | −0.31 (−1.03–0.48) | 0.052 |
| Females | WHtR | 836 (19) | 0.42 (0.39–0.45) | 728 (330) | 0.42 (0.39–0.46) | 0.712 |
| Females | SBP, mmHg | 826 (29) | 100 (93–109) | 1,034 (24) | 100 (93–108) | 0.775 |
| Females | DBP, mmHg | 826 (29) | 62 (56–69) | 1,034 (24) | 62 (56–68) | 0.287 |
| Females | Hb, g/L | 847 (8) | 134 (128–139) | 990 (68) | 132 (126–137) | <0.001 |
| Females | TG, mmol/L | 625 (230) | 0.8 (0.65–1.06) | 768 (290) | 0.73 (0.58–0.98) | <0.001 |
| Females | TC, mmol/L | 625 (230) | 4.21 (3.77–4.73) | 768 (290) | 4.39 (3.95–4.87) | <0.001 |
| Females | LDL-C, mmol/L | 600 (255) | 2.21 (1.9–2.6) | 749 (309) | 2.53 (2.16–2.93) | <0.001 |
| Females | HDL-C, mmol/L | 600 (255) | 1.4 (1.25–1.58) | 749 (309) | 1.42 (1.26–1.61) | 0.075 |
| Females | 25(OH)D, nmol/L | 627 (228) | 51 (43.3–59) | 712 (346) | 46 (37–56) | <0.001 |
| Females | Fasting glucose, mmol/L | 608 (247) | 4.71 (4.48–4.94) | 732 (326) | 4.74 (4.5–4.98) | 0.104 |
| Females | sUA, μmol/L | 625 (230) | 274 (242–319) | 749 (309) | 285 (247–331) | 0.012 |
Table 3 presents the results of quantile regression models, it was found that BMI, TC, LDL-C, and sUA were positively correlated with year, while Hb, TG, and 25(OH)D were negatively associated with year (all $P \leq 0.05$) after adjusting for age, sex, and BMI when appropriate. The differences in WHtR, SBP, DBP, HDL-C and fasting glucose between 2019 and 2020 were not statistically significant (all $P \leq 0.05$).
**Table 3**
| Health indicators | N (n*) | Year (2020 vs. 2019) | Year (2020 vs. 2019).1 | Year (2020 vs. 2019).2 |
| --- | --- | --- | --- | --- |
| | | β | SE | P |
| BMI, kg/m2 | 4,808 (0) | 0.302 | 0.091 | 0.001 |
| WHtR | 3,905 (903) | 0.001 | 0.002 | 0.515 |
| SBP, mmHg | 4,659 (149) | 0.572 | 0.341 | 0.093 |
| DBP, mmHg | 4,659 (149) | 0.135 | 0.282 | 0.632 |
| Hb, g/L | 4,654 (154) | −1.006 | 0.298 | 0.001 |
| TG, mmol/L | 3,692 (1,116) | −0.071 | 0.012 | <0.001 |
| TC, mmol/L | 3,692 (1,116) | 0.177 | 0.029 | <0.001 |
| LDL-C, mmol/L | 3,586 (1,222) | 0.294 | 0.023 | <0.001 |
| HDL-C, mmol/L | 3,586 (1,222) | 0.018 | 0.012 | 0.147 |
| 25(OH)D, nmol/L | 3,554 (1,254) | −6.05 | 0.453 | <0.001 |
| Blood glucose, mmol/L | 3,567 (1,241) | 0.029 | 0.015 | 0.044 |
| sUA, μmol/L | 3,661 (1,147) | 9.754 | 2.739 | <0.001 |
Table 4 shows the prevalence of abnormal health conditions in both male and female children examined in 2019 and 2020, respectively. The prevalence of OW and OB, hTC, hLDL-C, vitamin D deficiency, and hUA were 20.6, 16.2, 10, 22.6, and $18.9\%$, respectively; and were higher in children examined in 2020 than those in 2019 (all $P \leq 0.05$). Meanwhile, the prevalence of hTG was slightly lower in 2020 than in 2019 ($P \leq 0.05$). As for the prevalence of abdominal obesity, hypertension, hyperglycemia, anemia and low HDL-C, no significant differences were identified in children examined in 2020 and 2019 ($P \leq 0.05$). Additionally, we found that the prevalence of vitamin D deficiency increased in both males (from 5.6 to $20.5\%$) and females (from 12.1 to $26.1\%$). Moreover, anemia and hTG prevalence decreased mainly in boys, while hUA prevalence increased mainly in girls.
**Table 4**
| Characteristics | Total | Total.1 | Unnamed: 3 | Male | Male.1 | Unnamed: 6 | Female | Female.1 | Unnamed: 9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 2019 | 2020 | P | 2019 | 2020 | P | 2019 | 2020 | P |
| | N (%) | N (%) | | N (%) | N (%) | | N (%) | N (%) | |
| zBMI | | | 0.002 | | | 0.003 | | | 0.046 |
| Underweight | 144 (6.7) | 135 (5.1) | | 92 (7.1) | 83 (5.2) | | 52 (6.1) | 52 (4.9) | |
| Normal weight | 1,657 (76.6) | 1,966 (74.3) | | 937 (71.7) | 1,099 (69.2) | | 720 (84.2) | 867 (82) | |
| Overweight | 245 (11.3) | 332 (12.5) | | 177 (13.5) | 229 (14.4) | | 68 (7.9) | 103 (9.7) | |
| Obesity | 16 (5.4) | 213 (8.1) | | 101 (7.7) | 177 (11.2) | | 15 (1.8) | 36 (3.4) | |
| Central obesity, yes | 361 (16.7) | 545 (20.6) | 0.274 | 273 (21.3) | 248 (23.5) | 0.195 | 107 (12.8) | 96 (13.2) | 0.82 |
| Hypertension, yes | 165 (7.9) | 221 (8.6) | 0.433 | 92 (7.3) | 133 (8.6) | 0.218 | 73 (8.8) | 88 (8.5) | 0.803 |
| Anemia, yes | 38 (1.8) | 38 (1.5) | 0.493 | 23 (1.8) | 12 (0.8) | 0.019 | 15 (1.8) | 26 (2.6) | 0.216 |
| hTG, yes | 71 (4.3) | 57 (2.8) | 0.018 | 49 (4.7) | 33 (2.6) | 0.008 | 22 (3.5) | 24 (3.1) | 0.682 |
| hTC, yes | 170 (10.2) | 327 (16.2) | <0.001 | 94 (9) | 206 (16.5) | <0.001 | 76 (12.2) | 121 (15.8) | 0.055 |
| hLDL-C, yes | 47 (2.9) | 198 (10) | <0.001 | 22 (2.2) | 116 (9.4) | <0.001 | 25 (4.2) | 82 (10.9) | <0.001 |
| lHDL-C, yes | 79 (4.9) | 73 (3.7) | 0.065 | 49 (4.9) | 43 (3.5) | 0.097 | 30 (5) | 30 (4) | 0.378 |
| Vitamin D | | | <0.001 | | | <0.001 | | | <0.001 |
| Deficiency | 135 (8.1) | 425 (22.6) | | 59 (5.6) | 239 (20.5) | | 76 (12.1) | 186 (26.1) | |
| Insufficient | 556 (33.2) | 700 (37.2) | | 338 (32.3) | 446 (38.2) | | 218 (34.8) | 254 (35.7) | |
| Sufficient | 983 (58.7) | 755 (40.2) | | 650 (62.1) | 483 (41.3) | | 333 (53.1) | 272 (38.2) | |
| Hyperglycemia, yes | 18 (1.1) | 29 (1.5) | 0.335 | 13 (1.3) | 23 (1.9) | 0.271 | 5 (0.8) | 6 (0.8) | 0.996 |
| hUA, yes | 252 (15.1) | 376 (18.9) | 0.002 | 194 (18.6) | 270 (21.8) | 0.055 | 58 (9.3) | 106 (14.2) | 0.005 |
## Discussion
In this retrospective study using real-world evidence from the Children's Hospital, we found that after at least 3 months of lockdown, children were more likely to be OW/OB, have higher serum TC, LDL-C, fasting glucose and UA levels, and more likely to be vitamin D deficient and anemia compared with their counterparts from the same period in 2019. Meanwhile, the TG level and prevalence of high TG were slightly lower in 2020.
The increasing prevalence of OW and OB in children after the COVID-19 outbreak we found in our study was consistent with previous studies conducted in other countries (18–22). In European countries such as Spain and Poland [18, 19], 25.8~$35\%$ of children's body weight increased during COVID-19 lockdown. In Palestine [20], as high as $41.7\%$ of adolescents gained more weight. In the United States, zBMI of children has increased by 0.056 after 2 months of school closure [21]. The situation of children's health status during the lockdown in China was not exceptional [22]. In a cohort study conducted in Henan, China [22], $28.1\%$ of children aged 7~12 years with normal weight before the COVID-19 outbreak developed into OW or OB, while $42.4\%$ of OW children before COVID-19 developed into OB.
Poor dietary choices, more sedentary and screen time as well as less physical activity might be the explanation for children's weight gaining during the COVID-19 lockdown. In a retrospective survey including 10,082 school-aged youths from 31 provinces in China [7], increases in consumption of wheat products, staple foods, and preserved vegetables were observed. Poor diet quality during COVID-19 lockdown was reported to be associated with an increase in BMI [19]. Moreover, after the implementation of the “classes suspended, learning continues” policy during the lockdown in China, e-learning became the only-option for school-aged children and adolescents, which inevitably led to higher exposure to digital screens and increased time of sedentary behaviors. In a study performed in Shanghai, China, screen time in 7–12 years old children has increased from 0.67 h/day before the COVID-19 outbreak to 5.24 h afterwards [8]. Androutsos et al. found that the prevalence of children having excessive screen time, i.e., ≥2 h/day, increased from $23.4\%$ before lockdown to $68.4\%$ after, and $66.9\%$ children reported decreased time spent in physical activity [9].
We have also observed an increase in TC, LDL-C, fasting glucose and sUA levels and a decrease in TG level after the COVID-19 outbreak in our study, similar with two other findings [23, 24]. In one study carried out in around 100 Korean children with paired data on lipid parameters before and after the COVID lockdown [23], the average levels of LDL-C and uric acid increased by 6.2 and 0.3 mg/dL during the COVID-19 period, respectively. In another study performed in 312 children with type 1 diabetes in India [24], children with dyslipidemia increased from $30\%$ before COVID outbreak to $51\%$ during COVID restrictions.
Apart from the decreasing TG, all the other changes, including higher BMI, indicated metabolic disorder. It was reported that [25] even a reduction in daily physical activity of 3 days influenced glycemic control, and physical inactivity of 2 weeks influenced lipid profile and lean mass, especially in overweight participants. Inconsistent with the two afore-mentioned studies [23, 24]. TG levels in children examined in 2020 decreased in our study. Though we did not collect dietary intake and physical activity information in the present study, it is well-established that increasing consumption of saturated fatty acid and low-quality carbohydrates can result in increasing cholesterols, while decreasing energy contribution from carbohydrates can lower TG level [26, 27]. In a recent study, unfavorable dietary changes such as increases in fat/oil intake and decreases in fruit intake during the 2020 COVID-19 pandemic were reported [28]. However, researches on changes of energy source and distribution during lockdown are still limited and more profound studies are warranted.
A higher prevalence of vitamin D deficiency after months of lockdown in 2020 was also identified in our study, consistent with findings in the Korean children [23]. The decrease of 25(OH)D levels in children might be caused by inadequate sunlight exposure due to less outdoor physical activity during the lockdown period. Additionally, a greater increase of vitamin D deficiency was found in boys than in girls, which may be explained by higher exposure to sunlight in boys than in girls before the COVID-19 pandemic [15, 29] and an undifferentiated reduction of sun exposure due to the lockdown. As a crucial nutrient in children's bone health and a protective factor for metabolic health, adequate vitamin D consumption should be considered, especially when sufficient sun exposure was not available.
Our study did not confirm significant changes in children's blood pressure. Inconsistently, in a study performed in 445 children from Henan, China [22], 46.6 % of children with normal blood pressure before the COVID-19 outbreak turned to be children with pre- or elevated blood pressure, and the prevalence of hypertension increased from $20.9\%$ at baseline to $34.6\%$. However, the sample size in this study was relatively smaller, and only children aged 7~12 years were included. Besides, the percentage of children with OW/OB ($65.6\%$) before the outbreak in this study was much higher than that in our study ($16.7\%$). It was proved that OB was significantly associated with an increased risk of essential hypertension [30].
Although there are lots of studies on the adverse impact of COVID-19 on children's lifestyle change, our study was one of the few pieces of real-world evidence that using hospital-based health records to investigate the impacts of COVID-19 lockdown on children's physiological health. By doing so, we avoided recall bias because all anthropometric parameters were measured by well-trained health professionals and the laboratory indexes were detected according to established standards. In addition, our study has included an extensive set of health indicators such as weight, height, waist circumference, systolic and diastolic blood pressure, lipid parameters, serum 25(OH)D level, sUA, and fasting blood glucose, which can comprehensively reflect the metabolism of children.
The study has several limitations warranting for attentions as well. First, selection bias may exist since all subjects were from hospitals but not communities, which may limit the generalization of the study conclusion. Second, children having their health checkups in 2020 were slightly younger than those in 2019, which might compromise the homogeneity of comparison. However, we have adjusted age inregression models. Thus, findings in our study may not be mistakenly estimated. Third, we did not collect information on children's diets, 24-h physical activity or adverse events occurred during COVID-19 pandemic. Although further analysis on factors associated with physiological change was infeasible, insufficient understanding of these contributing factors should have little change on our conclusion.
## Conclusions
In this real-world study, we observed adverse changes in children's weight status, cholesterols, sUA and vitamin D levels after months of COVID-19 lockdown. Since childhood obesity and metabolic abnormality were proved to be risk factors for future cardiovascular diseases and type 2 diabetes [31], we call for attention from health professionals, educational practitioners, policymakers, and other stake-holders as well as parents on this issue. As reported by UNICEF, $91\%$ of students worldwide – around 1.6 billion children and young people were affected by the COVID-19 pandemic [32]. Considering physical inactivity pandemic is getting worse [33], we recommend a more nutrition-balanced diet and sufficient physical activity for children to keep healthy and fit in this new normal against COVID-19.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors upon request, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Children's Hospital, Zhejiang University School of Medicine. Written informed consent for participation was not provided by the participants' legal guardians/next of kin because: Data used in this study was retrieved from electronic records in the hospital. By the time this study was conducted, all participants have finished their health checkup and left the hospital. Considering no additional physical harm were added to the participants due to this study, informed consent was waived by the Medical Ethics Committee of Children's Hospital, Zhejiang University School of Medicine [Review number: 2021-IRB-185].
## Author contributions
WHD, TMG, GNB, and JS: study concept, design, and drafting of the manuscript. WHD, TMG, BQZ, and YS: acquisition of data. WHD, TMG, BQZ, YS, XYH, GNB, and JS: statistical analysis, interpretation of data, and critical revision of the manuscript for important intellectual content. JS: obtaining funding and study supervision. BQZ and JS: administrative, technical, or material support. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Myofibroblast Ccn3 is regulated by Yap and Wwtr1 and contributes to adverse
cardiac outcomes
authors:
- Michael A. Flinn
- Santiago Alvarez-Argote
- Makenna C. Knas
- Victor Alencar Almeida
- Samantha J. Paddock
- Xiaoxu Zhou
- Tyler Buddell
- Ayana Jamal
- Reiauna Taylor
- Pengyuan Liu
- Jenny Drnevich
- Michaela Patterson
- Brian A. Link
- Caitlin C. O’Meara
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10043314
doi: 10.3389/fcvm.2023.1142612
license: CC BY 4.0
---
# Myofibroblast Ccn3 is regulated by Yap and Wwtr1 and contributes to adverse cardiac outcomes
## Abstract
### Introduction
While Yap and Wwtr1 regulate resident cardiac fibroblast to myofibroblast differentiation following cardiac injury, their role specifically in activated myofibroblasts remains unexplored.
### Methods
We assessed the pathophysiological and cellular consequence of genetic depletion of Yap alone (Yapfl/fl;PostnMCM) or Yap and Wwtr1 (Yapfl/fl;Wwtr1fl/+;PostnMCM) in adult mouse myofibroblasts following myocardial infarction and identify and validate novel downstream factors specifically in cardiac myofibroblasts that mediate pathological remodeling.
### Results
Following myocardial infarction, depletion of Yap in myofibroblasts had minimal effect on heart function while depletion of Yap/Wwtr1 resulted in smaller scars, reduced interstitial fibrosis, and improved ejection fraction and fractional shortening. Single cell RNA sequencing of interstitial cardiac cells 7 days post infarction showed suppression of pro-fibrotic genes in fibroblasts derived from Yapfl/fl,Wwtr1fl/+;PostnMCM hearts. In vivo myofibroblast depletion of Yap/Wwtr1 as well in vitro knockdown of Yap/Wwtr1 dramatically decreased RNA and protein expression of the matricellular factor Ccn3. Administration of recombinant CCN3 to adult mice following myocardial infarction remarkably aggravated cardiac function and scarring. CCN3 administration drove myocardial gene expression of pro-fibrotic genes in infarcted left ventricles implicating CCN3 as a novel driver of cardiac fibrotic processes following myocardial infarction.
### Discussion
Yap/Wwtr1 depletion in myofibroblasts attenuates fibrosis and significantly improves cardiac outcomes after myocardial infarction and we identify Ccn3 as a factor downstream of Yap/Wwtr1 that contributes to adverse cardiac remodeling post MI. Myofibroblast expression of Yap, Wwtr1, and Ccn3 could be further explored as potential therapeutic targets for modulating adverse cardiac remodeling post injury.
## Introduction
Following cardiac injury such as myocardial infarction (MI), progressive fibrosis can contribute to adverse ventricular remodeling and ultimately heart failure. Resident cardiac fibroblasts, which directly produce the pro-fibrotic response, are a heterogeneous population of stromal cells. When activated following injury, a subset of resident fibroblasts adopt a myofibroblast phenotype [1, 2] characterized by expression of Periostin (Postn) and alpha smooth muscle actin [αSMA [1]], increased production of extracellular matrix (ECM) proteins [primarily collagens [3, 4]], and secretion of metalloproteases [1], inflammatory chemokines, and cytokines [5, 6]. While myofibroblast function is critical for wound contraction, initial scar formation, and to prevent catastrophic heart rupture [1, 7], extended myofibroblast proliferation and activation can lead to persistent matrix protein secretion causing exacerbated scar formation, myocyte uncoupling, decreased cardiac compliance, and progressive heart failure [5]. There is currently an un-met need to identify molecular mediators of myofibroblast activity so that we can therapeutically target these pathways and regulate the fibrotic response following cardiac injury.
The Hippo-Yap/Wwtr1 pathway (herein referred to as “Hippo-Yap”) is a highly conserved signaling pathway consisting of core protein kinases; Stk3, Stk4, Lats1, and Lats2, and scaffolding proteins; Sav1, Mob1a, and Mob1b, whose kinase activity suppresses function of the transcriptional co-activators Yap and Wwtr1 [8]. Phosphorylation of Yap and Wwtr1 by the Hippo pathway kinases prevents nuclear localization and promotes phospho-degradation of Yap and Wwtr1, thereby inhibiting nuclear transcriptional activity. It has been demonstrated in mice Lats$\frac{1}{2}$ deletion specifically in resident cardiac fibroblasts promotes Yap and Wwtr1 activity, driving proliferation and cell fate transition to a myofibroblast state in a cell autonomous manner and resulting in deleterious fibrosis in mice [9, 10]. Inhibiting the Hippo-Yap pathway by verteporfin following myocardial infarction reduces fibrosis and injury size [11]. Furthermore, depletion of Yap/Wwtr1 in Tcf21 or Collagen (Col1a2 or Col11a1) positive cells attenuated pathological remodeling and improved cardiac function [9, 10, 12, 13]. However, Tcf21, Col1a2 and Col11a1 target all fibroblast populations and the role of endogenous Yap and Wwtr1 in differentiated cardiac myofibroblasts specifically has not been investigated. Here, we take the novel approach of investigating the role of endogenous Yap/Wwtr1 expression in activated cardiac myofibroblasts (Postn+) following MI. We demonstrate a cooperative role for myofibroblast Yap and Wwtr1 in scar size, interstitial fibrosis, and cardiac function post MI.
The downstream pathways modulated by Hippo-Yap activity in myofibroblasts are poorly understood. We found that depletion of Yap/Wwtr1 in myofibroblasts both in vivo and in vitro strongly suppresses expression of the secreted matricellular protein Ccn3 (also known as nephroblastoma overexpressed— Nov), suggesting Ccn3 expression might contribute to adverse outcomes post MI. Indeed, global administration of recombinant CCN3 to mice post MI significantly exacerbates adverse cardiac function and scarring. CCN3 signaling to myocardial tissue post MI promoted expression of pro-fibrotic genes. Collectively, we identify a novel factor expressed downstream of Yap/Wwtr1 in cardiac fibroblasts that independently promotes adverse cardiac wound healing dynamics.
## Animals
The following mouse lines from The Jackson Laboratory were utilized: stock 027929 Yapfl, 029645 PostnMCM, 030532 Yapfl; Wwtr1fl, and 004077 Rosa26-eGFPfl. Sprague Dawley neonatal rats (Charles River Laboratories) were used for cardiac fibroblast cell isolation. Adult mice were euthanized by administration of isoflurane and subsequent thoracotomy, neonatal mice and rats were euthanized by decapitation.
## Isoflurane administration
During echocardiography, surgical MI, or euthanasia, adult mice were anesthetized via inhalation of $1\%$–$3\%$ isoflurane vaporized with compressed O2 at a flow rate of 1 L/min.
## Myocardial infarction
Six- to ten-week-old mice were used for adult MI studies. The left anterior descending (LAD) coronary artery was ligated using the “Rapid Method” originally described by Gao et al. [ 14]. Briefly, a 1–2 cm incision was made in the skin on the left lateral side of the thorax. The heart was retracted from the thoracic cavity via the 5th intercostal space and a surgical needle with 6-0 prolene suture was inserted under the LAD artery. The vessel was then tied off to create a permanent infarction. The heart was returned to the thorax and the chest wall compressed along the sternal midline to force out excess air. The outer incision of the skin was closed using monofilament nonabsorbable nylon suture. The mouse was removed from anesthesia and returned to a cage to recover under a warming lamp. Immediately following surgery animals were administered 1.5 mg/kg slow-release Buprenorphine and the following day received oral administration of 5 mg/kg Meloxicam.
P6 neonatal MI surgeries were performed as described in Mahmoud et al. [ 15]. Briefly, methods were similar to that of adult MI except P6 mice were anesthetized on ice. Following surgery, mice recovered under a heat lamp. When animals were sternal and active, they were returned to their mother.
## Tamoxifen treatment
Mice of all genotypes were treated with tamoxifen to induce Cre expression and excision of floxed genes following MI. Cre negative controls received identical tamoxifen dosing. For neonatal mice, tamoxifen (Sigma) was dissolved into a mixture of $25\%$ ethanol and $75\%$ sunflower oil at a concentration of 1.5 mg/ml and injected subcutaneously at a dosage of 30 mg/kg. Postnatal day 6 (P6) neonatal mice were administered with tamoxifen 1, 3, and 5 days post injury (dpi). For adult mice, a diet of 0.4 g/kg tamoxifen citrate chow (Envigo) was provided ad libitum starting immediately after MI and continued for the duration of the study.
## CCN3 mouse administration
Following MI in 8 week-old C57/B6J mice, recombinant CCN3 (R&D Systems 1640-NV- 050) was resuspended in PBS at a concentration of 0.01 µg/µl, and 6 µg/kg was administered by intraperitoneal injection following surgical MI (Tuesday) and subsequently three times weekly (Monday, Wednesday, Friday) as described in Riser et al. [ 16] over the course of 28 days. At this concentration, intraperitoneal injection of CCN3 was shown to localize to the heart, liver, and kidneys and to elicit renal phenotypes but not overt adverse effects in mice [16]. PBS was administered by intraperitoneal injection to vehicle control animals.
## Echocardiography
Echocardiograms were obtained from adult mice using a Vevo 770 with an RMV 707 transducer or Vevo 3,100 with an MX550D transducer. Scans were taken of the parasternal long axis and short axis at the papillary muscle level in triplicate and measured in a genotype or treatment blinded manner. We calculated ejection fraction (%EF) based on measurements from tracing the circumference of the LV chamber in long axis mode during systole and diastole. Measurements based on internal diameters in short axis mode at the mid-papillary level were used to calculate fractional shortening (%FS), left ventricular internal diameter during diastole (LVID-d), and systole (LVID-s).
## Histological analysis
Hearts were fixed in $10\%$ formalin for 48 h at room temperature prior to processing and paraffin embedding. Sectioning was performed starting at the apex and progressing towards the base of the heart in 250 µM steps. 4 µM sections were collected at each level. Gömöri trichrome staining was performed to assess scar size. Slides were scanned using a Super Coolscan 9,000 (Nikon) or for higher resolution slides were scanned using a NanoZoomer 2.0-HT (Hamamatsu Photonics K.K.). For scar size quantification, four sections starting at 1,000 µm from the apex and proceeding every 500 µm toward the papillary muscles were averaged to quantify fibrotic area and midline percentage using MIquant [17]. Interstitial fibrosis was quantified from five representative images in the remote region of the left ventricle and quantified using Fiji ImageJ colorimetric analysis. Immunohistology was evaluated using a Nikon A1 confocal microscope or Eclipse 80i fluorescent microscope (Nikon) and Panda sCMOS camera (PCO). Immunostaining was performed with antibodies described in Supplementary Table S1. Click-iT (Thermo Fisher) 5-ethynyl-2′-deoxyuridine (EdU) staining was performed according to the manufacturer's recommendations. Two to five regions within the scar were averaged per heart to assess EdU incorporation in non-myocyte scar associated cells. Wheat germ agglutinin (WGA) staining was used to identify the cell surface of cardiomyocytes to assess cross sectional area from five representative images within the remote region of the left ventricle. For denatured collagen assessment, serial sections of the left ventricle containing robust scar regions (∼2,000 µm from the apex) were treated with collagen hybridizing peptide with Cy3 conjugate (3Helix) according to the manufacturer's recommendations. Representative images of the injury were quantified for presence of Cy3 vs. scar area. Yap and Wwtr1 staining was assessed by confocal microscopy and intensities were quantified by mean grey value via ImageJ in either the cytoplasm or nuclei, delineated by DAPI, in GFP+ cells. Confocal microscopy was used to verify measured nuclei were inside of GFP+ cells.
## siRNA knockdown in cultured rat cardiac fibroblasts
Primary cardiac fibroblasts were isolated from 2-day-old rats using a neonatal heart dissociation kit (Miltenyi Biotec) according to the manufacturer's protocol, followed by percoll gradient separation. 24 h after plating, cultured cells were transfected for 48 h with 25 nM siRNAs designed against Yap and/or Wwtr1, or with universal negative control siRNAs (Supplementary Table S2) as detailed in Flinn, et al. [ 18]. Untreated cells were cultured in Dulbecco's modified eagle medium (DMEM, Life Technologies) supplemented with $7.5\%$ fetal bovine serum without siRNA.
## 3H thymidine incorporation in cultured rat cardiac fibroblasts
Forty-eight hours post siRNA transfection cells were treated with DMEM containing 3H thymidine for DNA synthesis quantification. Cells were exposed to 3H thymidine treated media for 16 h, fibroblasts were then washed with PBS and lysed with 0.1 M NaOH + $0.1\%$ Sodium Dodecyl Sulfate (SDS). 3H thymidine incorporation was quantified on the LS 6,500 MultiPurpose Scintillation Counter (Beckman Coulter).
## CCN3 administration to cultured rat cells
Twenty-four hours after plating P2 neonatal rat cardiac fibroblasts or cardiomyocytes, cells were serum starved for 1 h and then administered CCN3 at either 0.1 µg/ml or 1.0 µg/ml or PBS as a control. Cells were incubated for 5 h after which they were collected and stored in TRIzol (Thermo Fisher) for RNA extraction.
## Single cell RNA sequencing (scRNAseq)
To identify differentially expressed genes (DEGs) in cardiac myofibroblasts between PostnMCM and Yapfl/fl;Wwtr1fl/+;PostnMCM in vivo we performed scRNAseq on interstitial cells from hearts following MI. Both strains carried R26-eGFP transgene. MI was performed on 8–10 week old mice as described above and animals were put on tamoxifen citrate chow immediately following surgery. At 7 dpi hearts were extracted and retrograde perfused with 50 ml of 1 mg/ml Collagen type II to digest hearts. Following digestion, atria and valves were removed and ventricular tissue was resuspended in PBS and filtered through a 40 µm cell strainer to remove cardiomyocytes and undigested tissue. Single cells were resuspended in 1 ml of ACK (ammonium-chloride-potassium) lysis buffer (Thermofisher Cat# A1049201) for 2 min at room temperature and washed with 2 ml of FACS buffer— $1\%$ fetal bovine serum, $0.1\%$ NaN3 Dulbecco's Phosphate Buffered Saline (DBPS) without Calcium and Magnesium (Lonza). Cells were resuspended in 4 ml of FACS buffer for cell counting, which was performed using the LUNATM automated cell counter (Logos Biosystems) using $0.4\%$ trypan blue (Thermofisher Cat# T10282) and the LUNATM cell counter bright field feature. Subsequently, samples were resuspended to a concentration of one million cells per 100 µl of FACS buffer. Samples were stained with 3 µM of [4′,6-Diamidino-2-Phenylindole, Dilactate (DAPI, Biolegend Cat# 422801)], in a volume of 1 ml of DAPI-FACS buffer per sample for 15 min. Then samples were washed with 1 ml of FACS buffer and resuspended again in 1 ml of FACS buffer for cell sorting. Samples were sorted using BD FACS Aria II Cell Sorter (BD Bioscences) and collected in 3–5 ml of FACS buffer.
Single cell capture, cDNA synthesis, barcoding, and library preparation was performed using the 10x Chromium system using V3.1 chemistry according to the manufacturer's recommendation (10x Genomics). Each sample was loaded onto a single lane of a Chromium Next GEM chip G to target 3,000 cells per sample. Cells were captured in single GEMs and lysed followed by cDNA synthesis using 12 amplification cycles, followed by library construction per manufacturer's protocol. An i7 multiplex single index kit was used to generate the libraries over 14 cycles of sample index PCR. Fragment size of cDNA and libraries was assessed using Agilent's 5,200 Fragment Analyzer System.
## scRNAseq data analysis
Libraries were sequenced at the Roy J. Carver Biotechnology Center at the University of Illinois, Urbana Champaign on a NovaSeq 6,000 using one S4 lane with 2X150nt reads. Samples were demultiplexed using Cell Ranger v6.1.1 (10X Genomics). A custom reference was made using NCBI's GRCm39 genome and Annotation Release 109, along with Cloning vector pEGFP-1 (GenBank: U55761.1) and SA-betageo synthetic construct [full details of modifications in Supplemental R file, [19]]. The “cellranger count” pipeline with default parameters was run separately on each sample to call cells and collapse reads to unique molecular identifiers (UMIs). Both samples were combined using “cellranger aggr” with “–normalize = none”.
The UMI counts per gene for all called cells were read into R [v4.1.2, [20]] and analyzed using Seurat [v4.0.6, [21]]. Genes were filtered out if they did not have at least 1 UMI in at least 20 cells, leaving 16,855 genes. Initial quality control involved performing sctranform normalization [22], principal components analysis, shared nearest neighbor cluster calling, and Uniform Manifold Approximation and Projection [UMAP, [23]] dimension reduction (hereafter referred to as the “Seurat pipeline”). One cluster of cells had extremely high percentage of UMIs in mitochondrial genes (likely dead/dying cells) and two other clusters had extremely low numbers of genes detected and total number of UMIs (likely stripped nuclei). These 3 clusters were removed completely along with a few other cells that had total numbers of UMIs > 54,742 or percentage of UMIs in mitochondrial genes >4.58 (thresholds set at 6 median absolute deviations). The remaining cells were re-run through the Seurat pipeline to create the final clustering and dimension reduction.
*Marker* genes per cluster were found by recursively comparing each cluster's cells against all other clusters combined using a Wilcoxon Rank Sum test. Within each cluster, gene expression differences between Yapfl/flWwtr1fl/+;PostnMCM and PostnMCM cells were tested also using a Wilcoxon Rank Sum test. The cells that expressed Postn were overwhelmingly in one cluster, so this one cluster was run by itself through the Seurat pipeline to find sub-clusters of cells. Sub-cluster marker genes and within-subcluster Yapfl/flWwtr1fl/+;PostnMCM and PostnMCM DEGs were calculated as before. Full R codes for all Seurat analyses are in the Supple Methods.
## Western blot
Cultured rat cardiac fibroblasts or mouse left ventricles were collected in RIPA buffer. Rat cardiac fibroblasts were collected 48 h post siRNA transfection. Protein lysates were combined with Laemmli buffer and separated on a $4\%$–$15\%$ Mini-PROTEAN TGC precast gel (Bio-Rad) by electrophoresis. Proteins were then transferred to a 0.45 µm pore size nitrocellulose membrane (Bio-Rad). Western blots were processed according to Li-Cor's Near-Infrared Western Blot Detection protocol and blocked using TBS based Intercept buffer (Li-Cor Biosciences). Protein detection was performed using an Odyssey-CLx infrared imager (Li-Cor Biosciences). Uncropped blots are provided in the supplemental data.
## RNA extraction and qRT-PCR
Twenty-four hours post siRNA transfection media was changed to DMEM with $7.5\%$ FBS, and forty-eight hours later cells were collected for gene expression analysis by either qRT-PCR or RNAseq. CCN3 administered cells were collected 5 h after treatment. Cells were washed in PBS and collected in TRIzol for RNA extraction according to the manufacturer's recommendations. For qRT-PCR analysis RNA was reverse transcribed using a high-capacity cDNA reverse transcription kit (Applied Biosystems). qRT-PCR was performed with SybrGreen (Invitrogen) and primers listed in Supplementary Table S3 and amplification was detected on the QuantStudio 6 Flex (Thermo Fisher). Gene expression was normalized to 18s ribosomal RNA expression and analyzed in QuantStudio (Thermo Fisher).
## Bulk RNAsequencing
Two methods were used to attain bulk RNAseq data. First, primary cardiac fibroblasts were isolated from 2-day-old rats, treated with siRNA or a universal negative control, and RNA was extracted from cells using TRIzol extraction protocol according to the manufacturer's recommendations. This process was repeated with separate litters to achieve 3 biological replicates per group. RNA quality was determined using an Agilent BioAnalyzer. RNA libraries were prepared by BGI Americas. Sequencing was performed using a DNBSEQ-G400 platform at 20 M reads per sample. Adapter sequences were removed from the output sequence and reads with low base quality (<13) were further trimmed using Trim_Galore v0.6.5 (Babraham Bioinformatics). Trimmed reads were then aligned to the rat genome (rn6) using Hisat2 v 2 2.1. Transcripts were assembled from RNA-seq alignments using Stringtie2 v2.1.5. Expression was quantified by fragments per kilobase of transcript per million reads mapped (FPKM). DEGs for each experimental group, as compared to the negative control, were detected using DESeq2.
Second, left ventricles were obtained from 4 dpi adult mice treated with either 6 µg/kg CCN3 or PBS daily starting at 1 dpi. RNA qas extracted from homogenized tissue by TRIzol extraction. RNA libraries were prepared by the Roy J. Carver Biotechnology Center at the University of Illinois with the Kapa Hyper mRNA library kit (Roche) and sequenced with a NovaSeq 6,000 with V1.5 sequencing kits. Fastq files were generated and demultiplexed with the bcl2fastq v2.20 Conversion Software (Illumina). Salmon version 1.4.0 was used to quasi-map reads to the GRCm39 transcriptome (NCBI) and quantify the abundance of each transcript. Data was normalized by removing unwanted variation by a factor of 2 [24]. *Differential* gene expression analysis was performed using the edgeR-quasi method using a model of treatment + 2 RUV factors plus False Discovery Rate (FDR) correction on the P-values.
For both methods, analysis using the Ingenuity Pathway Analysis (IPA, Qiagen) and Database for Annotation, Visualization and Integrated Discovery [DAVID, [25]] was performed.
## Statistics
Data were analyzed using Prism 8.2.0 (GraphPad). Two-way ANOVA followed by Tukey's multiple comparisons tests were performed on samples with two experimental factors. For data series consisting of two experimental factors assessed at multiple timepoints, a repeated measures Two-way ANOVA was performed followed by Sidak multiple comparisons test. Statistical comparisons between two groups were analyzed by Student's t-test, or between three or more groups by one-way ANOVA followed by Tukey's or Dunnett's multiple comparisons test. Welch's correction was utilized when the variance differed significantly in groups. Assessment of survival curves was performed using a Logrank Mantel-Cox test. Error bars in graphical data represent standard error.
## Study approval
All protocols in these studies were approved by the local Animal Care and Use Committee and conform to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health.
## Yap and Wwtr1 are expressed in myofibroblasts after MI
After an ischemic injury, resident cardiac fibroblasts differentiate into myofibroblasts which aggregate at the site of injury as soon as 1 dpi with a robust presence around 4–7 dpi [1]. By comparison, uninjured hearts show little signs of myofibroblast differentiation (<$1\%$ of interstitial cells). To assess whether the Hippo-Yap pathway was active in myofibroblasts, we leveraged a myofibroblast specific and tamoxifen inducible Postn Cre [PostnMCM[1]] crossed to a transgenic reporter line containing a floxed stop codon prior an enhanced green fluorescent protein (eGFP) in the Rosa26 locus (R26-eGFPfl/+) to express eGFP in myofibroblasts following tamoxifen induction (Figure S1A). This reporter model facilitates reliable identification of myofibroblasts by eGFP expression in tissue sections. R26-eGFPfl/+;PostnMCM mice were crossed to Yap and Wwtr1 floxed mice to generate Yapfl/fl;Wwtr1fl/wt;R26-eGFPfl/+; PostnMCM reporter mice that are depleted for Yap and Wwtr1 expression in Postn expressing cells following tamoxifen administration (Figure S1B). We performed MI followed by tamoxifen treatment in both postnatal day 6 (P6) and in 8–10 week old adult mice to investigate expression patterns of Yap and Wwtr1 at two developmental timepoints (Figure 1A and Supplementary Figure S1C). At 7 dpi we identified a robust population of GFP + cells at the site of injury, but not in remote cardiac tissue, in R26-eGFPfl/+;PostnMCM mice (Figure 1B and Supplementary Figures S1 D,E). At both timepoints we observed nuclear Yap and Wwtr1 expression in GFP+ myofibroblasts in R26-eGFPfl/+;PostnMCM mice while Yapfl/fl;Wwtr1fl/+;R26eGF;PostnMCM mice showed marked decrease in nuclear Yap (Figures 1B,C) and Wwtr1 (Figures 1D,E) expression.
**Figure 1:** *Yap and Wwtr1 expression is detected in myofibroblasts after ischemic injury. (A) Experimental timeline of adult MIs and subsequent tamoxifen administration. (B–E) Representative confocal data of the scar region in R26-eGFPf/+;PostnMCM and Yapfl/fl;Wwtr1fl/+;R26-eGFPf/+;PostnMCM mice at 7 dpi. Scale = 100 µm. (B) Immunostaining with α-GFP and α-Yap antibodies and DAPI. Yellow arrows denote Yap co-localization in GFP positive cells, red arrows denote absence of Yap colocalization in GFP positive cells, white arrows denote nuclear localized Yap in GFP negative cells. (D) Immunostaining with α-GFP and α-Wwtr1 antibodies and DAPI. Yellow arrows denote Wwtr1 co-localization in GFP positive cells, red arrows denote absence of Wwtr1 colocalization in GFP positive cells, white arrows denote nuclear localized Wwtr1 in GFP negative cells. (C,E) Quantification of Yap and Wwtr1 signal from histological staining in adult mice following MI. n = 13 randomly selected GFP + cells. Unpaired student's t-test with Welch's correction. **P <0.01. ****P < 0.0001.*
## Depletion of Yap in myofibroblasts results in modest protection against ventricular dilation post MI
To test if Yap deletion in myofibroblasts influences cardiac function we performed MI on Yapfl/fl;PostnMCM and Yapfl/fl animals followed by tamoxifen administration provided ad libitum in chow. We found no difference between genotypes in cardiac function at baseline (3 days before MI) or at 3 dpi indicating similar degree of injury in both groups (Supplementary Figure S2A). At 28 dpi Yapfl/fl;PostnMCM animals had significantly smaller left ventricular internal diameters during diastole (LVID-d) compared to Yapfl/fl(Supplementary Figures S2A,B). LVID-s by comparison was not significantly different ($$P \leq 0.27$$). While the Yapfl/fl;PostnMCM genotype conferred modest protection against left ventricular dilation, %EF and %FS were not significantly different between Yapfl/fl;PostnMCM and Yapfl/fl animals (Supplementary Figure S2A) nor were heart weights or body weights (Supplementary Figures S2C,D). At the histological level we found no significant difference in CM cross sectional area between experimental and control animals, suggesting reduced ventricular dilation does not correlate with attenuated CM hypertrophy in our model (Supplementary Figures S2E,F). Together, these data illustrate that Yap depletion from myofibroblasts imparts a slight protection against adverse ventricular dilation after MI, but functional contractility parameters such as %EF and %FS remain comparable to control animals.
Considering Yapfl/fl;PostnMCM animals showed a slight improvement in ventricular dilation following injury, we sought to investigate if cell proliferation or scarring differences could be observed. To assess cell cycle activity, following MI we administered a single dose of EdU at 6 dpi, a time when Postn expression and myofibroblast proliferation is high in the heart [1, 2]. We found a $70\%$ decrease in EdU+ nuclei in the scar region of Yapfl/fl;PostnMCM mice compared controls (Supplementary Figures S2G,H). Based on the known role of Yap in promoting cell proliferation, we postulated that the significant reduction of scar associated cell EdU incorporation is the result of decreased myofibroblast proliferation. However, despite the reduction in purported myofibroblast proliferation, we did not observe any change in scar size after injury as assessed by both midline scar percentage and the percentage of collagen content within the left ventricle of Gömöri trichrome stained serial sections (Supplementary Figures S2I,J).
## Depletion of both Yap and Wwtr1 in myofibroblasts improves cardiac function followingMI
Yap and Wwtr1 share overlapping roles in various cell types. In some cases, depletion of Yap can be compensated by increased Wwtr1 activity and vice versa [26]. In vitro knockdown of either Yap or Wwtr1 in neonatal rat cardiac fibroblasts demonstrated a significant decrease in DNA synthesis compared to negative siRNA or non-treated cells while an additive effect was observed with knockdown of both genes suggesting redundant role for Yap and Wwtr1 in fibroblasts (Supplementary Figure S3). We hypothesized that depletion of both Yap and Wwtr1 in myofibroblasts would improve cardiac outcomes post MI compared to Yap depletion alone. To test this hypothesis, we used a genetic mouse model were both copies of Yap and a single copy of Wwtr1 are floxed, as both PostnMCM and Wwtr1 are located chromosome 3 in mice at approximately 3 million base pairs apart. *This* genetic linkage made generating a homozygous floxed Wwtr1 line with the Cre driver locus impractical, either alone or in combination with Yap.
We subjected Yapfl/fl;Wwtr1fl/+;PostnMCM or Yapfl/fl;Wwtr1fl/+ littermate controls to MI and subsequently fed a continuous diet of tamoxifen citrate chow (Figure 2A). Similar to the loss of Yap alone, depletion of Yap and Wwtr1 did not affect heart weight or body weight at 60 dpi (Figures 2B,C). While we found no difference in cardiac function between genotypes at baseline or 3 dpi, Yapfl/fl;Wwtr1fl/+;PostnMCM mice showed significantly improved cardiac function at 60 dpi compared to control (Figures 2D,E). While %FS and %EF declined to ∼$10\%$ and ∼$17\%$ respectively in Yapfl/fl;Wwtr1fl/+ animals, %EF and %FS in Yapfl/fl;Wwtr1fl/+;PostnMCM were maintained at ∼$18\%$ and ∼$35\%$ respectively. Both LVID-s and LVID-d also trended downward in Yapfl/fl;Wwtr1fl/+;PostnMCM mice at 60 dpi, but were not statistically different from control mice ($$P \leq 0.17$$ and 0.22 respectively, Supplementary Figure S4). In contrast, when allowed to progress to the 60 dpi timepoint, Yapfl/fl;PostnMCM still showed no difference in %EF or %FS or fibrosis compared to Yapfl/fl littermates and heart weight and body weight were not affected (Supplementary Figures S5–S7). We tested if the preservation in cardiac function in Yapfl/fl;Wwtr1fl/+;PostnMCM as compared to Yapfl/fl;Wwtr1fl/+ controls was the result of Cre expression in within myofibroblasts alone by repeating the MI study on PostnMCM transgenic mice and wildtype littermate controls. We found no difference in %FS or %EF between these genotypes indicating that preserved cardiac function is due to depletion of Yap and Wwtr1, and not due to Cre expression in myofibroblasts (Figure 2E). Together, these data illustrate that depletion of both Yap and Wwtr1 attenuate adverse cardiac remodeling and improve cardiac function after ischemic injury.
**Figure 2:** *Depletion of Yap and a single copy of Wwtr1 from myofibroblasts improves left ventricular function in response to ischemic injury. (A) Experimental timeline of echocardiography, MIs, tamoxifen chow administration, and EdU administration in adult Yapfl/fl;Wwtr1fl/+and Yapfl/fl;Wwtr1fl/+;PostnMCM mice. Quantification of (B) heart and (C) body weights at 60 dpi. n = 7 Yapfl/fl;Wwtr1fl/+ and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (D) Representative M-mode echocardiograms of left ventricles 60 dpi. Short axis view at mid papillary muscle. Horizontal bar = 500 ms. Vertical bar = 5 mm. (E,F) Quantification of %FS and %EF analyzed by repeated measures two-way ANOVA and Sidak multiple comparisons test. n = 7 Yapfl/fl;Wwtr1fl/+, 4 Yapfl/fl;Wwtr1fl/+;PostnMCM, 5 R26-eGFPf/+, 4 R26-eGFPf/+;PostnMCM. A separate subset of animals establishing a baseline for Yapfl/fl;Wwtr1fl/+ animals as denoted by the hashed lines and assessed by un-paired student's t-test. n = 4 Yapfl/fl;Wwtr1fl/+ and 5 Yapfl/fl;Wwtr1fl/+;PostnMCM. ns = not significant, *P < 0.05.*
Despite the improved cardiac function of Yapfl/fl;Wwtr1fl/+;PostnMCM mice after surgical MI, no difference in cardiomyocyte cross sectional area was observed between genotypes (Figures 3A,B). We next tested whether depletion of both Yap and Wwtr1 from myofibroblasts resulted in modulation of cell proliferation, scar formation, or fibrosis in our model. To quantify proliferation of cells within the scar region, adult mice subjected to MI were administered a single dose of EdU at 3 dpi. Consistent with results observed in Yapfl/fl;PostnMCMmice, Yapfl/fl;Wwtr1fl/+;PostnMCMmice exhibited a $60\%$ decrease in EdU+ scar associated cells compared to controls, whereas the percentage of EdU+ interstitial cells remained unchanged (Figures 3C,D and Supplementary Figure S8). However, unlike Yapfl/fl;PostnMCMmice which showed no difference in scar size at 28 or 60 dpi (Supplementary Figures S2I,J, S5, S6), Yapfl/fl;Wwtr1fl/+;PostnMCMmice displayed significantly reduced scar size as assessed by scar midline length ($38\%$ reduction) and fibrotic percentage of the left ventricle ($38\%$ reduction) (Figures 3E,F) and a $43\%$ reduction in interstitial fibrosis compared to control (Figure 3G,H). We further characterized scar composition by quantifying denatured collagen, which is more easily turned over and can reduce deleterious fibrosis in the heart, using collagen hybridizing peptide Cy3 conjugate (CHP) [27]. CHP binds to the unfolded triple-helix of collagen fibers, thus marking denatured collagen. CHP has been shown to correlate with other assays assessing degradation of collagen post MI such as matrix metalloproteinase activity in vivo and zymography [28, 29]. While scars from Yapfl/fl;PostnMCMmice showed no difference in CHP binding compared to controls (Figures 3I,J), Yapfl/fl;Wwtr1fl/+;PostnMCM mice displayed ∼2.5 fold increase in the amount of denatured collagen in the scar region as compared to Yapfl/fl;Wwtr1fl/+ littermates (Figures 3K,L). Thus, depletion of both Yap and Wwtr1 significantly modulated scar size and also collagen composition following MI.
**Figure 3:** *Depletion of myofibroblast Yap and Wwtr1 decreases fibrosis at 60 dpi. (A) Representative images of WGA staining in the remote zone of the left ventricle at 60 dpi. Scale = 50 µm. (B) Quantification of cardiomyocyte cross sectional area. n = 7 Yapfl/fl;Wwtr1fl/+ and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (C) Representative images denoting EdU+ nuclei within the scar area. White arrows indicate EdU+ scar associated nuclei. Scale = 100 µm. (D) Quantification of EdU+ scar associated nuclei. n = 6 Yapfl/fl;Wwtr1fl/+ and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (E) Representative serial sections of Gömöri trichrome stained hearts measured from the apex. Scale = 5 mm. (F) Quantification of infarct scar size by either total fibrotic area or midline size of the left ventricle. n = 7 Yapfl/fl;Wwtr1fl/+ and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (G) Representative images depicting interstitial fibrosis within the remote zone of Gömöri trichrome stained hearts. Scale = 50 µm. (H) Quantification of the % blue fibrotic area vs. total ventricular tissue. n = 7 Yapfl/fl;Wwtr1fl/+ and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (I,K) Representative images of CHP staining within the scar region of 60 dpi mice. Scale = 100 µm. (J,L) Quantification of CHP as a % area of the scar region. n = 4 Yapfl/fl, 3 Yapfl/fl;PostnMCM, 7 Yapfl/fl;Wwtr1fl/+, and 4 Yapfl/fl;Wwtr1fl/+;PostnMCM. Unpaired student's t-test. (M) Survival curve comparing Yapfl/fl;Wwtr1fl/+ and Yapfl/fl;Wwtr1fl/+;PostnMCM or R26-eGFPf/+ and R26-eGFPf/+;PostnMCMmice after MI until the 60 dpi end point. Animals that died during surgery were not included. n = 9 Yapfl/fl;Wwtr1fl/+, 7 Yapfl/fl;Wwtr1fl/+;PostnMCM, 8 R26-eGFPf/+, 10 R26-eGFPf/+;PostnMCM. Logrank Mantel-Cox test. Ns, not significant, *P < 0.05, and **P < 0.01.*
Prior studies have demonstrated that genetic ablation of Postn expressing cells (i.e., myofibroblasts) results in a stark decreased survival post MI due to lack of scar deposition and subsequent rupture [1]. Importantly, neither depletion of Yap and Wwtr1 nor expression of Cre itself significantly affect survival of infarcted mice (Figure 3M). Collectively, combined depletion of Yap and Wwtr1 in myofibroblasts attenuates fibrosis and improves functional outcomes following MI.
## Single cell analysis defines Yap and Wwtr1 downstream targets in cardiac myofibroblasts post MI
We next performed transcriptomic profiling on interstitial cells from PostnMCM and Yapfl/fl;Wwtr1fl/+;PostnMCM hearts post injury to identify transcriptional changes and differential infiltration of cell types between genotypes. At 7 days post MI, hearts were extracted, digested into single cell suspension, and FACS sorted for live nucleated cells. We targeted cDNA and library construction of 3,000 cells per genotype on the 10x Chromium Controller for sequencing. We obtained a total of 6,063 high quality sequenced cells, 2,930 from PostnMCM and 3,133 from Yapfl/fl;Wwtr1fl/+;PostnMCM hearts, at an average sequencing depth of 47,945 reads per cell. Cluster analysis of interstitial cells revealed 18 main populations consisting of neutrophils (clusters 1, 18), B cells (cluster 2), macrophages/monocytes (clusters 0, 3, 11, 12, 13), T cells (clusters 4, 7, 8, 10), fibroblasts (cluster 5), natural killer cells (cluster 14), and dendritic cells (cluster 17) (Figures 4A,B and Supplementary Tables S4, S5). Two small clusters (15 and 16) could not be identified or expressed mixed cell type markers. While cells from each genotype were represented in most clusters, clusters 6, 9, and 10 were primarily derived from Yapfl/fl;Wwtr1fl/+;PostnMCM (Figures 4C). Cluster 6 and 10 were enriched for proliferation markers (Top2a and Mki67), suggesting a population of proliferative T cells infiltrate myocardial injury in response to Yap/Wwtr1 myofibroblasts depletion.
**Figure 4:** *scRNAseq reveals Yap and Wwtr1 downstream targets in cardiac fibroblasts post MI. (A) UMAP projection and identification of combined interstitial cardiac cells, post filtering, from PostnMCM and Yapfl/fl;Wwtr1fl/+;PostnMCMadult mouse heart 7 dpi. Clusters are identified by enriched gene markers in Supplementary Table S4. (B) Heatmap of top 10 genes enriched within each cluster. (C) UMAP projection split by genotype and quantification of percent of cells from each genotype for each cell cluster. (D) Violin plots denoting UMI count for Yap, Wwtr1, or Postn for each cluster. (E) IPA analysis of predicted upstream regulators of upregulated or downregulated DEGs (Yapfl/fl;Wwtr1fl/+;PostnMCMvs. PostnMCM) in fibroblasts (cluster 5). (F) UMAP projection of re-clustering only fibroblasts (cluster 5) and identification of cardiac fibroblast subclusters. Epi., epicardial; Trascript., transcriptionally; Prolif., Proliferating; Myofib., myofibroblast; MFC, matrifibocyte. (G) UMAP projection split by genotype.*
We were primarily interested in fibroblast gene expression profiles in Yapfl/fl;Wwtr1fl/+;PostnMCM hearts following MI compared to control, as this comparison could provide insights into the mechanisms downstream of Yap and Wwtr1 that mediate adverse cardiac remodeling or fibrotic phenotypes. Notably, Yap and Wwtr1 expression was almost exclusively detected in fibroblasts (cluster 5) (Figure 4D). Differential expression analysis on fibroblasts (cluster 5) revealed 319 differentially expressed genes (DEGs) between Yapfl/fl;Wwtr1fl/+;PostnMCM and PostnMCM genotypes (Supplementary Table S6). Upstream regulator analysis of DEGs indicated fibroblasts from Yapfl/fl;Wwtr1fl/+;PostnMCM mice upregulated pathways related to proinflammatory cytokines (IFNγ, Stat1, Tnf) and downregulated pathways related to cell cycle activation (Myc [30] and Trim24 [31]) and pro-fibrotic cytokine activation such as IL4 [32] (Figure 4E). Furthermore, Yapfl/fl;Wwtr1fl/+;PostnMCM fibroblasts showed significant negative activation scores for Yap, Wwtr1, and Tead1 as upstream regulators (−0.427, P-value 1.56E−16; −1.026, P-value 7.05E−03; and −1.739, P-value 2.44E−07 respectively) compared to PostnMCM fibroblasts, indicating downregulation of reported Yap/Wwtr1/Tead1 target genes in conditional knockout fibroblasts (Supplementary Figure S9). Amongst the most strongly upregulated genes in fibroblasts from Yapfl/fl;Wwtr1fl/+;PostnMCM hearts included genes associated with collagen secretion and pro-tumorigenic fibroblast properties [Saa3, Mpp6, Pdgfra [33, 34]]. Amongst the most downregulated genes included proto-onco genes (Laptm4b [35], Clec3b [36]) and the myogenic marker, Desmin [Des [37]]. Of particular interest, the secreted matricellular protein Ccn3 was amongst the most strongly suppressed in Yapfl/fl;Wwtr1fl/+;PostnMCM fibroblasts (average Log2 fold change = −1.69, adjusted P-value 1.24 × 10−14). While CCN family members, Ccn1 [Cyr61 [38]] and Ccn2, [Ctgf [39, 40]] are well known transcriptional targets of Yap and Wwtr1, Ccn3 expression has not been previously linked to the Hippo-Yap pathway.
To improve resolution of fibroblast phenotypes, we re-clustered cells from fibroblast cluster 5 which resulted in 6 distinct clusters 0–5 (Figure 4F). Cells from both Yapfl/fl;Wwtr1fl/+;PostnMCM and PostnMCM genotypes populated clusters 3 and 5 while cluster 0, 2, and 4 were derived primarily from PostnMCM hearts and cluster 1 derived primarily from Yapfl/fl;Wwtr1fl/+;PostnMCM hearts (Figure 4G). Cluster 0 was highly enriched for anti-proliferative and pro-fibrotic IGF binding proteins [Igfbp5, Igfbp6 Igfbp3 [41, 42]] as well the epicardial marker Dmkn (41, 43–45). Cluster 2 was enriched for genes encoding chemotactic genes (Cxcl13, Cxcl1, Cxcl12, Apln), and Cluster 4 was strongly enriched for matrifibrocyte markers (Angptl7, Thbs1, Sfrp2) described by Forte et al. as well as Vegfc [46]. Thus, fibroblasts derived primarily from PostnMCM control hearts appear to be enriched for pro-fibrotic and pro-inflammatory genes and more closely resemble matrifibrocyte phenotype designed to support a rigid scar [2]. Cluster 1 was enriched for epicardial markers [Saa3, Mpp6 [46]] suggesting distinct activation of epicardial derived fibroblasts in Yapfl/fl;Wwtr1fl/+;PostnMCM hearts. Cluster 3 contained cells from both genotypes and was strongly enriched for genes described by Forte et al. as proliferating myofibroblasts [Cenpa, Hmgb2, Cdc20, Birc5, Cks2, Stmn1, Top2a, among others [46]]. Cluster 5 contained genes from both genotypes and was strongly enriched for genes encoding ECM components (Mfap4, Col8a1, Col14a1, Col15a1) and contractile proteins (Postn, Acta2, Tagln) suggesting a secretory myofibroblast phenotype [47]. Collectively, differential gene expression and marker analysis of fibroblast sub-clusters indicates fibroblasts from Yapfl/fl;Wwtr1fl/+;PostnMCM hearts are less proliferative and secretory, and display distinct inflammatory chemokines compared to PostnMCM hearts.
## Yap and Wwtr1 co-depletion synergistically modulates Ccn3 gene expression in cardiac fibroblasts
We next sought to address if gene expression changes observed in vivo in Yapfl/fl;Wwtr1fl/+;PostnMCM cells were associated with depletion of either Yap or Wwtr1 individually or required depletion of both factors. We performed siRNA mediated knockdown of Yap and/or Wwtr1 in cultured primary rat neonatal cardiac fibroblasts which resulted in greater than ∼$70\%$ depletion of Yap and Wwtr1 as verified by qRT-PCR (Figure 5A). Compared to the negative control and single knockdowns of either Yap or Wwtr1, we identified distinct genetic profiles for Yap/Wwtr1 double knockdown cells as evident by a principal component analysis (PCA) of DEGs (Figure 5B). A relatively large set of significantly DEGs ($P \leq .05$) with Log2 fold change (Log2FC) >2 or <−2 were unique to Yap/Wwtr1 double knockdown cells (252 genes) while Yap or Wwtr1 single knockdowns shared the majority of their DEGs (Figure 5C), suggesting high functional redundance of Yap and Wwtr1 in cardiac fibroblasts. A large gene cluster that was activated only when both Yap and Wwtr1 were knocked down contained genes primarily related to the immune response (Figure 5D). A gene cluster that was significantly downregulated in the Yap/Wwtr1 knockdown group contained genes primarily regulated by Tgfβ-1 and related to “hepatic fibrosis”, Stat3 pathway, and Th2 pathway— illustrating suppression of pro-fibrotic genes with depletion of Yap and Wwtr1. Gene ontology analysis of DEGs unique to Yap and Wwtr1 co-depletion were related to extracellular space (Figure 5E) indicating modulation of matrix or secreted proteins. We specifically measured expression of genes that were most strongly differentially expressed in the fibroblast cluster (cluster 5) from our in vivo scRNAseq experiment to determine if their expression was regulated by depletion of Yap, Wwtr1, or both Yap and Wwtr1. Out of the top five downregulated genes from the in vivo scRNAseq experiment, Ccn3 was the most highly expressed in negative siRNA treated cultured cardiac fibroblasts (Figure 5F). Of these genes, only Ccn3 was robustly suppressed with Yap + Wwtr1 siRNA mediated depletion (Figure 5G). While expression of most genes tested showed a synergistic (Cfb, Mgp, Prss23) or additive (B2m, Irf7, Isg15, Tnfrsf11b) response to Yap and Wwtr1 depletion, Anxa2 appeared primarily regulated by Yap while Ccn3 and S100a10 more strongly regulated by Wwtr1 (Supplementary Figures S10A,B). From these data we observed a synergistic role between Yap and Wwtr1 regulating gene expression in cardiac fibroblasts, with Ccn3 expression of particular interest given its prominence in the datasets and function as a matrix element.
**Figure 5:** *Gene expression following Yap and/or Wwtr1 depletion in vitro. (A) Relative RNA expression of Yap and Wwtr1 after siRNA knockdown in primary cardiac fibroblasts. n = 3 negative control, n = 4 Yap, n = 4 Wwtr1, n = 4 Yap + Wwtr1. Each data point represents a biological replicate derived from cells from independent pooled litters. One-way ANOVA comparing experimental groups to negative siRNA control by Dunnett's test. (B) PCA of DEGs from RNAseq from siRNA treated cells n = 3 per group. (C) Venn Diagram denoting unique and common DEGs with pAdj <0.05 and log2FC >2 or <−2 from Yap, Wwtr1, and Yap/Wwtr1 knockdown (D) Heat map showing relative DEGs with pAdj <0.05 and log2FC >2 or <−2. The top three canonical pathways and top three upstream regulators identified for each gene cluster by IPA are listed. (E) The top cellular component terms derived from genes uniquely differentially expressed after Yap/Wwtr1 knockdown. (F) Table depicting the top downregulated differentially expressed genes between Yapfl/fl;Wwtr1fl/+;PostnMCM and PostnMCM fibroblasts and the baseline expression in FPKM values for negative siRNA treated primary cardiac fibroblasts. (G) FPKM values of the genes depicted in (F) following siRNA treatment. Ns, not significant, *P < 0.05, and ***P < 0.001.*
## CCN3 administration contributes to adverse ventricular remodeling and fibrosis post MI in mice
Ccn3 was the most strongly downregulated gene in Yapfl/fl;Wwtr1fl/+;PostnMCM fibroblasts at 7 dpi (Supplementary Table S6 and Figures S11A,B) that was also significantly and robustly decreased following in vitro knockdown of Yap + Wwtr1 in cardiac fibroblasts, but not Yap knockdown alone (Figure 5G). To confirm our results from scRNAseq and bulk RNAseq of cultured fibroblasts, we repeated the siRNA knockdown experiment in neonatal rat cardiac fibroblasts and measured Ccn3 protein levels. In agreements with our RNAsequencing, knockdown of Yap alone showed no change in Ccn3 protein levels, whereas Yap + Wwtr1 knockdown significantly decreased Ccn3 protein expression (Figures 6A,B and Supplementary Figure S12). Ccn3 is a member of the CCN (Cyr61, Ctgf, Nov) family of secreted extracellular proteins [48]. While studies have linked Ccn3 to integrin and Notch1 mediated signaling [49] and prevention of renal fibrosis [16], the role of Ccn3 in the heart post injury is virtually unexplored. Ccn3 protein is more abundant in infarcted hearts at 14 dpi compared to uninjured hearts and its expression is decreased in Yapfl/fl;Wwtr1fl/+;PostnMCM compared to Yapfl/fl;Wwtr1fl/+ mice (Figure 6C and Supplementary Figure S13). These data mirror observations in human patients suffering from dilated cardiomyopathies, who also show elevated cardiac CCN3 expression [50]. Furthermore, Ccn3 expression is strongly enriched in cardiac fibroblasts when compared to other interstitial cells (Figures 6D and Supplementary Figure S14, Tables S5, S7) suggesting fibroblasts are the primary source of CCN3 in the heart. We hypothesized that Ccn3 downregulation in Yapfl/fl;Wwtr1fl/+;PostnMCM fibroblasts contributes to improved cardiac functional outcomes post MI, and therefore CCN3 overactivation would promote adverse cardiac remodeling. To test this hypothesis, we performed MI on adult C57/B6 mice and subsequently administered mice with either recombinant CCN3 or vehicle (PBS) by intraperitoneal injection 3 times per week, beginning at 1 dpi (Figure 7A). A prior study performed same protocol to investigate the role of CCN3 on fibrosis linked to diabetic nephropathy, demonstrating its efficacy in localizing to the heart following interperitoneally injection [16]. Strikingly, we found at just 3 dpi that compared to vehicle, mice receiving CCN3 already started to show a decline in cardiac function, and by 14 and 28 dpi cardiac function was substantially and significantly worse (Figure 7B and Supplementary Figure S15). LVID-s increased significantly with administration of CCN3 over the 28-day experiment while LVID-d trended towards being larger ($$P \leq 0.08$$ at 28 dpi, Figure 7B). Heart weight and body weight were not significantly different between groups (Figure 7C). Histological analysis at 28 dpi revealed significantly larger scars (Figures 7D,E) and increased proliferation of scar associated cells in response to CCN3 administration (Figures 6F,G) but no difference in cardiomyocyte cross sectional area (Figures 6H,I).
**Figure 6:** *Ccn3 abundance decreases with depletion of Yap/Wwtr1. (A) Western blots depicting protein abundance of Yap, Wwtr1, or Ccn3 following siRNA treatment in cultured neonatal rat cardiac fibroblasts with Gapdh loading controls. (B) Quantification of Ccn3 protein from neonatal rat cardiac fibroblasts following siRNA, normalized to non-treated samples. n = 3 biological replicates per group. Each replicate consisted of cells from independent litters. One-way ANOVA comparing experimental groups to negative siRNA control by Dunnett's test. (C) Western blot depicting Ccn3 abundance in left ventricles of uninjured and 14 dpi left ventricles. (D) Feature plot illustrating the abundance of Ccn3 expressing cells by UMI within clusters 5 and 16. Ns, not significant and *P < 0.05.* **Figure 7:** *Ccn3 administration promotes fibrotic gene expression post MI. (A) Experimental timeline of echocardiography, MIs, CCN3 administration, and EdU administration in adult mice. (B) Quantification of left ventricular function by fractional shortening, ejection fraction, and left ventricular internal diameters during diastole and systole at baseline. 3, 14, and 28 dpi timepoints were analyzed by repeated measures two-way ANOVA and Sidak multiple comparisons test. n = 10 PBS treated, 9 CCN3 treated. (C) Quantification of heart and body weights at 28 dpi. n = 10 PBS treated, 9 CCN3 treated. Unpaired student's t-test. (D) Representative serial sections of Gömöri trichrome stained hearts measured as distance from the apex. Scale = 5 mm. (E) Quantification of infarct scar size by either total fibrotic area or midline size of the left ventricle. n = 10 PBS treated, 9 CCN3 treated. Unpaired student's t-test. (F) Representative immunohistological images denoting EdU+ nuclei within the scar area. White arrows indicate EdU+ scar associated nuclei. Scale = 100 µm. (G) Quantification of EdU+ scar associated nuclei. n = 7 PBS treated, 6 CCN3 treated. Scale bar = 100 µm. Unpaired student's t-test. (H) Representative images and (I) quantification of WGA staining in the remote zone of the left ventricle. n = 9 PBS treated, 9 CCN3 treated. Scale = 100 µm. Unpaired student's t-test. ns, not significant. F-H Are from 28 dpi hearts. ns, not significant, *P < 0.05, **P < 0.01, *** P < 0.001, ****P < 0.0001.*
We next assessed whether CCN3 administration in mice following infarction resulted in transcriptional changes that promote adverse remodeling. We performed MI and administered CCN3 or PBS for 3 consecutive days starting at 1 dpi. Hearts were collected 4 h after the final injection and left ventricular tissue was processed for bulk RNAsequencing. RNAseq data was normalized to remove unwanted variation, resulting in distinct genetic profiles between CCN3 and PBS control treated animals (Figure 8A). We observed extracellular matrix associated genes were predominantly upregulated in ventricles of CCN3 treated mice while transcripts related to mitochondrial function were suppressed (Figures 8B–D). Thus, we illustrate CCN3 administration following injury drives fibrotic gene networks in myocardial tissue. Further assessment of DEGs by IPA was performed to identify which pathways were modulated. Tgfβ1, a well characterized promoter of fibrosis, was the most activated upstream regulator while Tead1, a transcription factor activated with Yap/Wwtr1 activity, was the most strongly inhibited (Figure 8F) [8, 51]. Interestingly, while genes mediated by Tead1 activity were predominantly suppressed, indicating a potential repression of Yap/Wwtr1 activity, the expression of the matrix associated genes and CCN family members, Ccn1 and Ccn2, were significantly increased (Figure 8G). Ccn1 is promoted by and subsequently drives Tgfβ1 activity, promoting fibrosis in the heart [52, 53] and our data suggests a novel role for exogenously administered CCN3 in contributing to Ccn1, Ccn2, and Tgfβ1 signaling in the heart and aggravating fibrotic remodeling post MI. We further assessed the cell type on which CCN3 is acting by treating neonatal rat ventricular cardiac fibroblasts or myocytes at two different dosages. At the higher dose, CCN3 drove expression of the pro-fibrotic genes Fn1, Serpine1, and Ccn2 as well as Yap in cardiac fibroblasts (Supplementary Figure S16). CCN3 administration to cardiomyocytes however significantly promoted expression of the cell cycle gene Aurkb, the hypertrophic gene Nppb, as well as the Hippo-Yap elements Ccn1, Ccn2, and Wwtr1 (Supplementary Figure S17). Together, our data implicates Ccn3 as an element promoting adverse remodeling.
**Figure 8:** *Ccn3 administration promotes fibrotic gene expression post MI. (A) PCA of DEGs from RNAseq from left ventricles. n = 3 per group. (B) Heatmap of 309 genes with one-way ANOVA FDR P-value <0.2. The top three canonical pathways identified for each gene cluster by IPA are listed. (C,D) The top cellular component terms derived from upregulated (red) or downregulated (blue) DEGs derived from DAVID analysis. (F) The top upstream regulators from all DEGs predicted by IPA. (G) The top upregulated and downregulated genes by fold-change mediated by Tead1 or Tgfβ1.*
## Discussion
A nuanced understanding of how myofibroblasts function during wound healing as they proliferate [2], migrate [54], secrete matrix and cytokines [55, 56], recruit immune cells [57], and facilitate a multitude of other roles is salient to understanding the complex nature of progressive heart failure. A therapeutic means to promote the beneficial nature of myofibroblasts (early matrix deposition after injury and recruitment of anti-inflammatory immune cells) while reducing deleterious aspects (latent fibrosis) would be beneficial to curbing heart failure. To this extent, our studies characterize a means by which myofibroblast proliferation and production of cell matrix genes is regulated by the Hippo-Yap pathway. However, how Hippo-Yap signaling in myofibroblasts affects cardiomyocyte function and whether it elicits an apoptotic response has yet to be determined.
Although the role of the Hippo-Yap pathway has been studied in progenitor epicardial cells and resident cardiac fibroblasts [9, 10, 12, 13], we take the novel approach of assessing the role of endogenous Yap and Wwtr1 expression specifically in myofibroblasts, the cell type responsible for the lion's share of matrix deposition following MI [1]. While deletion of Yap alone did not result in observable changes in scar size or fibrosis, co-disruption of Yap/Wwtr1 resulted in significantly improved cardiac function as well as reduced scar size, interstitial fibrosis, and increased denatured collagen. These results are similar to depletion of Yap and Wwtr1 in resident cardiac fibroblasts whereby Yap and Wwtr1 have been shown to regulate the transition of cardiac fibroblasts to a myofibroblast state [12]. However, as therapeutic strategies would likely be implemented after an injury event, once myofibroblast are already activated, our study indicates that inhibiting Yap/Wwtr1 or downstream functional mediators after the transition has already occurred would a reasonable approach.
We highlight the synergistic role of Yap and Wwtr1 in regulating gene expression in cardiac fibroblasts. Our in vivo data demonstrates a significant improvement in cardiac function in Yapfl/fl;Wwtr1fl/+;PostnMCM whereas minimal improvement was observed in Yapfl/fl;PostnMCM animals. However, these studies did not include depletion of Wwtr1 alone. Thus, Yapfl/fl;Wwtr1fl/+;PostnMCM phenotypes could be attributed to either Wwtr1 depletion alone or to Yap and Wwtr1 depletion, necessitating further studies to verify this. Our in vitro transcriptomic data elucidated strong synergy in gene regulation with depletion of both Yap and Wwtr1, and enabled us to identify genes differentially expressed in vivo in Yapfl/fl;Wwtr1fl/+;PostnMCM that were regulated by Wwtr1 or Yap + Wwtr1, but not by Yap alone. This cross-reference approach helped us prioritize candidate factors that might be mediating the effects of Yap/Wwtr1 in cardiac myofibroblasts.
Our in vitro and in vivo RNA sequencing experiments pointed to candidate genes whose function has been unexplored in the context of cardiac remodeling. Of these genes, Ccn3 was of particular interest. CCN family members consist of secreted extracellular proteins which have been shown to interact with extracellular matrix components such as Fbln1 and receptors such as integrins and Notch [49]. Other members of the CCN family include the well-studied and direct targets of Yap/Wwtr1 mediated transcription Ccn1 and Ccn2 [39, 40]. Literature on the interaction between Ccn3 and Hippo-Yap signaling is sparse, but data indicate Ccn3 expression correlates with Yap/Wwtr1 activity. Administration of the Yap/Wwtr1/Tead inhibitor verteporfin decreases CCN3 expression in cultured human dermal fibroblasts [58] while a decrease in the ratio of transcriptionally suppressed phosphorylated Yap to unphosphorylated Yap correlated with an increase in Ccn3 during fragmentation of murine ovaries [59]. Similar to our findings in injured mice, endomyocardial biopsies from patients with dilated cardiomyopathy show significantly increased expression of CCN3 [50]. In mice, Ccn3 knockout mutants display endocardial defects and delayed ventricular septum fusion during development, but are viable as adults [60]. Adult knockout mice exhibit cardiomyopathy denoted by hypertrophy and calcification of the septal wall, but not overt ventricular fibrosis [60]. To our knowledge, MI studies have not been performed in Ccn3 knockout mice. Overall, the consequence of Ccn3 expression on fibrosis across tissues is not well defined in the literature. While it has been documented in some models that Ccn3 reduces fibrosis via antagonism with Ccn2 and by extension Tgfβ1 signaling [58, 61], this is not always the case [50, 62, 63]. Indeed, we illustrate both Tgfβ1 signaling and Ccn1 expression are both increased in vivo with administration of CCN3. Furthermore, proliferation of interstitial scar cells following cardiac injury was increased. These results mirror those from Lin et al. where CCN3 has been shown to promote DNA synthesis in cultured human skin fibroblasts in the presence of FGF2 [64]. However, as systemic administration of CCN3 does affect the renal system and potentially other organ systems and various cell types, we cannot rule out off target effects such as hypertension that may indirectly impact reduced cardiac function we observed in our experiments. Future studies will be aimed at elucidating the how Yap/Wwtr1 modulates Ccn3 expression in cardiac fibroblasts and the collective mechanisms by which Ccn3 contributes adverse cardiac remodeling.
Together, our work illustrates the intrinsic function of Yap and Wwtr1 in myofibroblast activity which promotes fibrosis and deleterious remodeling of the left ventricle after injury. We demonstrate that Ccn3 expression is regulated by Yap and Wwtr1, and CCN3 administration substantially contributes to adverse cardiac function post MI. As such, the Hippo-Yap pathway, Ccn3, or other downstream elements expressed in cardiac myofibroblasts could be attractive targets for modulating adverse remodeling following MI.
## Data availability statement
Data from the bulk in vitro RNAseq, bulk left ventricular RNAseq, and scRNAseq have been uploaded to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) and can be accessed under GSE185368, GSE217925, and GSE204712 respectively.
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Ethics statement
The animal study was reviewed and approved by The Institutional Animal Care and Use Committee of the Medical College of Wisconsin.
## Author contributions
Conceptualization: CCO, MAF, MP, and BAL; Methodology: MAF, SA-A, JD, and XZ; Investigation: MAF, SA-A, JD, MCK, VAA, SJP, XZ, TB, AJ, and RT; Formal analysis: MAF, SA-A, JD, MCK, VAA, SJP, XZ, TB, AJ and RT; Writing - original draft: MAF, CCO, SA-A, and JD; Writing - review & editing: MAF, SA-A, MCK, VAA, SJP, XZ, TB, AJ, PL, JD, MP, BAL, and CCO; Supervision: CCO, MP, BAL, and PL; Project administration: CCO; Funding acquisition: CCO, BAL, MAF, MP, and SJP. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1142612/full#supplementary-material.
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|
---
title: The 20 years transition of clinical characteristics and metabolic risk factors
in primary liver cancer patients from China
authors:
- Yezhou Ding
- Mingyang Feng
- Di Ma
- Gangde Zhao
- Xiaolin Wang
- Baoyan An
- Yumin Xu
- Shike Lou
- Lanyi Lin
- Qing Xie
- Kehui Liu
- Shisan Bao
- Hui Wang
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10043326
doi: 10.3389/fonc.2023.1109980
license: CC BY 4.0
---
# The 20 years transition of clinical characteristics and metabolic risk factors in primary liver cancer patients from China
## Abstract
### Background
The clinical characteristics of primary liver cancer (PLC) patients are changing, maybe due to hepatitis viral vaccination and lifestyle changes, etc. The linkage between these changes and outcomes among these PLCs has not yet been fully elucidated.
### Methods
It was identified total of 1691 PLC cases diagnosed between 2000 ~ 2020. Cox proportional hazards models were utilized to determine the connections between the clinical presentations and their close risk factor(s) from PLC patients.
### Results
The average age of PLC patients increased gradually from 52.74 ± 0.5 years in 2000 ~ 2004 to 58.63 ± 0.44 years in 2017 ~ 2020, accompanied by an increased proportion of females from $11.11\%$ to $22.46\%$, and non-viral hepatitis-related PLC was raised from $1.5\%$ to $22.35\%$. 840 ($49.67\%$) PLC patients with alpha-fetoprotein (AFP) < 20ng/mL (AFP-negative). The mortality was 285 ($16.85\%$) or 532 ($31.46\%$) PLC patients with alanine transaminase (ALT) between 40 ~ 60 IU/L or ALT > 60 IU/L. The PLC patients with pre-diabetes/diabetes or dyslipidemia also increased from $4.29\%$ or $11.1\%$ in 2000 ~ 2004 to $22.34\%$ or $46.83\%$ in 2017 ~ 2020. The survival period of the PLC patients with normoglycemia or normolipidemic was 2.18 or 3.14 folds longer than those patients with pre-diabetes/diabetes or hyperlipidemia ($P \leq 0.05$).
### Conclusions
It was gradually increased that age, the proportion of females, non-viral hepatitis-related causes, AFP-negative, and abnormal glucose/lipids among PLC patients. Proper control of glucose/lipids or ALT may improve the prognosis of PLCs.
## Introduction
Primary liver cancer (PLC) is the fourth leading cause of cancer-related death worldwide [1]. Hepatocellular carcinoma (HCC) is the most common (>$80\%$) type of PLC (1–3). There are quite distinct risk factors contributing to the development of PLC at genetic and molecular levels between Chinese and Causations [4]. The main risk factor for HCC in Chinese or *Causations is* chronic hepatitis B virus (HBV) infection or hepatitis C virus (HCV) infection, as well as, alcohol intake, respectively. The causes of PLC in China include chronic infection with HBV and/or HCV, excessive alcohol consumption, non-alcoholic fatty liver disease (NAFLD), and aflatoxin exposure [5, 6]. The incidence of PLC has been improved following lifestyle changes, i.e. quitting smoking, reducing alcohol, and high-sugar/fat diet consumption. The reduced morbidity of PLC is also related to the introduction of new anti-viral therapies, nucleos(t)ide analogs (NAs), and direct-acting antivirals (DAAs) [1, 7] against HBV or HCV infections over the last two decades, contributing to the significant change of clinical characteristics of PLC.
Dietary intake has changed substantially following rapid economic development over the last decades in China, i.e. significantly reduced grain intake, but increased fat intake [8]. In addition, daily salt intake is also much higher, but vegetables and/or fruits are lower than recommended [9]. All of these factors could contribute to the rapidly rising prevalence of metabolic syndrome, i.e. up to $24.2\%$ from a large Chinese cross-sectional study [10]. Thus, metabolic syndrome, characterized by visceral obesity, dyslipidemia, hyperglycaemia, and hypertension, is a major challenge in public health nationwide [9, 11, 12]. Furthermore, disturbed glycemia is associated with an increased risk of PLC [13]. However, it remains to be explored that the potential role of dyslipidemia and hyperglycaemia involves the development of PLC.
We investigated the epidemiology of PLC over the last 20 years, including age, sex, causes, metabolic risk factors, and prognosis of PLC patients. Such information may offer novel therapeutic management to improve outcomes.
## Patients
It was performed a cohort retrospective study of PLC in the Ruijin Hospital (a tertial teaching hospital, Shanghai Jiao Tong University School of Medicine, China) over the last 20 years from January 2000 to December 2020. The total number of chronic liver diseases identified in The Department of Infectious Diseases, Ruijin Hospital were 6749 (Figure 1). Subsequently, 4981 were excluded: chronic HBV infection ($$n = 3486$$), chronic HCV infection ($$n = 499$$), autoimmune hepatitis (AIH) ($$n = 290$$), primary biliary cholangitis (PBC) ($$n = 31$$), drug-induced liver injury (DILI) ($$n = 7$$), alcoholic liver disease (ALD) ($$n = 19$$), hereditary liver disease ($$n = 10$$), primary sclerosing cholangitis (PSC) ($$n = 1$$), NAFLD ($$n = 88$$), parasitic liver disease (PLD) ($$n = 3$$), undetermined ($$n = 547$$) (Figure 1).
**Figure 1:** *The flow chart demonstrated the selection criteria for PLC patients.*
Based on the American Association for the Study of Liver Diseases (AASLD) Guidelines for the management of HCC [14], there were 1768 PLC cases in the current study fitted with the inclusion criteria, i.e.: only diagnosed PLC with treatment-naïve and complete clinical/laboratory data at the time of diagnosis. Subsequently, 77 cases were further excluded, because these patients received surgery ($$n = 19$$) or interventional therapy ($$n = 30$$), incomplete data ($$n = 28$$). Thus, the final eligible PLC patients for the current study were 1691 (Figure 1).
## Definitions and data assessment
The definition of CHB was based on AASLD 2018 Hepatitis B Guidance, i.e. persistent presence of HBsAg for more than six months [15]. The definition of CHC was based on AASLD 2019 Hepatitis C Guidance [16],, i.e. persistent detected HCV RNA for more than six months.
The 1691 cases were subclassed into normoglycemia (3.9 ~ 5.5mmol/L), and pre-diabetes/diabetes (≥ 5.5 mmol/L) groups, based on the latest guidelines of the American Diabetes Association [17].
In addition, these 1691 cases were also classified into normolipidemic and dyslipidemia groups, based on the Chinese guidelines for the prevention and treatment of dyslipidemia in adults, i.e: the cut-off: total cholesterol (TC) ≥ 5.2 mmol/L, low-density lipoprotein cholesterol (LDL-C) ≥ 3.4 mmol/L (130 mg/dl), triglycerides (TG) ≥ 1.7 mmol/L and apolipoprotein B (ApoB) ≥ 1.13 g/L. The patients with cut-off values below the above were classified as the normolipidemic group.
The Barcelona clinic liver cancer (BCLC) stage, the model for end-stage liver disease (MELD) score, albumin-bilirubin (ALBI) grade, and platelet-albumin-bilirubin (PALBI) *Grade is* followed by previous publication (18–21).
## Statistical analysis
All statistics were performed, using SPSS version 22.0 software (SPSS Inc., Chicago, IL, USA). Data were presented as mean ± standard derivation for normally distributed continuous data, as median (interquartile range, Q25–Q75) for abnormally distributed continuous data, or as actual values for categorical data. Baseline characteristics were summarized, using descriptive statistics. Groups were compared using χ2 tests for categorical and Mann-Whitney U tests for continuous variables. Multivariable Cox’s proportional hazard model was carried out to identify independent factors associated with the outcome (death) through time and cumulative overall survival (OS) was calculated to estimate the hazard ratios. Factors with $p \leq 0.05$ in the univariate cox regression analysis were entered into the multivariable model. Survival rates were estimated by the Kaplan-Meier method and the differences were compared using the log-rank test. P value < 0.05 was considered to indicate a statistically significant difference.
## Baseline clinical characteristics of PLC patients
A total of 1691 PLC patients were included in the current study, i.e. 1605 were HCC ($94.91\%$), 58 were intrahepatic cholangiocarcinoma (ICC) ($3.42\%$), and 28 were HCC-ICC mixed types ($1.66\%$). There was significant difference of the average age between HCC and ICC (56.62 ± 18.51 vs 60.8 ± 10.3 years, $P \leq 0.05$). Among $\frac{1183}{1691}$ ($69.96\%$) PLC patients with cirrhosis, $97.38\%$ or $1.86\%$ were HCC or ICC ($P \leq 0.05$). There were $93.44\%$ or $6.56\%$ BCLC grade A-C or D; $91.01\%$; or $8.99\%$ were Child-Turcotte-Pugh class A-B or stage C. The MELD score, ALBI score, alpha-fetoprotein (AFP), aspartate aminotransferase (AST); and total bilirubin (Tbil) level were significantly higher in HCC patients compared to ICC patients ($P \leq 0.05$). In contrast, albumin (Alb) levels, neutrophils, monocytes, lymphocytes and platelet counts from the HCC patients were significantly lower than the ICC patients ($P \leq 0.05$). There was no significant difference in survival time between HCC and ICC patients ($P \leq 0.05$) (Table 1).
**Table 1**
| Charcteristics | Total (n, %) | HCC (n, %) | ICC (n, %) | P value |
| --- | --- | --- | --- | --- |
| Number | 1691 (100%) | 1605 (94.91%) | 58 (3.42%) | |
| Age, years | 56.8 ± 18.14 | 56.62 ± 18.51 | 60.8 ± 10.3 | <0.05 |
| Gender | | | | <0.05 |
| Male | 1360 (80.43%) | 1312 (96.47%) | 33 (2.43%) | |
| Female | 331 (19.57%) | 293 (88.52%) | 25 (7.55%) | |
| Cirrhosis | | | | <0.05 |
| No | 508 (30.04%) | 453 (89.17%) | 36 (7.09%) | |
| Yes | 1183 (69.96%) | 1152 (97.38%) | 22 (1.86%) | |
| BCLC stage | | | | 0.6 |
| A | 573 (33.89%) | 548 (95.64%) | 22 (3.84%) | |
| B | 510 (30.16%) | 490 (96.08%) | 15 (2.94%) | |
| C | 497 (29.39%) | 476 (95.77%) | 19 (3.82%) | |
| D | 111 (6.56%) | 91 (81.98%) | 2 (1.8%) | |
| Child-Pugh class | | | | 0.13 |
| A | 1086 (64.22%) | 1047 (96.41%) | 39 (3.59%) | |
| B | 453 (26.79%) | 432 (95.36%) | 18 (3.97%) | |
| C | 152 (8.99%) | 126 (82.89%) | 1 (0.66%) | |
| MELD score | 7.27 (6.93∼7.61) | 7.48 (7.12∼7.83) | 4.3 (3.1∼5.49) | <0.05 |
| ALBI score | -1.99 (-2.03∼1.96) | -1.98 (-2.02∼-1.94) | -2.2 (-2.37∼-2.02) | <0.05 |
| pALBI score | -2.13 (-2.15∼-2.1) | -2.12 (-2.14∼-2.1) | -2.19 (-2.32∼-2.05) | 0.19 |
| AFP (ng/mL) | 2173 (1895∼2452) | 2284 (1991∼2576) | 452.4 (215.2∼1120) | <0.05 |
| ALT (IU/L) | 63.46 (57.13∼69.79) | 63.73 (57.09∼70.37) | 61.93 (42.38∼81.48) | 0.43 |
| AST (IU/L) | 83.54 (75.83∼91.25) | 84.59 (76.63∼92.56) | 54.42 (35.07∼73.77) | <0.05 |
| Alb (g/L) | 34.34 (33.98∼34.7) | 34.19 (33.82∼34.57) | 36.02 (34.43∼37.6) | <0.05 |
| Tbil (μmol/L) | 44.35 (40.4∼48.31) | 45.08 (40.95∼49.21) | 36.54 (19.97∼53.12) | <0.05 |
| Neutrophil count (× 109/L) | 3.85 (3.64∼4.06) | 3.82 (3.61∼4.04) | 4.3 (3.71∼4.89) | <0.05 |
| Monocyte count (× 109/L) | 0.5 (0.47∼0.52) | 0.49 (0.47∼0.52) | 0.53 (0.46∼0.59) | <0.05 |
| Lymphocyte count (× 109/L) | 1.47 (1.31∼1.62) | 1.46 (1.3∼1.62) | 1.54 (1.39∼1.69) | <0.05 |
| Platelet count (× 109/L) | 138.9 (134.7∼143.1) | 138.9 (134.7∼143.1) | 186 (164.2∼207.7) | <0.05 |
| Blood glucose (mmol/L) | 5.58 (5.47∼5.69) | 5.58 (5.47∼5.69) | 5.59 (5.17∼6.01) | 0.42 |
| TG (mmol/L) | 1.16 (1.11∼1.22) | 1.16 (1.1∼1.22) | 1.2 (1∼1.4) | 0.12 |
| TC (mmol/L) | 4.02 (3.91∼4.12) | 4.01 (3.9∼4.12) | 4.27 (3.83∼4.71) | 0.27 |
| LDL (mmol/L) | 2.41 (2.33∼2.5) | 2.41 (2.32∼2.5) | 2.55 (2.22∼2.88) | 0.31 |
| ApoB (mmol/L) | 0.83 (0.8∼0.86) | 0.83 (0.8∼0.86) | 0.82 (0.73∼0.9) | 0.58 |
| OS (months) | 24.98 (23.38∼26.59) | 25.32 (23.66∼26.99) | 17.14 (12.76∼21.53) | 0.09 |
Chronic hepatitis B (CHB) was still the predominant aetiology ($83.32\%$) (Figure S1A). The percentage of antiviral treatment among CHB-related PLC patients with or without cirrhosis was $\frac{762}{1183}$ ($64.41\%$) and $\frac{115}{508}$ ($22.64\%$), respectively (Figure S1B). The proportion of CHB-related PLC patients receiving antiviral therapy was raised from $24.11\%$ in 2000 ~ 2004 to $63.45\%$ in 2017 ~ 2020 (Figure S1C).
## Changes in clinical characteristics of PLC patients over 20 years
The average age of PLC patients increased gradually from 52.74 ± 0.5 to 58.63 ± 0.44 years over the last two decades (Figure 2A). Furthermore, the percentage of patients over 50 years increased from $59.23\%$ in 2000 ~ 2004 to $77.98\%$ in 2017 ~ 2020 (Figure 2B). The age distribution of PLC patients was $\frac{488}{1691}$ ($28.86\%$), $\frac{476}{1691}$ ($28.15\%$), $\frac{446}{1691}$ ($26.37\%$) or $\frac{281}{1691}$ ($16.62\%$) for 50 ~ 59, 60 ~ 69, 40 ~ 49, or the remaining age group (Figure 2C). The percentage of males decreased from $88.89\%$ in 2000 ~ 2004 to $77.54\%$ in 2017 ~ 2020, while the females increased from $11.11\%$ to $22.46\%$ (Figure 2D). PLC patients with alpha-fetoprotein (AFP) < 20ng/mL, 20ng/mL ≤ AFP < 400ng/mL, or AFP ≥ 400 ng/mL was $\frac{840}{1691}$ ($49.67\%$), $\frac{403}{1691}$ ($23.83\%$) or $\frac{448}{1691}$ ($26.5\%$), respectively (Figure 2E), and the proportion of patients with AFP < 20ng/mL (AFP-negative) gradually was increased from $24.2\%$ in 2000 ~ 2004 to $51.43\%$ in 2017 ~ 2020 (Figure 2F).
**Figure 2:** *Changes in age, sex, and different AFP levels ratio among PLC patients. Changes in age of diagnosis of PLC patients (A). Changes in age composition ratio (B). Percentage of PLC patients’ age (C). Changes in sex ratio (D). Stratification of AFP levels (E). Changes in the proportion of different AFP stratifications (F).*
The proportion of PLC patients with cirrhosis decreased from $93.37\%$ in 2005 ~ 2008 to $83.86\%$ in 2017 ~ 2020 (Figure 3A), while those with non-viral hepatitis-related PLC increased from $1.5\%$ in 2005 ~ 2008 to $22.35\%$ in 2017 ~ 2020 (Figure 3B). The proportion of patients with BCLC A stage increased from $14.29\%$ in 2000 ~ 2004 to $33.01\%$ in 2017 ~ 2020 (Figure 3C), and the proportion of the patients who received any treatment (including surgical or interventional therapy) increased from $9.5\%$ in 2000 ~ 2004 to $23.12\%$ in 2017 ~ 2020 (Figure 3D).
**Figure 3:** *Changes in clinical characteristics and metabolic risk factors of PLC patients. Changes in the proportion of combined vs without cirrhosis (A). Changes in the proportion of non-/viral hepatitis-related PLC patients (B). Changes in the proportion of different BCLC stages (C). Changes in the proportion of different treatment protocols (D). Changes in the proportion of pre-diabetes/diabetes vs normoglycemia (E). Changes in the proportion of hyperlipidemia vs normolipidemia (F).*
The percentage of patients with Child-Pugh class A or MELD score ≤ 14 increased from $28.57\%$ or $40\%$ in 2000 ~ 2004 to $71.33\%$ or $93.04\%$ in 2017 ~ 2020 (Figures S2A, B), and the proportion of ALBI grade I or pALBI grade I was raised from $13.39\%$ or $17.24\%$ in 2005 ~ 2008 to $28.12\%$ or $58.76\%$ in 2017 ~ 2020, respectively (Figures S2C, D).
## Metabolic risk factors and related prognosis among PLC patients
Within these PLC patients, 866 ($52.3\%$), 313 ($18.51\%$), 712 ($42.11\%$) or 979 ($57.89\%$) had normoglycemia, pre-diabetes/diabetes, normolipidemic or hyperlipidaemia. The PLC patients with elevated blood glucose (blood glucose reaching pre-diabetes/diabetes levels) increased from $4.29\%$ in 2000 ~ 2004 to $22.34\%$ in 2017 ~ 2020 (Figure 3E). Similarly, the PLC patients with dyslipidaemia increased from $11.1\%$ in 2000-2004 to $46.83\%$ in 2017-2020 (Figure 3F). There were increased percentages of female patients with elevated blood glucose and hyperlipidaemia from $12.5\%$ to $21.24\%$ and $14.29\%$ to $23.9\%$ from the period of 2005 ~ 2008 to the period of 2017 ~ 2020, whereas the ratio of male patients declined from $87.5\%$ or $85.71\%$ to $76.1\%$ or $78.76\%$ (Figure S3A, B).
The average survival period of the PLC patients with normoglycaemia or pre-diabetes/diabetes was 10 or 4.58 years with a significant difference ($P \leq 0.05$) (Figure 4A). The average survival period of the PLC patients with normolipidemic or hyperlipidemia was 5.50 or 1.75 years with a significant difference ($P \leq 0.05$) (Figure 4B). The survival periods for the PLC patients with high TC or apolipoprotein B (ApoB) was 0.84 or 0.83 years, compared to the PLC patients with normal TC or ApoB which was 3.58 or 3.42 years ($P \leq 0.05$) (Figures 4C–F).
**Figure 4:** *Effect of metabolic risk factors on the prognosis of PLC patients. Prognostic differences between pre-diabetes/diabetes and normoglycemia (A). Prognostic differences between hyperlipidemia and normolipidemia (B). Prognostic differences between normal TC level and high TC level (C). Prognostic differences between normal TG level and high TG level (D). Prognostic differences between normal LDL-C levels and high LDL-C levels (E). Prognostic differences between normal ApoB level and high ApoB level (F).*
## Predictors for poor overall survival of PLC patients
Using univariate analysis, predictors for poor overall survival of PLC patients included: age (hazard ratio, 0.984 [$95\%$ confidence interval, 0.974–0.995], $P \leq 0.05$), gender ([1.574, 1.421–1.782], $P \leq 0.05$), MELD score ([1.051, 1.04–1.062], $P \leq 0.05$), ALBI score ([1.952, 1.778–2.143], $P \leq 0.05$), pALBI score ([2.843, 2.495–3.239], $P \leq 0.05$), ALT ([1.015, 1.008–1.024], $P \leq 0.05$), AST ([1.002, 1.001–1.002], $P \leq 0.05$), Tbil ([1.004, 1.003–1.004], $P \leq 0.05$), Alb ([0.939, 0.93–0.948], $P \leq 0.05$), platelet count ([1.002, 1.001-1.003], $P \leq 0.05$), neutrophil count ([1.13, 1.107–1.153], $P \leq 0.05$), monocyte count ([1.087, 1.008–1.172], $P \leq 0.05$), blood glucose ([1.952, 1.895–2.012], $P \leq 0.05$), LDL-C ([1.847, 1.46–2.278], $P \leq 0.05$), ApoB ([1.414, 1.121–1.783], $P \leq 0.05$), AFP ([1.931, 1.443–2.483], $P \leq 0.05$) (Table 2).
**Table 2**
| Parameter | Univariate analysis HR (95%CI) | P value | Multivariate analysis HR (95%CI) | P value.1 |
| --- | --- | --- | --- | --- |
| Age | 0.984 (0.974-0.995) | <0.05 | | 0.8 |
| Gender: male/female | 1.574 (1.421-1.782) | <0.05 | 2.094 (1.036-4.234) | <0.05 |
| Cirrhosis | 1.574 (1.014-1.973) | 0.07 | | |
| PLC family history | 3.465 (2.132-5.631) | 0.1 | | |
| Smoking | 0.99 (0.812-1.206) | 0.05 | | |
| Alcohol | 1.005 (0.807-1.251) | 0.52 | | |
| Hypertension | 1.064 (0.831-1.361) | 0.07 | | |
| MELD score | 1.051 (1.04-1.062) | <0.05 | | |
| ALBI score | 1.952 (1.778-2.143) | <0.05 | | |
| pALBI score | 2.843 (2.495-3.239) | <0.05 | | |
| ALT (IU/L) | 1.015 (1.008-1.024) | <0.05 | 1.753 (1.086-2.832) | <0.05 |
| AST (IU/L) | 1.002 (1.001-1.002) | <0.05 | 1.066 (1.03-1.104) | <0.05 |
| Tbil (μmol/L) | 1.004 (1.003-1.004) | <0.05 | 1.005 (1.001-1.012) | <0.05 |
| Alb (g/L) | 0.939 (0.930-0.948) | <0.05 | 0.752 (0.57-0.993) | <0.05 |
| Platelet count (× 109/L) | 1.002 (1.001-1.003) | <0.05 | | 0.58 |
| Neutrophil count (× 109/L) | 1.13 (1.107-1.153) | <0.05 | | 0.06 |
| Monocyte count (× 109/L) | 1.087 (1.008-1.172) | <0.05 | | 0.39 |
| Lymphocyte count (× 109/L) | 1.017 (0.96-1.032) | 0.81 | | |
| Blood glucose (mmol/L) | 1.952 (1.895-2.012) | <0.05 | 1.72 (1.064-2.265) | <0.05 |
| TG (mmol/L) | 0.983 (0.845-1.146) | 0.08 | | |
| TC (mmol/L) | 1.073 (0.988-1.167) | 0.33 | 1.059 (1.017-1.102) | <0.05 |
| LDL-C (mmol/L) | 1.847 (1.46-2.278) | <0.05 | | 0.09 |
| ApoB (g/L) | 1.414 (1.121-1.783) | <0.05 | 1.992 (0.366-5.826) | <0.05 |
| AFP (ng/mL) | 1.931 (1.443-2.483) | <0.05 | 2.213 (1.589-3.081) | <0.05 |
To verify if these factors were significant in prediction for overall survival in PLC patients, multivariate analysis was applied. It was observed that the independent predictors included: gender (2.094 [1.036–4.234], $P \leq 0.05$), ALT ([1.753, 1.086–2.832], $P \leq 0.05$), AST ([1.066, 1.03–1.104], $P \leq 0.05$), Tbil ([1.005, 1.001–1.012], $P \leq 0.05$), Alb ([0.752, 0.57–0.993], $P \leq 0.05$), blood glucose ([1.72, 1.064–2.265], $P \leq 0.05$), TC ([1.059, 1.017–1.102], $P \leq 0.05$), ApoB ([1.992, 0.366–5.826], $P \leq 0.05$), AFP ([2.213, 1.589–3.081], $P \leq 0.05$) (Table 2).
## Discussion
The clinical importance of PLC, particularly in China, is due to the ~ 2.54-fold higher incidence than the world average [6]. Although most Chinese PLC patients had HBV infection via the mother-fetal route, the incidence of PLC in adolescents is still rare [6, 22]. It is reported that [23] incidence of HCC and age are directly correlated in most populations. In the current study, middle and older aged men between the ages of 50 ~ 70 years accounted for more than $80\%$ of PLC patients, with the largest proportion of the PLC patients aged 50 ~ 60 years ($28.86\%$). Therefore, it is necessary to monitor the onset of PLC within this age group carefully. Following determining the incidence of PLC patients, we found that the mean age of PLC patients increased from 52.74 ± 0.5 years in 2000 ~ 2004 to 58.63 ± 0.44 years in 2017 ~ 2020. Interestingly, there was the proportion of patients with BCLC grade A, Child-Turcotte-Pugh A class, and low MELD, ALBI, pALBI scores trended higher in this study, particularly the proportion of patients who underwent surgery or interventional treatment was significantly higher. In addition, there is an increasing trend of CHB-related HCC patients receiving antiviral therapy. These data suggest that such change may be due to more PLC patients having received advanced life-saving interventions over the past 20 years. These new conditions among PLC patients may be partially due to: The government organized vaccination against HBV program is being a common practice in Chinese communities over the last two decades, as well as the gradual availability of anti-viral medicines, which substantially reduce the disease progression towards cirrhosis and/or PLC. This is supported by our finding that there were $83.32\%$ of PLC patients had HBV infection in the current study. Such observation suggests the good value of early intervention to reduce/prevent PLC in recent years. Therefore, we should strengthen the coverage of treatment for viral hepatitis patients to reduce the onset of PLC in the future.
There are significant sex differences in the incidence of PLC globally, showing that men are 2 ~ 4 times higher than women [24]. Consistent with other reports from China and East Asia [23, 25, 26], we found that men accounted for most PLC patients in the current study ($80.43\%$). Significant sex differences in the prognosis of HCC patients have been reported that females aged < 65 years had a better prognosis than males [25], supporting our current finding that the survival period of females was significantly longer than that of males, but the proportion of males was decreased by >$10\%$. In our study, the percentage of female patients with elevated blood glucose or hyperlipidaemia increased from $12.5\%$ to $21.24\%$ or $14.29\%$ to $23.9\%$ from the period of 2005 ~ 2008 to the period of 2017 ~ 2020. The increased blood glucose and lipidemia contribute to increased incidence of NAFLD in female, and subsequent the prevalence of primary liver cancer. There was no linkage with age, may be due to relatively smaller in sample size. Classical risk factors for PLC, e.g., viral hepatitis, alcohol consumption, and smoking, are more common in males, but the risk factors of developing PLC in females are shifting from viral hepatitis to NAFLD at the same period [27]. Such finding suggests that women with NAFLD, especially those over the age of 50, are more likely to develop advanced fibrosis than men [28]. Although CHB was still the predominant cause of PLC ($83.32\%$) in the current study, the proportion of non-hepatitis virus-related PLC patients was also increased ~ 10 fold over the last 20 years, suggesting the critical potential influence of metabolism-related liver disease for the PLC development. Thus, we should enhance screening or/and early intervention for liver cancer progress in female patients with non-viral chronic hepatic diseases.
AFP remains an important biomarker for the diagnosis of PLC in East Asia. Serum AFP expression levels are elevated in many PLC patients, and persistently elevated AFP levels are often associated with PLC progression and relapse [29]. However, AFP < 20ng/mL (AFP-negative) has been found in the current study and many PLC clinical research [29, 30], which implies that AFP can’t be used to screen PLC in these cases. Future studies should be carried out to find reliable biomarkers for diagnosing AFP-negative PLC. Thus, ensuring the timely initiation of treatment.
Long-term ALT abnormalities are strongly associated with the risk of developing HCC [31, 32]. This is in line with our finding that the mortality was $16.85\%$ or $31.46\%$ of PLC patients with ALT between 40 ~ 60 IU/L or ALT ≥ 60 IU/L. The PLC patients with ALT ≥ 40 IU/L had a higher risk of HCC progression than those PLC patients with ALT 40 ~ 60 IU/L. This is further supported by a South Korean team [33] showing that the risk of liver-related mortality is increased even in patients with high normal ALT levels (ALT 35 ~ 45 IU/L). However, the definition of ULN of ALT is not consistent in the different regions, the ULN of ALT used in our institute is 64 IU/L, which is higher than the ULN in the study of the East Asian populations mentioned above [33, 34], and the prognosis of PLC patients with ALT < 40 IU/L was better than that of the patients with ALT ≥ 40 IU/L, showing that ALT is inversely correlated with survival time in PLC patients. Our data is similar to other reports [35], suggesting that the current standards of China may make it difficult to accurately assess the monitoring of PLC risk. Thus, these data suggest that lowering ALT properly improves the prognosis of PLC patients.
Because the liver is the main organ in regulating sugar and lipid metabolism, compromised liver function from PLC patients may contribute to the imbalance of glucose and lipid composition, and ultimately promotes PLC progression due to the development of prediabetes/diabetes and hyperlipidaemia (36–38). This is supported by the report [39], showing that there is ~ 100-fold increased risk of developing PLC in patients with Diabetes Mellitus compared to those without. In the current study, the percentage of PLC patients with comorbid glucose abnormalities was increased ~ 5-fold over the last two decades, accompanied by significantly reduced survival time, compared to that of normoglycemic PLC patients. We also found that blood glucose ≥ 5.5 mmol/L was a significant risk factor affecting the prognosis of PLC patients. Thus, such information highlights the seriousness of blood glucose levels involved in the risk of PLC progression, and it is fundamentally important to control blood glucose among these PLC patients for the best of their prognosis and outcomes.
Moreover, we found that there was an inverse correlation between the proportion of PLC patients with hyperlipidaemia and the survival period. Our data is in line with a Japanese team [40], showing that increased incidence of non-hepatitis virus-related HCC patients with diabetes mellitus and hyperlipidaemia over the last two decades, as well as, metabolic factors are non-negligible risk factors for HCC. Furthermore, an inverse correlation between the survival rate of hepatitis virus-related PLC patients and hyperlipidaemia [41]. Therefore, it is absolutely necessary to strictly manage lipid levels in PLC patients to minimize the risk for further PLC progression.
Furthermore, in the current study, the prognosis of PLC patients with hyperlipidaemia was poorer than that of PLC patients with normal lipids. This is consistent with Hwang et al. [ 42] showing that TC could be used as a marker to predict PLC recurrence because TC is correlated well with serum AFP. Moreover, AFP levels are positively correlated with poor prognosis of patients with PLC [43]. Thus, in addition to AFP, TC may be a potential adjunctive in assessing the prognosis of PLC patients. Interestingly lipoproteins promote the proliferation and invasion of cancer cells, but also enhance anti-tumor immunity [44]. ApoB is associated with tumour size and poor patient prognosis [45]. ApoB/ApoA-I rates in HCC patients are correlated with AFP, distant metastases, and TNM stage, particularly in the patients with high ApoB/ApoA-I rates [46]. Thus, managing blood lipids is equally as important as managing TC and ApoB to facilitate more accurate prognostic assessment in PLC patients.
Although we described the real-life clinical experience of a busy general hospital, the results reliably reflect the clinical features and treatment approaches to PLC in China over the past 20 years. There are some limitations in the current study. Most PLC patients were CHB-related HCC in the current study, other causes will also be included, such as diabetes mellitus, and other metabolic syndrome-related biomarkers, such as body mass index (BMI), should be explored in the future. The epidemiological characteristics of HCC vary considerably across geographic regions and ethnic groups. The epidemiological characteristics of HCC vary considerably across geographic regions and ethnic groups. However, our current study was a single-center focused investigation, which will be extended to multicenter with different regions/ethnic backgrounds in the future.
In conclusion, we found that the age of the PLCs, female patients, and non-viral hepatitis-related PLC incidence gradually increased. Therefore, we should monitor PLC patients with AFP-negative regularly and discover novel biological markers to assess PLC progression. Proper control of glucose/lipids or ALT is beneficial for PLC patients to improve their prognosis. Such information offers some evidence for the management and prediction of PLC for clinicians and public health sections.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Human Ethics Committee, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
HW was fully responsible for the conduct of this study. HW and YD designed the experiment. YD, MF, DM, and GZ coordinated the study. YD performed the majority of the experiment and drafted the manuscript. SB & HW & KL, interpreted data and revised the manuscript. Clinical data collection was completed by MF, DM, GZ, XW, BA, YX, SL, LL and QX. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1109980/full#supplementary-material
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|
---
title: 'Causal associations between modifiable risk factors and pancreatitis: A comprehensive
Mendelian randomization study'
authors:
- Xiaotong Mao
- Shenghan Mao
- Hongxin Sun
- Fuquan Huang
- Yuanchen Wang
- Deyu Zhang
- Qiwen Wang
- Zhaoshen Li
- Wenbin Zou
- Zhuan Liao
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10043332
doi: 10.3389/fimmu.2023.1091780
license: CC BY 4.0
---
# Causal associations between modifiable risk factors and pancreatitis: A comprehensive Mendelian randomization study
## Abstract
### Background
The pathogenesis of pancreatitis involves diverse environmental risk factors, some of which have not yet been clearly elucidated. This study systematically investigated the causal effects of genetically predicted modifiable risk factors on pancreatitis using the Mendelian randomization (MR) approach.
### Methods
Genetic variants associated with 30 exposure factors were obtained from genome-wide association studies. Summary-level statistical data for acute pancreatitis (AP), chronic pancreatitis (CP), alcohol-induced AP (AAP) and alcohol-induced CP (ACP) were obtained from FinnGen consortia. Univariable and multivariable MR analyses were performed to identify causal risk factors for pancreatitis.
### Results
Genetic predisposition to smoking (OR = 1.314, $$P \leq 0.021$$), cholelithiasis (OR = 1.365, $$P \leq 1.307$$E-19) and inflammatory bowel disease (IBD) (OR = 1.063, $$P \leq 0.008$$) as well as higher triglycerides (OR = 1.189, $$P \leq 0.016$$), body mass index (BMI) (OR = 1.335, $$P \leq 3.077$$E-04), whole body fat mass (OR = 1.291, $$P \leq 0.004$$) and waist circumference (OR = 1.466, $$P \leq 0.011$$) were associated with increased risk of AP. The effect of obesity traits on AP was attenuated after correcting for cholelithiasis. Genetically-driven smoking (OR = 1.595, $$P \leq 0.005$$), alcohol consumption (OR = 3.142, $$P \leq 0.020$$), cholelithiasis (OR = 1.180, $$P \leq 0.001$$), autoimmune diseases (OR = 1.123, $$P \leq 0.008$$), IBD (OR = 1.066, $$P \leq 0.042$$), type 2 diabetes (OR = 1.121, $$P \leq 0.029$$), and higher serum calcium (OR = 1.933, $$P \leq 0.018$$), triglycerides (OR = 1.222, $$P \leq 0.021$$) and waist-to-hip ratio (OR = 1.632, $$P \leq 0.023$$) increased the risk of CP. Cholelithiasis, triglycerides and the waist-to-hip ratio remained significant predictors in the multivariable MR. Genetically predicted alcohol drinking was associated with increased risk of AAP (OR = 15.045, $$P \leq 0.001$$) and ACP (OR = 6.042, $$P \leq 0.014$$). After adjustment of alcohol drinking, genetic liability to IBD had a similar significant causal effect on AAP (OR = 1.137, $$P \leq 0.049$$), while testosterone (OR = 0.270, $$P \leq 0.002$$) a triglyceride (OR = 1.610, $$P \leq 0.001$$) and hip circumference (OR = 0.648, $$P \leq 0.040$$) were significantly associated with ACP. Genetically predicted higher education and household income levels could lower the risk of pancreatitis.
### Conclusions
This MR study provides evidence of complex causal associations between modifiable risk factors and pancreatitis. These findings provide new insights into potential therapeutic and prevention strategies.
## Introduction
Pancreatitis is a complex, progressive and debilitating inflammatory disease of the pancreas, the continuum of which includes clinical diagnoses of acute pancreatitis (AP), recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP). AP is one of the most common gastrointestinal conditions resulting in hospital admission [1], with an estimated annual incidence of 34 cases per 100000 person-years and 1.16 deaths per 100000 person-years in high-income countries [2]. Recurrent episodes of AP can eventually lead to pancreatic failure and CP. Although CP has a lower overall incidence (9.62 per 100 000 person-years) and mortality (0.09 per 100 000 person-years) than AP [2], it can result in intractable abdominal pain, endocrine/exocrine pancreatic insufficiency, impaired quality of life and reduced life expectancy [3, 4]. Furthermore, CP is considered an important risk factor for developing pancreatic cancer, a highly lethal malignancy with few effective therapeutic options [5]. Excessive alcohol consumption is a well-established etiological factor for both AP (~$20\%$) and CP (40-$70\%$) [1, 3]. Some patients with pancreatitis can be diagnosed with alcohol-induced AP (AAP) or CP (ACP) based on a history of alcohol exposure.
Since inflammatory disorders of the human pancreas tend to form a continuum, AP and CP share numerous common aetiologies. Besides excessive alcohol consumption, several important risk factors for pancreatitis, including smoking, hypertriglyceridaemia and autoimmune disease, are well established [1, 3, 4, 6, 7]. Gallstone disease and hypercalcemia can significantly increase the risk of AP [1, 6], while chronic kidney disease (CKD) and celiac disease are associated with an increased risk of CP [3, 7]. Inflammatory bowel disease (IBD) and systemic lupus erythematosus (SLE) seem to increase the risk of pancreatitis, although exact risk estimates are not available [8]. Serum parameters, including serum amylase [9], cholesterol [10, 11] and C-reactive protein (CRP) [12], are reported as potential biomarkers for pancreatitis. Additionally, observational studies suggest that pancreatitis is associated with metabolic comorbidities, including obesity [13, 14] and type 2 diabetes (T2D) [15, 16]. Although observational studies can control for known confounders through statistical techniques, the existence of unknown or unmeasured confounders could influence the results. Randomized controlled trials (RCTs) are considered a standard epidemiological design for establishing a risk factor’ direct, causal effect on disease development. However, due to cost, implementation difficulty, or ethical concerns, RCTs are not always feasible.
Mendelian randomization (MR) is an instrumental variable analysis for examining causal associations between risk factors and disease outcomes in epidemiology [17]. It uses genetic variants robustly associated with a risk factor as instrumental variables (IVs) and mimics a randomized controlled setting in which all other variables except the exposure of interest are randomly and equally distributed over subgroups. Thus, MR analyses are less vulnerable to bias from confounding, reverse causation and measurement errors. As an emerging method, MR analyses are increasingly applied to explore the causal association between various risk factors and pancreatitis. Based on an analysis of participants with genetic variants associated with increased plasma triglyceride levels, Hansen et al. found that higher concentrations of triglycerides, caused by genetic variants impairing lipoprotein lipase function, increase the risk of AP [18]. The study by Yuan et al. revealed the causal roles of gallstone disease, diabetes, calcium, triglycerides, smoking and alcohol consumption in AP and CP [19]. More recently, Mi et al. investigated the causal associations of genetically predicted blood metabolites on pancreatitis and found that elevated triglycerides levels and reduced degree of unsaturation in fatty acids increased the risk of pancreatitis [20]. Nevertheless, many other modifiable risk factors for pancreatitis, such as lifestyle factors, autoimmune diseases, serum parameters and metabolic comorbidities, have not been comprehensively studied. Better understanding and management of risk factors for pancreatitis will enable clinicians to reduce and prevent the disease.
This study explores the causal effects of 30 genetically-proxied potential risk factors on the risks of AP, CP, AAP and ACP using a two-sample MR framework. The aim of this study is to provide a comprehensive overview of putative modifiable risk factors for pancreatitis and offer novel insights into the aetiology of pancreatitis.
## MR design
MR was utilised to investigate the relationships between various risk factors and different types of pancreatitis. A total of 30 primary risk factors were selected and classified into six categories: lifestyle behaviours, related diseases, serum parameters, lipid metabolism, glucose metabolism and obesity traits. Single nucleotide polymorphisms (SNPs) associated with these risk factors were used as IVs. The following three assumptions served as the foundation for the MR study: [1] the SNPs are closely related to the risk factors; [2] the SNPs are irrelevant to various confounders; [3] the SNPs only influence the outcomes through the risk factors (Figure 1). The datasets used in this study are available from public databases and received ethical approval before implementation. This study, therefore, did not require additional ethical approval.
**Figure 1:** *Overview of the design and methods used in this Mendelian randomization study. AP, acute pancreatitis; CP, chronic pancreatitis; AAP, alcohol-induced acute pancreatitis; ACP, alcohol-induced chronic pancreatitis; CRP, C-reactive protein; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ApoA- I, apolipoprotein A-I; HbA1c, glycated hemoglobin; BMI, body mass index.*
## Selection of genetic instruments
Genome-wide association studies (GWASs) of individuals of European ancestry were selected as data sources for genetic instruments associated with the 30 risk factors. Genetic instruments of smoking initiation, cigarettes per day and alcoholic drinks per week were extracted from the GSCAN (GWAS and Sequencing Consortium of Alcohol and Nicotine use) consortium [21]. GWAS summary statistics for coffee intake and household income were obtained from the MRC-IEU (MRC Integrative Epidemiology Unit) consortium. IVs for education level were selected from SSGAC (Social Science Genetic Association Consortium) [22]. GWAS summary statistics for cholelithiasis and autoimmune diseases were obtained from the FinnGen consortium [23]. IVs for IBD were extracted from IIBDGC (The International Inflammatory Bowel Disease Genetics Consortium) [24]. The UK Biobank study was used as the data source for GWAS summary statistics for lipid metabolism traits including testosterone, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A-I and total cholesterol [25]. Genetic instruments for body mass index (BMI) and whole body fat mass were selected from Neale Lab (http://www.nealelab.is/uk-biobank). GWAS summary statistics for hip circumference, waist circumference and waist-to-hip ratio were extracted from the GIANT (Genetic Investigation of ANthropometric Traits) consortium [26]. For CKD [27], celiac disease [28], SLE [29], serum calcium [30], serum amylase [31], CRP [32], T2D [33], fasting glucose, HbA1c and fasting insulin [34], IVs were selected from associated GWAS studies. SNPs at the genome-wide significance level ($P \leq 5$×108) were extracted and those with a considerable physical distance (≥ 10,000 kb) and less probability of linkage disequilibrium (R2 < 0.01) were included.
## GWAS summary statistics for pancreatitis cohorts
GWAS summary statistics for AP, CP, AAP and ACP were obtained from the FinnGen consortium. The R5 release of the FinnGen consortium data was used [23]; this data set contains 3,022 cases and 195,144 controls for AP (https://risteys.finngen.fi/endpoints/K11_ACUTPANC), 1,737 cases and 195,144 controls for CP (https://risteys.finngen.fi/phenocode/K11_CHRONPANC), 457 cases and 218,335 controls for AAP (https://risteys.finngen.fi/phenocode/ALCOPANCACU) and 977 cases and 217,815 controls for ACP (https://risteys.finngen.fi/phenocode/ALCOPANCCHRON). All selected GWASs from the FinnGen consortium obtained ethical approval from the FinnGen Steering Committee and individuals provided informed consent.
## Evaluation of the strength of the genetic instruments
The F-statistic was used to assess the genetic instrument strength. F statistics (F = beta2/se2) were calculated for each SNP and a general F statistic was calculated for all SNPs for the corresponding exposure. F > 10 was considered to be sufficient strength. All F statistics were over 10.
## Univariable MR analyses
The random-effect inverse-variance weighted (IVW) method was utilised as the primary analysis to estimate the association between genetic liability to modifiable risk factors and the risk of pancreatitis. Given that the analysis is sensitive to outliers and horizontal pleiotropy, three sensitivity analyses, including the weighted median, MR-Egger and MR-PRESSO methods, were used to examine the consistency of the results. The weighted median model can produce unbiased estimates under the precondition that at least $50\%$ of the selected IVs are valid [35]. MR-Egger regression was used to obtain cogent causal estimates under the influence of pleiotropy [36]. The MR-PRESSO method was performed to identify outlier SNPs due to the existing pleiotropy; causal effect estimates were obtained with the IVW approach after removing these outliers [37]. The MR-PRESSO and Cochrane’s Q statistics were used to evaluate pleiotropy and heterogeneity, respectively.
## Multivariable MR analyses
Considering that gallstone disease ($45\%$) is the most frequent cause of AP [1],multivariable MR analysis was undertaken with adjustment for genetically predicted cholelithiasis to assess potential mediating effects of cholelithiasis on AP risk. As alcohol consumption (40-$70\%$) and smoking (~$60\%$) are common aetiological risk factors for CP [3], a multivariable MR analysis with adjustment for genetically predicted alcohol consumption and genetic liability was conducted to reduce their potential pleiotropy. Furthermore, associations between the genetically predicted risk factors and alcohol-induced pancreatitis were assessed using multivariable MR analyses after adjustment for genetic liability to alcohol consumption.
The results are reported as odds ratios (OR) with corresponding $95\%$ confidence intervals (CIs). A Bonferroni-corrected significance level of $P \leq 1.67$×10-3 ($\frac{0.05}{30}$) was used and P values ranging from 1.67×10−3 to 0.05 were classified as suggestive causal associations. All statistical analyses were performed using R 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria), with the R packages “TwoSampleMR” (https://github.com/MRCIEU/TwoSampleMR) and “MRPRESSO” (https://github.com/rondolab/MR-PRESSO). Two-Sample MR analysis was performed according to the developers’ guidelines (https://mrcieu.github.io/TwoSampleMR/index.html). The data visualization was performed using the R package “forestploter” (https://github.com/adayim/forestploter).
## Baseline characteristics of the 30 candidate risk factors
Thirty potential risk factors were included in the analyses. The risk factors can be classified into six categories: lifestyle behaviours, related diseases, serum parameters, lipid metabolism, glucose metabolism and obesity traits (Table 1). The lifestyle behaviours include smoking, alcohol consumption, coffee consumption, education and income. The related diseases included cholelithiasis, CKD and autoimmune diseases (including celiac disease, IBD and SLE). The serum parameters include calcium, amylase, CRP and testosterone. Additionally, five traits related to lipid metabolism, four related to glucose metabolism and five pertaining to obesity traits were analysed. The number of SNPs ranged from 4 to 481. Across the 30 modifiable potential risk factors examined, the F-statistics for their respective genetic instruments were all greater than the empirical threshold of 10, suggesting no potential weak instrument bias.
**Table 1**
| Exposures | GWAS ID | SNPs | Unit | Sample* | F | PubMed ID or Consortium |
| --- | --- | --- | --- | --- | --- | --- |
| Exposures | GWAS ID | SNPs | Unit | Sample* | F | PubMed ID or Consortium |
| Lifestyle behaviors | Lifestyle behaviors | Lifestyle behaviors | Lifestyle behaviors | Lifestyle behaviors | Lifestyle behaviors | Lifestyle behaviors |
| Smoking initiation | ieu-b-4877 | 106 | SD | 607291 | 39.80 | 30643251 |
| Cigarettes per day | ieu-b-25 | 28 | SD | 337334 | 127.83 | 30643251 |
| Alcoholic drinks per week | ieu-b-73 | 39 | SD | 335394 | 46.69 | 30643251 |
| Coffee intake | ukb-b-5237 | 44 | SD | 428860 | 71.67 | MRC-IEU |
| Education level | ieu-a-1239 | 481 | SD | 766345 | 46.37 | 30038396 |
| Household income | ukb-b-7408 | 54 | SD | 397751 | 40.73 | MRC-IEU |
| Related diseases | Related diseases | Related diseases | Related diseases | Related diseases | Related diseases | Related diseases |
| Cholelithiasis | finn-b-K11_CHOLELITH | 50 | | 214167 | 101.26 | FinnGen |
| Chronic kidney disease | ebi-a-GCST003374 | 4 | | 117165 | 64.58 | 26831199 |
| Autoimmune | finn-b-AUTOIMMUNE | 73 | | 218792 | 112.86 | FinnGen |
| Celiac disease | ieu-a-1058 | 33 | logOR | 24269 | 609.42 | 22057235 |
| Inflammatory bowel disease | ieu-a-294 | 183 | logOR | 65642 | 103.67 | 26192919 |
| Systemic lupus erythematosus | ebi-a-GCST003156 | 58 | logOR | 14267 | 56.75 | 26502338 |
| Serum parameters | Serum parameters | Serum parameters | Serum parameters | Serum parameters | Serum parameters | Serum parameters |
| Serum calcium | | 7 | SD | 61079 | 304.27 | 24068962 |
| Serum amylase | prot-a-89 | 6 | | 3301 | 105.74 | 29875488 |
| C-reactive protein | ieu-b-35 | 76 | | 204402 | 157.98 | 30388399 |
| Testosterone | ukb-d-30850_irnt | 131 | SD | 230454 | 62.07 | 32042192 |
| Lipid metabolism | Lipid metabolism | Lipid metabolism | Lipid metabolism | Lipid metabolism | Lipid metabolism | Lipid metabolism |
| Triglycerides | met-d-Total_TG | 100 | SD | 115078 | 130.74 | UK Biobank$ |
| HDL-C | met-d-HDL_C | 137 | SD | 115078 | 121.61 | UK Biobank$ |
| LDL-C | met-d-LDL_C | 71 | SD | 115078 | 164.71 | UK Biobank$ |
| Apolipoprotein A-I | met-d-ApoA1 | 108 | SD | 115078 | 118.54 | UK Biobank$ |
| Total cholesterol | met-d-Total_C | 86 | SD | 115078 | 127.89 | UK Biobank$ |
| Glucose metabolism | Glucose metabolism | Glucose metabolism | Glucose metabolism | Glucose metabolism | Glucose metabolism | Glucose metabolism |
| Type 2 diabetes | ebi-a-GCST006867 | 136 | logOR | 655666 | 863.97 | 30054458 |
| Fasting glucose | ebi-a-GCST90002232 | 89 | SD | 200622 | 123.84 | 34059833 |
| HbA1c | ebi-a-GCST90002244 | 98 | SD | 146806 | 96.23 | 34059833 |
| Fasting insulin | ebi-a-GCST90002238 | 43 | SD | 151013 | 50.21 | 34059833 |
| Obesity traits | Obesity traits | Obesity traits | Obesity traits | Obesity traits | Obesity traits | Obesity traits |
| Body mass index | ukb-a-248 | 458 | SD | 336107 | 54.91 | UK Biobank |
| Whole body fat mass | ukb-a-265 | 422 | SD | 330762 | 53.52 | UK Biobank |
| Waist circumference# | ieu-a-67 | 72 | SD | 231353 | 49.58 | 25673412 |
| Hip circumference# | ieu-a-55 | 90 | SD | 211114 | 56.92 | 25673412 |
| Waist-to-hip ratio# | ieu-a-79 | 43 | SD | 210082 | 54.33 | 25673412 |
## Causal effects of the modifiable risk factors on acute pancreatitis
The univariable MR analyses revealed that genetically predicted cholelithiasis (OR = 1.365, $$P \leq 1.307$$E-19) and higher BMI (OR = 1.335, $$P \leq 3.077$$E-04) were significantly associated with an increased risk of AP (Figure 2; Supplementary Figure 1; Supplementary Table 1). Genetically predicted smoking initiation, IBD, higher triglycerides, whole body fat mass, and increased waist circumference were suggestively associated with AP. The ORs were 1.314 ($$P \leq 0.021$$) for smoking initiation, 1.189 ($$P \leq 0.016$$) for triglycerides, 1.291 ($$P \leq 0.004$$) for whole body fat mass, and 1.466 ($$P \leq 0.011$$) for waist circumference. Possible pleiotropy and heterogeneity were observed for whole body fat mass (P pleiotropy < 0.001; P heterogeneity = 0.003). Thus, MRPRESSO analysis was performed after removing the outliers. The relationship remained stable in the MRPRESSO-corrected results ($$P \leq 0.011$$). There was a potential association between genetically predicted IBD and increased risk of AP (OR = 1.063, $$P \leq 0.008$$). Notably, higher education and household income level were significantly associated with a reduced risk of AP. The odds of AP decreased with increas ing education level (OR = 0.478, $$P \leq 2.111$$E-10), household income (OR = 0.418, $$P \leq 0.001$$), LDL-C (OR = 0.843, $$P \leq 0.038$$), total cholesterol (OR = 0.822, $$P \leq 0.017$$) and hip circumference (OR = 0.780, $$P \leq 0.021$$). A possible pleiotropy and heterogeneity were observed for LDL-C (P pleiotropy = 0.006; P heterogeneity = 0.004) and total cholesterol (P pleiotropy = 0.026; P heterogeneity = 0.037), and these relationships remained significant in the MR-PRESSO-corrected results.
**Figure 2:** *Forest plot to visualize the causal effect of modifiable risk factors on AP using the inverse variance-weighted method. AP, acute pancreatitis; OR, odds ratio; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, glycated hemoglobin.*
## Causal effects of the modifiable risk factors on chronic pancreatitis
Genetically predicted cholelithiasis was significantly associated with an increased risk of CP (OR = 1.180, $$P \leq 0.001$$), while genetically predicted smoking initiation, alcohol consumption, autoimmune diseases, IBD, T2D, and higher serum calcium, triglycerides and waist-to-hip ratio were suggestively associated with CP (Figure 3; Supplementary Figure 2; Supplementary Table 2). The odds of CP increased with increasing smoking initiation (OR = 1.595, $$P \leq 0.005$$), alcoholic drinks per week (OR = 3.142, $$P \leq 0.020$$), serum calcium (OR = 1.933, $$P \leq 0.018$$), triglycerides (OR = 1.222, $$P \leq 0.021$$) and waist-to-hip ratio (OR = 1.632, $$P \leq 0.023$$). Genetically predicted autoimmune diseases, IBD and T2D were suggestively associated with an increased risk of CP (autoimmune: OR = 1.123, $$P \leq 0.008$$; IBD: OR = 1.066, $$P \leq 0.042$$; T2D: OR = 1.121, $$P \leq 0.029$$). We observed possible heterogeneity for alcoholic drinks per week (P heterogeneity = 0.05) and cholelithiasis (P heterogeneity = 0.039). Higher education level, household income and testosterone were protective for CP (education: OR = 0.536, $$P \leq 4.682$$E-05; income: OR=0.470, $$P \leq 0.029$$; testosterone: OR = 0.538, $$P \leq 0.017$$). Possible pleiotropy for education level was observed (P pleiotropy = 0.041).
**Figure 3:** *Forest plot to visualize the causal effect of modifiable risk factors on CP using the inverse variance-weighted method. CP, chronic pancreatitis; OR, odds ratio; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, glycated hemoglobin.*
## Causal effects of the modifiable risk factors on alcohol-induced pancreatitis
The causal associations between the risk factors and AAP were examined (Figure 4; Supplementary Figure 3; Supplementary Table 3). Notably, genetic liability to alcohol consumption was strongly associated with higher odds of AAP (OR = 15.045, $$P \leq 0.001$$). Genetic liabilities to smoking, IBD, higher BMI, and increased waist circumference were suggestively associated with an increased risk of AAP. The odds of AAP would increase with increasing smoking initiation (OR = 2.028 $$P \leq 0.018$$), BMI (OR = 1.876, $$P \leq 0.002$$) and waist circumference (OR = 2.021, $$P \leq 0.048$$). For BMI, there were possible pleiotropy (P pleiotropy = 0.036) and heterogeneity (P heterogeneity = 0.045). Genetic predisposition to IBD was associated with an increased risk of AAP (OR = 1.124, $$P \leq 0.047$$). Higher education level and increased hip circumference were protective factors for AAP (education: OR = 0.299, $$P \leq 3.621$$E-05; hip circumference: OR = 0.509, $$P \leq 0.013$$).
**Figure 4:** *Analysis of modifiable risk factors and alcohol-induced pancreatitis by the inverse variance-weighted method. AAP, alcohol-induced acute pancreatitis; ACP, alcohol-induced chronic pancreatitis; OR, odds ratio; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, glycated hemoglobin.*
The causal effects of these candidate factors on ACP were then analysed (Figure 4; Supplementary Figure 4; Supplementary Table 4). Genetic predisposition to alcohol drinking, smoking initiation, and higher serum triglycerides and calcium suggestively correlated with ACP. The ORs were 6.042 ($$P \leq 0.014$$) for alcohol drinking, 1.611 ($$P \leq 0.033$$) for smoking initiation, 1.367 ($$P \leq 0.011$$) for triglycerides, and 2.194 ($$P \leq 0.034$$) for serum calcium. There were possible pleiotropy (P pleiotropy = 0.006) and heterogeneity (P heterogeneity = 0.004) for alcohol drinking. The relationship remained stable in the MRPRESSO-corrected results ($$P \leq 0.002$$). Higher education level was significantly associated with lower odds of ACP (OR = 0.497, $$P \leq 3.420$$E-04), while higher household income, higher testosterone level and increased hip circumference were suggestively protective of ACP (income: OR = 0.266, $$P \leq 0.003$$; testosterone: OR = 0.435, $$P \leq 0.017$$; hip circumference: OR = 0.581, $$P \leq 0.004$$).
## Multivariable MR analysis of pancreatitis
In the multivariable MR model, smoking (OR = 1.287, $$P \leq 0.026$$), education level (OR = 0.441, $$P \leq 1.78$$E-09), household income (OR = 0.436, $$P \leq 0.002$$), IBD (OR = 1.052, $$P \leq 0.036$$) and triglycerides (OR = 1.202, $$P \leq 0.009$$) had similar significant causal effects on AP after adjusting for genetically predicted cholelithiasis, whereas LDL_C, total cholesterol, BMI, whole body fat mass, hip circumference and waist circumference did not reach statistical significance (Figure 5A). This suggests that these latter associations could be affected by cholelithiasis. Adjusting for the genetic risk of alcohol consumption and smoking did not change the associations between CP and education (OR = 0.393, $$P \leq 1.79$$E-05), cholelithiasis (OR = 1.177, $$P \leq 0.002$$), triglycerides (OR = 1.284, $$P \leq 0.014$$) and the waist-to-hip ratio (OR = 0.013, $$P \leq 1.745$$). In contrast, no significant associations remained between CP and household income, autoimmune diseases, IBD, testosterone and T2D (Figure 5B). Finally, multivariable MR models of alcohol-induced pancreatitis were examined (Figures 5C, D). Education level remained a statistically significant (AAP: OR = 0.164, $$P \leq 3.03$$E-08; ACP: OR = 0.348, $$P \leq 2.75$$E-06) risk factor for alcohol-induced pancreatitis, which confirms the robustness of the results. Genetic liability to IBD had a similar significant causal effect on AAP (OR = 1.137, $$P \leq 0.049$$). Conversely, genetically predicted smoking, BMI, hip circumference and waist circumference were no longer significant risk factors for AAP in the multivariable MR model. Adjustment for alcohol consumption did not change the associations between ACP and household income (OR = 0.358, $$P \leq 0.038$$), testosterone (OR = 0.270, $$P \leq 0.002$$), triglycerides (OR = 1.610, $$P \leq 0.001$$) and hip circumference (OR = 0.648, $$P \leq 0.040$$).
**Figure 5:** *The association between adjusted modifiable risk factors and pancreatitis by multivariable Mendelian randomization. (A) Association between modifiable risk factors and AP after adjustment of cholelithiasis. (B) Association between modifiable risk factors and CP after adjustment of alcohol consumption and smoking. (C, D) Association between modifiable risk factors and AAP or ACP after adjustment of alcohol consumption. AP, acute pancreatitis; CP, chronic pancreatitis; AAP, alcohol-induced acute pancreatitis; ACP, alcohol-induced chronic pancreatitis; SNPs, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; LDL-C,low-density lipoprotein cholesterol.*
## Discussion
Inflammatory diseases of the pancreas often form a continuum, with a sentinel AP event at one end of the continuum, followed by RAP and the eventual development of CP. AP is an inflammatory process with a highly variable clinical course. The causal factors implicated in AP include gallstones, alcohol abuse, hypertriglyceridemia, hypercalcemia, autoimmune diseases, medication, endoscopy, (surgical) trauma, infection and pancreatic division [1, 6]. Epidemiological data have established that excessive alcohol consumption is the second leading cause of AP after gallstones [1, 6] and the most prevalent risk factor for CP [3, 7]. It is also a risk factor for recurrent pancreatitis after the first AP attack and increases the risk of progression to CP [38]. ACP can be diagnosed in patients who have consumed more than 80 g of alcohol on average per day for 6-12 years or have been diagnosed with alcohol addiction in the context of CP, or when symptom onset is directly associated with alcohol consumption [3]. Additionally, genetic anticipation has been suggested to play an essential role in developing pancreatitis [39]. The development and progression of pancreatitis are multidimensional, with interactions between genetic and environmental factors [40]. In this study, the causal effects of 30 potential risk factors on pancreatitis were systematically investigated using MR analyses.
Cigarette smoking and alcohol use are two well-recognized lifestyle risk factors for pancreatitis [1, 3]. Smoking promotes the progression from AP to RAP or CP and accelerates the development of alcohol-induced pancreatitis [3]. MR analysis confirmed that smoking initiation was associated with a higher risk of pancreatitis. The effects of smoking on AAP and ACP were partially attenuated after adjusting for alcohol consumption, suggesting that this association is not robust enough in alcohol-induced pancreatitis. Alcohol exposure contributes to the initiation and progression of pancreatitis and amplifies the association between genetic risk factors and CP in a dose-dependent manner [41]. The outcomes in the current study verify the causal associations between alcohol consumption and CP, AAP and ACP. In accordance with expectation, the risks of AAP and ACP due to genetically predicted alcohol consumption were higher than that of CP. However, there was no evidence of a positive association between alcohol consumption and AP. As highlighted by Yuan et al., the lack of this association could be due to the small proportion of moderate or heavy drinkers in the GWAS cohort [19]. Furthermore, alcohol accounts for 40-$70\%$ of CP aetiologies, and only $20\%$ of AP aetiologies. Thus, associations between AP and alcohol drinking could be less robust than associations with CP or alcohol-induced pancreatitis. The relationship between coffee consumption and pancreatitis is controversial. Some studies have reported that coffee reduces the risk of pancreatitis [42], while another prospective cohort study found no association between coffee intake and the risk of pancreatitis [43]. Our MR analyses found no evidence of any associations between genetically predicted coffee consumption and pancreatitis risk.
Gallstone disease is the most common risk factor for AP in high-income countries [1]; however, the relationship between gallstones and CP remains uncertain [44]. The MR results indicated that genetically predicted cholelithiasis was strongly associated with a higher risk of AP events. Our results also suggested that genetically predicted cholelithiasis significantly increases the risk of CP, which is consistent with Yuan’s study [19]. Autoimmune diseases, including celiac disease, IBD and SLE, have been reported to be associated with pancreatitis in previous studies (45–47). Pancreatic abnormalities in IBD include AP, CP, autoimmune pancreatitis, pancreatic exocrine insufficiency and asymptomatic abnormalities [48]. The MR results in the current study support the causal relationship between IBD and pancreatitis. Nevertheless, no evidence was found for an association between pancreatitis and celiac disease or SLE. It is still a matter of debate whether AP is more prevalent in patients with CKD than in the general population. Two single-center studies demonstrated the prevalence of AP is higher in patients with end-stage renal disease [49, 50]. In contrast, a recent large population-based study revealed that the prevalence of AP in the United *States is* comparable between advanced CKD and non-CKD [51]. In the present study, genetic predisposition to CKD trended toward an increased risk of AP, but this association did not reach statistical significance.
Among the serum parameters, a possible association between genetically predicted physiologically higher calcium levels and CP or ACP was observed, consistent with Yuan et al. ’s study [19]. Hypertriglyceridemia is a well-established risk factor for both AP and CP, and this was supported by both the univariable and multivariable MR models in this study. LDL-C, HDL-C and apolipoprotein A-I were previously reported to be associated with the severity of AP [10, 11]. However, the current results showed no associations between pancreatitis risk and genetically predicted HDL-C or apolipoprotein A-I. Genetically predicted higher LDL-C and total cholesterol were suggestively associated with lower odds of AP, whereas these associations were not significant after adjustment for cholelithiasis. Notably, the recent MR study by Chen et al. demonstrated that the odds of cholelithiasis would decrease with higher levels of total cholesterol and LDL-C [52], suggesting the protective effect of higher LDL-C or total cholesterol levels against AP could be affected by cholelithiasis. The null association between total cholesterol and pancreatitis was supported by a prospective cohort study conducted in Sweden [53].
Previous prospective studies suggested a positive link between T2D and the risk of AP [15, 54], which was already supported by the MR study by Yuan et al. [ 19]. However, the current data suggests that genetic liability to T2D did not significantly increase the risk of AP. Two factors may influence this difference. First, the T2D GWAS dataset used by Yuan et al. combined data from multiethnic cohorts, while the T2D GWAS dataset processed in this study was obtained from European populations. Second, the updated data for AP was advantageous in the current study due to the much larger sample size. Further research is warranted to characterize and consolidate the relationship between T2D and AP. Interestingly, a suggestive relationship between T2D and CP was observed in the current study, but this relationship was not statistically significant after adjusting for smoking and alcohol consumption. Causal associations between pancreatitis and fasting glucose, HbA1c and fasting insulin were also not observed in the present study. Furthermore, the outcomes revealed suggestive associations between obesity traits and AP. However, these associations did not persist after adjusting for cholelithiasis, suggesting that an elevated risk of cholelithiasis due to obesity traits may explain this relationship [52].
Some protective factors for pancreatitis were identified in the present MR study. It is a pleasant surprise to find that a genetic liability to higher education levels significantly decreased the risk of pancreatitis in both the univariable and multivariable models. Genetically predicted household income was also associated with lower AP, CP and ACP risks. Higher education and household income may modulate pancreatitis risk by affecting multiple pathways, including individuals’ health behaviours, living environments and lifestyles. Additionally, there was a suggestive association between higher testosterone and reduced risks of CP and ACP. The potential relationship between testosterone and ACP remained significant after adjustment for alcohol consumption, which may explain previous findings indicating that female patients develop alcoholic pancreatitis at younger ages, with shorter durations, and under smaller cumulative amounts of alcohol consumption than male patients [55]. Notably, testosterone has a protective effect on pancreatic beta cells, while testosterone deficiency may contribute to the development of metabolic syndrome in men [56]. Furthermore, the current results suggest that bigger hip circumference was associated with reduced risks of AP, AAP and ACP, and this association persisted in the ACP cohort after adjusting for alcohol consumption.
The present study has several strengths related to the data sources and research design. First, the MR design enabled estimating the causal links between two complex heritable traits, which avoids the biases inherent in conventional observational epidemiological studies. Multiple sensitivity analyses were performed to confirm the plausibility of the instrumental variable assumptions and interpreted the results after considering horizontal pleiotropy and outliers. Second, this study has systematically analysed the largest number of modifiable causal risk factors for pancreatitis. Third, GWAS data used in this study were primarily derived from participants of European ancestry, which could reduce population stratification bias. Aside from cholelithiasis and autoimmune diseases, this study avoided sample overlap between most exposure types and outcomes, thereby keeping the type 1 error rate as low as possible. Nonetheless, some limitations of the current study also need to be considered. First, as with all MR studies, it is difficult to confirm a lack of bias due to horizontal pleiotropy. Thus, MR-Egger regression and the MR-PRESSO global test were used to detect widespread horizontal pleiotropy [36, 37]. Importantly, the results of this study remained robust after the removal of outlier variants detected by the MR-PRESSO outlier test. Second, the sample size for ACP was relatively small, which could limit the statistical power to detect genuine causal relationships. Third, this study is based on individuals of European ancestry. Given the genetic differences between the races, replication studies in other populations will ensure the generalisability of the current findings.
In conclusion, the present MR study systematically elucidated the causal associations between different types of pancreatitis and various lifestyle factors, related diseases, serum parameters, lipid metabolism, glucose metabolism and obesity. This work provides a better understanding of the risk factors for the occurrence and development of pancreatitis and may inform more targeted prevention and treatment strategies.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Author contributions
ZhuL and WZ conceived, designed, and supervised the project. XM, SM, HS, FH, YW and DZ collected data. XM and SM performed statistical analyses. XM and SM wrote the first draft with inputs from QW, ZhaL and WZ. All authors reviewed, revised, and approved the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1091780/full#supplementary-material
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|
---
title: 'Brain effective connectivity and functional connectivity as markers of lifespan
vascular exposures in middle-aged adults: The Bogalusa Heart Study'
authors:
- Kai-Cheng Chuang
- Sreekrishna Ramakrishnapillai
- Kaitlyn Madden
- Julia St Amant
- Kevin McKlveen
- Kathryn Gwizdala
- Ramasudhakar Dhullipudi
- Lydia Bazzano
- Owen Carmichael
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC10043334
doi: 10.3389/fnagi.2023.1110434
license: CC BY 4.0
---
# Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study
## Abstract
### Introduction
Effective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.
### Methods
In this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.
### Results
Women and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of $$p \leq 0.043$$). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of $$p \leq 0.047$$). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of $$p \leq 0.044$$). Women and non-drinkers had better FC-rest metrics (value of $$p \leq 0.004$$).
### Discussion
In a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.
## Introduction
Functional relationships between distinct brain regions in distributed networks have become essential to our understanding of the neural substrates of cognitive function and how they change over the course of development, maturation, aging, and disease progression (Kregel and Zhang, 2007; Eyler et al., 2011; Friston et al., 2013; Dennis and Thompson, 2014). These inter-regional functional relationships, including those derived from functional magnetic resonance imaging (fMRI) data, attempt to go beyond traditional task activation analyses by capturing the dynamics of information flow within the distributed networks (Fellows et al., 2005; Stevens, 2009; Friston, 2011). Most fMRI studies to date have formulated inter-regional functional relationships in terms of signal synchrony (functional connectivity, FC). FC makes no attempt to identify asymmetric relationships between regions, for example relationships wherein the fMRI signal in one region influences the fMRI signal occurring later on in another region. Because FC relationships are symmetric in this way, they are naturally represented using undirected graphs where nodes represent brain regions and edges are drawn between regions with high levels of FC. These graphs have led to new observations about how the brain changes over the course of child development (Bitan et al., 2007; Fair et al., 2010; Jolles et al., 2011; Morken et al., 2017), various brain diseases (Achard and Bullmore, 2007; Fox and Greicius, 2010; Lynall et al., 2010; Van Den Heuvel and Pol, 2010; Gao and Wu, 2016; Geng et al., 2018; Cao et al., 2020), and drug treatment (Wong and Stevens, 2012; Hutcheson et al., 2015; Sarpal et al., 2016; Vai et al., 2016; Cao et al., 2020), as well as how brain functioning relates to cognitive functioning (Supekar et al., 2008; Brier et al., 2014; Archer et al., 2016; Contreras et al., 2020; Sun et al., 2020; Zheng et al., 2021). Several studies have used these graph metrics to suggest that there are aberrant FC patterns in aging (Achard and Bullmore, 2007; Andrews-Hanna et al., 2007; Damoiseaux et al., 2008; Meunier et al., 2009; Mayer et al., 2011; Wu et al., 2011), cognitive impairment (Wang et al., 2006; Allen et al., 2007; Zhang et al., 2009; Sheline and Raichle, 2013), and cardiometabolic disease (Friston et al., 1993; Carnevale et al., 2020). Therefore, methods for quantifying network-level brain functional relationships are currently of intense research interest (Goebel et al., 2003; Sporns, 2007; Deshpande et al., 2009; Liao et al., 2009; Zhou et al., 2011; Nauta et al., 2019; Li et al., 2020; Ambrosi et al., 2021).
A much smaller number of fMRI studies have assessed effective connectivity (EC) between brain regions–the causal influence that functional activity in a source region exerts over functional activity in a target region (Friston, 2011; Friston et al., 2013). EC fundamentally differs from FC as it focuses on more complex and asymmetric relationships between brain regions (Horwitz et al., 2005; Stevens, 2009; Friston, 2011; Gürcan, 2014). These relationships may be either excitatory or inhibitory in nature (Wilson and Cowan, 1972; Tagamets and Horwitz, 1998; Horwitz et al., 2005). Because there is an inherent asymmetry between source and target regions, source-target relationships are naturally represented via directed graphs. EC characteristics have been calculated in clinical conditions of interest such as aging (Hinault et al., 2019), cognitive impairment (Luo et al., 2019; Sun et al., 2020), and cardiometabolic disease (Chand et al., 2017).
To our knowledge there have been no head-to-head comparisons of EC and FC in terms of how they associate with factors relevant to various aspects of brain health in a healthy middle-aged cohort. Previous studies including both FC and EC analysis have suggested that EC may be superior to FC for discriminating between brain disease groups, such as stroke patients with differing prognoses (Geng et al., 2018; Adhikari et al., 2021). Others have compared EC and FC patterns that emerge during execution of certain cognitive tasks (Parhizi et al., 2018; Silva et al., 2019). Additional studies have identified different patterns of age-related differences between EC and FC as well as differences among young adults of differing APOE genotypes (Archer et al., 2016; Zheng et al., 2021) To our knowledge, none of these prior studies have compared EC and FC in terms of how they relate to a multi-faceted array of prominent risk factors for late-life cognitive decline among cognitively healthy middle aged adults. In addition, none of these prior methods utilized an EC method that was both nonlinear (modeling nonlinear relationships between signals in source and target regions) and conditional (accounting for the effects of other regions on the target when modeling source-target relationships). Nonlinear source-target relationships are important to capture because they are believed to represent common cases in neuroscience (Aertsen et al., 1989; Buxton et al., 2004; Grosmark and Buzsáki, 2016), while conditional modeling is important because it reduces the potential for identifying spurious source-target relationships driven by a separate, common source (Chen et al., 2004; Zhou et al., 2009a,b). To address these limitations, we used a novel machine learning based method (Chuang et al., 2021, 2022) to assess nonlinear and conditional EC.
An additional limitation in the literature is that the vast majority of FC and EC analyses have been applied to resting-state rather than task-based fMRI data. Exceptions to this rule have been analyses of differences in FC during task performance between clinically-defined groups (Dennis et al., 2010), age-related changes in task-based fMRI EC (Archer et al., 2016; Hinault et al., 2019), and associations between task-based FC and cognitive function (Koshino et al., 2005; Barch et al., 2013; Monti et al., 2014; Jiang et al., 2020). To our knowledge, only two papers to date have directly compared task-based to resting-state EC, with suggestions that EC information derived from task-based fMRI is richer than corresponding data derived from resting-state fMRI (Archer et al., 2016; Voigt et al., 2021). For this reason, we compared EC and FC measures between resting-state and task-based data.
In this study, we conducted a head-to-head comparison between EC and FC graph metrics derived from task-based and resting-state fMRI in terms of how they correlated with known risk factors for late-life brain health as well as measures of cognitive function in a healthy middle-aged cohort. We used a standard FC method and a state-of-the-art EC method to generate undirected and directed graph representations of individual interregional functional relationships. Metrics derived from these graphs were then evaluated in terms of their associations with demographic, cardiometabolic, and cognitive measures from a middle-aged epidemiological sample.
## Study participants
The Bogalusa Heart Study began in 1973 as a community-based cohort study of atherosclerosis and risk factors for cardiovascular disease in a Black and White population of children in a rural town in southeastern Louisiana (Berenson, 2001). Participants with a history of stroke or TIA were excluded from the analysis in this study. At the end, 100 participants completed a 3 T brain MRI at Pennington Biomedical Research Center, as well as cardiometabolic measurements and cognitive tests at the Bogalusa Heart Study clinic in Bogalusa, Louisiana (Table 1). Participants in this study provided informed consent. The study was overseen by the Institutional Review Board of Pennington Biomedical Research Center. All Bogalusa Heart *Study data* may be made available following an approval process through the Bogalusa Heart Study Steering Committee.
**Table 1**
| N | 100 |
| --- | --- |
| Sex (% male) | 33 |
| Race (% African American) | 21 |
| Age at MRI (years) | 54.2 ± 4.3 |
| Education (%) | 1.5 middle, 24.6 high, 29.2 vocational, 33.8 college, 10.9 post graduate |
| Smoking (% smoker) | 20 |
| Drinking (% drinker) | 5 |
| BMI (kg/m2) | 31.2 ± 6.4 |
| SBP (mm Hg) | 119.7 ± 14.5 |
| DBP (mm Hg) | 76.0 ± 8.6 |
| Hemoglobin A1c (%) | 5.7 ± 1.1 |
| Fasting glucose level (mg/dL) | 96.0 ± 21.6 |
| HOMA-IR score | 3.5 ± 2.9 |
| Fasting insulin level (mIU/mL) | 13.6 ± 9.7 |
| APOE-ε4 (% APOE-ε4/ε4 or ε4/ε3 carriers) | 26 |
| Gray matter volume on MRI (% of TCV) | 43.3 ± 1.62 |
| White matter volume on MRI (% of TCV) | 36.4 ± 1.49 |
| WMH volume on MRI (% of TCV) | 0.04 ± 0.05 |
| Z-standardized mean score for all cognitive measures | 1.9 ± 3.7 |
| Digit span forwards score | 11.8 ± 2.3 |
| Digit span backwards score | 7.9 ± 2.0 |
| Logical memory I total score | 22.7 ± 6.3 |
| Logical memory II total score | 17.4 ± 6.2 |
| Logical memory II recognition total score | 24.6 ± 2.4 |
| Trail Making Test A (seconds) | 24.9 ± 7.7 |
| Trail Making Test B (seconds) | 56.5 ± 23.7 |
| Digit coding score | 64.9 ± 14.9 |
| Vocabulary score | 31.0 ± 9.0 |
| Word reading score | 43.1 ± 7.8 |
## Clinical measurements
Validated questionnaires were used to obtain demographic and lifestyle variables, specifically, age, race, sex, cigarette smoking, and alcohol consumption. Adiposity was characterized by the calculation of Body Mass Index, BMI (kg/m2) from the height and weight collected by a stadiometer. Duplicate measures of height and weight for each study participant were used to calculate BMI. Similarly, the calculated arithmetic average of blood pressure triplicate measures obtained on the right arm of the participants in a relaxed, sitting position using sphygmomanometers was used to calculate systolic and diastolic blood pressure (SBP and DBP). APOE genotyping was performed directly in the collected serum sample from venipuncture using a method based on isoelectric focusing of delipidated serum followed by immunoblotting using rabbit antihuman APOE antiserum (Srinivasan et al., 2001) Fasting measures of hemoglobin A1c, fasting glucose, HOMA-IR, and fasting insulin were collected using standardized methods (Foster and Berenson, 1987).
## Cognitive measurements
Cognitive tests included logical memory I (narrative memory free recall), logical memory II (long term narrative memory free recall), and logical memory II R (long term memory recognition) from the Wechsler Memory Scale III; digit span forward and backward from the Wechsler Adult Intelligence Scale III as well as Trail Making Tests A and B. A global cognition composite score was calculated by averaging the z-scores of each of the domain tests (Lynall et al., 2010; Mayer et al., 2011). Lesser scores on all cognitive measures except the Trails Making Tests are indicators of poorer cognitive health.
## Structural MRI acquisition and processing
Brain MRI scans were performed on a GE Discovery 3 T scanner at Pennington Biomedical Research Center. T1-weighted structural MPRAGE (voxel size, 1 × 1 × 1 mm3; voxel array, 256 × 256 × 176; flip angle, 8 degrees; NEX, 1) and 2D FLAIR (voxel size, 0.9 × 0.9 × 3 mm3; voxel array, 256 × 256 × 50; flip angle, 111 degrees; NEX, 1) images were acquired and analyzed using in-house software, which has been described elsewhere (Yoshita et al., 2006; DeCarli et al., 2008; Carmichael et al., 2012, 2019). Key FLAIR processing steps include manual removal of non-brain elements from the FLAIR image by operator guided tracing of the dura mater within the cranial vault, resulting in delineation of a total cranial volume (TCV) region; MRI non-uniformity correction of the TCV (DeCarli et al., 1996); thresholding of TCV into brain and non-brain tissues (DeCarli et al., 1992); fitting a single Gaussian distribution to the brain tissue intensity distribution and labeling of all voxels with intensity >3.5 standard deviations above the mean as white matter hyperintensities (WMH; DeCarli et al., 2005). Key T1-weighted image processing steps include MRI non-uniformity correction (Fletcher et al., 2012a); and segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) by a Bayesian maximum-likelihood expectation–maximization algorithm (Fletcher et al., 2012b). The primary measures of interest in subsequent analysis were volumes of WMH, GM, and WM, each expressed as a percentage of TCV.
## Functional MRI acquisition and preprocessing
Axial 2D gradient echo EPI BOLD were acquired for both task-based and resting-state fMRI (voxel size, 3.5 × 3.5 × 3.5 mm3; voxel array, 64 × 64 × 44; flip angle, 90 degrees; TE, 30 ms; TR, 3000 ms; NEX, 1). Two hundred and 160 volumes were acquired over the course of task execution and rest, respectively. Preprocessing of fMRI included slice timing correction, head motion correction (head rotation was required to be <1.5 degree and translation was required to be <1.5 mm at every fMRI time point. All fMRI data sets in this study met that criterion.), smoothing, co-registration to the T1-weighted image, and warping of T1-weighted data to a standard coordinate frame (using Statistical Parametric Mapping 12). Cardiac and respiratory time series were regressed out of the data using RETROICOR and REST Toolkit (Glover et al., 2000; Song et al., 2011). Twenty four regions of interest (ROIs) identified in previous fMRI studies as involved in execution of the Stroop task (Sheu et al., 2012) and 33 ROIs previously identified as default mode network (DMN) regions in resting-state fMRI studies (Buckner et al., 2008; Andrews-Hanna et al., 2010; Alves et al., 2019) were identified, and a single summary fMRI time series was extracted from each ROI using a 3 × 3 × 3 block of voxels in each scan by in-house MATLAB script for EC and FC analysis.
## Stroop task
The Stroop task tested inhibitory control in the context of negative feedback and time-pressured responses (Sheu et al., 2012). In each trial, for 400–5,000 ms participants saw one probe word and four target words that were names of colors. The task was to identify the target word whose color matched that of the probe. In the congruent (incongruent) condition, word meaning matched (did not match) the color it was printed in. Correct (incorrect) responses on 3 consecutive incongruent trials prompted a 300 ms reduction (increase) in stimulus duration. Four 52–60 s incongruent trial blocks were interleaved with 4 congruent trial blocks, each of which had the same number of trials as the previous incongruent block. The inter-block interval was 10–17 s.
## Resting-state fMRI
Resting-state fMRI was collected for 8 min using the same pulse sequence parameters as the task-based fMRI data. Participants were instructed to keep their eyes open and to stare at a white crosshair on a black background throughout acquisition.
Age at MRI was associated with EC based metrics but not with any FC based metric. Greater age was associated with worse EC based degree, clustering coefficient, transitivity, global efficiency, and characteristic path length (maximum value of $$p \leq 0.038$$). Gender was associated with both EC and FC based metrics. Better EC based assortativity in-out (value of $$p \leq 0.017$$) was found for males, but better FC based strength (value of $$p \leq 0.004$$) was found for females. Self-reported history of drinking was associated with both EC and FC based metrics. Better EC based small-worldness (value of $$p \leq 0.015$$) and better FC based strength (value of $$p \leq 0.009$$) was found for non-drinkers. Better EC based assortativity in-out (p-value = 0.017) was found for males, but better FC based strength (p-value = 0.004) was found for females. Race, education, and smoking history were not associated with any EC based metric (minimum p-value = 0.068), nor any FC based metric (minimum p-value = 0.077).
BMI and WMH were associated with EC based metrics but not with any FC based metric. Greater BMI was associated with worse EC based strength (p-value = 0.015). Greater fasting glucose, HOMA-IR, and fasting insulin were associated with worse EC based strength (maximum p-value = 0.035). Greater WMH was associated with worse EC based strength (p-value = 0.025). Systolic and diastolic blood pressure, APOE-ε4, and gray/white matter volume were not associated with any EC based metric (minimum p-value = 0.052), nor any FC based metric (minimum p-value = 0.137).
Logical memory II total score and word reading score were associated with EC based metrics but not with any FC based metric. Greater logical memory II total score and word reading score were associated with worse EC based strength (p-value = 0.044 and 0.038, respectively). None the rest of the cognitive measures were associated with any EC based metric (minimum p-value = 0.250), nor any FC based metric (minimum p-value = 0.228).
## Effective connectivity
The deep stacking network method used to estimate nonlinear Granger causality from the fMRI time series at a source region to that of a target region conditioned on the time series at other source regions has been described previously (Chuang et al., 2021, 2022). The code that supported the findings of this study are available from the corresponding author upon reasonable request. Briefly, the Granger causality of source ROI1 to target ROI2, conditioned on other sources ROIs (ROI1→ROI2|ROIs), is defined in terms of the reduction in prediction error when ROI1, ROI2, and other sources ROIs are used to reconstruct ROI2, compared to prediction error when only ROI2 and other sources ROIs are used to reconstruct ROI2. If incorporating ROI1 improves the reconstruction of ROI2 after accounting for effects of ROI2 and other sources ROIs, the Granger causality index GCindexROI1→ROI2|ROIs will be a larger positive number. Complex causal relationships among several time series can be disentangled by calculating conditional Granger causality with differing assignments of time series to the roles of ROI1, ROI2, and other sources ROIs. To reconstruct target time series from source time series, we used deep stacking networks, which consist of a set of convolutional neural network modules, each trained to reconstruct one time series based on another. Given time series from source and target regions collected from all participant fMRI scans, we used K-fold cross validation to train the deep stacking network K times, each time quantifying GCindexROI1→ROI2|ROIswithin the test data. We consider the evidence for a particular conditional Granger causal relationship strong at the group level when the mean of these GCindexROI1→ROI2|ROIs estimates is statistically significantly >0 in a one-tailed student’s t-test (value of $p \leq 0.05$). Each such causal relationship resulted in an edge originating at the ROI1 node, terminating at the ROI2 node, in the group level graph. The group level graph was constructed for the purpose of visualizing overall trends in causal relationships across the entire set of scans. We also calculated individual-level graphs that allowed us to quantify graph metrics from each scan. To construct an individual-level graph we started by omitting the individual’s scan from the overall data set and randomly partitioning the remainder into K disjoint sets. The deep stacking network was trained on each of the K disjoint sets, and GCindexROI1→ROI2|ROIswas quantified from just the omitted individual’s scan. We consider the evidence for a particular conditional Granger causal relationship strong when the mean of the K GCindexROI1→ROI2|ROIs estimates is statistically significantly >0 in a one-tailed student’s t-test (value of $p \leq 0.05$). Each such significant GCindexROI1→ROI2|ROIs resulted in a directed edge originating from the graph node corresponding to ROI1, terminating at the graph node corresponding to ROI2, in the individual directed graph representing EC relationships.
Inspired by Jia et al. [ 2016] and Zamora Esquivel et al. [ 2019], we used CNN-ACKs in our DSNs architecture to estimate causal relationships. An CNN-ACK is trained to transform the source (s) time series into the target time series. An ACK is defined by a dynamic filter that changes its weights automatically depending on the data in the source time series. The ACK is generated by convolving filters with source time series and using an activation function to transform the result into target time series. The first step is that at each timestep (t), the (1 × 6) hidden layer output is calculated as the dot product of six 1 × 2 filter with the source time series. Then, the Parametric Rectified Linear Unit (PReLU) activation function is applied to each element of hidden layer output to generate the ACK. The estimate of the target time series is the dot product of ACK with the source time series. In each CNN-ACK, the six convolving filters (2 weights and 1 bias terms for each filter) and the parameters of PReLU (6 weights for each timestep) are the learnable parameters. The outputs of each CNN-ACK are provided as inputs to an element-wise weighted sum to produce the final estimate of the target time series. We used the TensorFlow and Keras software packages to build our network architecture and optimized it with the Adam optimizer (β1 = 0.9, β2 = 0.999) with a learning rate of 0.001 to minimize the loss function of mean squared error between the predicted target time series and the actual target time series (Chollet, 2015; Abadi et al., 2016).
## Functional connectivity
We used the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) to calculate conditional FC between a pair of regions, ROI1 and ROI2, while accounting for the effects of all other regions ROIs (ROI1 ↔ ROI2|ROIs). Following common practice, the matrix of partial Pearson correlations (Pearson’s r) among all possible ROI1 and ROI2 was calculated, and after statistically significant correlation values (value of $p \leq 0.05$) were retained to construct the individual-level graph (Brier et al., 2014; Meng et al., 2018; Carnevale et al., 2020; Contreras et al., 2020; Zhang et al., 2021). Each such significant conditional FC ROI1 ↔ ROI2|ROIs resulted in an undirected link between ROI1 and ROI2 in the graph representing FC relationships. Group consensus graph was calculated as the mean of all individual-level graphs.
## Graph metrics
The common global measures graph metrics representing the different aspects of a brain network (Figure 1) were calculated from directed graphs resulting from EC as well as undirected graphs resulting from FC, using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010). The definition of each graph metric can be found in Appendix Table A1. Each graph metric had analogs for both directed and undirected graphs. In previous brain networks studies (Achard and Bullmore, 2007; Meunier et al., 2009; Sanz-Arigita et al., 2010; Petti et al., 2013; Brier et al., 2014; Parhizi et al., 2018; Sun et al., 2020), greater degree, clustering coefficient, transitivity, modularity, global efficiency, assortativity in-out, and small-worldness; as well as lower strength, characteristic path length, and flow coefficient; have been associated with better brain health.
**Figure 1:** *Graph metrics calculated from EC and FC graphs. Categories of graph metrics are shown in bold. Individual graph metrics within each category are listed.*
## Statistical analysis
Statistical analysis focused on relating demographic, lifestyle, cardiometabolic and cognitive measures as predictors to EC and FC based graph metrics as outcomes. The associations between predictors and outcomes were evaluated in multivariable linear regression models, and statistically significant associations at the value of $p \leq 0.05$ level are reported in the results. Each model included gender, race, and age at the time of MRI as nuisance covariates, along with one cardiometabolic or cognitive predictor. The set of cardiometabolic and cognitive predictors included BMI, SBP, DBP, APOE-ε4, hemoglobin A1c, fasting glucose, HOMA-IR, fasting insulin, volumes of WMH, GM, and WM, z-standardized mean score for all cognitive measures, digit span forwards/backwards score, logical memory I/II/II recognition total score, digit coding score, vocabulary score, word reading score. We evaluated differences between analogous EC and FC metrics qualitatively, in terms of differences in how they related to the demographic, cardiometabolic and cognitive predictors.
## Results
The group-consensus EC and FC graphs for Stroop task are shown in Figure 2. Figure 3 shows the group-consensus EC and FC graphs for resting-state fMRI. For both Stroop task and resting-state fMRI, almost all of the edges in the group-consensus EC graph were significant; however, few of the edges in the group-consensus FC graph were significant. A specific pattern of several ROIs appearing to be a source for specific target ROIs, including left and right caudate, cerebellar hemisphere, and cerebellar tonsil, has been identified in group-consensus EC for resting-state fMRI. No specific pattern has been found for the rest of the group-consensus graphs. The means and standard deviations of graph metric over all participants have been shown in Table 2. The associations between demographic, cardiometabolic, and cognitive measures and EC/FC based metrics for Stroop task fMRI are shown in Table 3. The associations between demographic, cardiometabolic, and cognitive measures and EC/FC based metrics for resting-state fMRI are shown in Table 4. Figure A1 and Table A2 show the associations between demographic, cardiometabolic, and cognitive measures and EC/FC based metrics among 24 task-related ROIs for Stroop task fMRI and eight core DMN ROIs for Stroop task fMRI.
**Figure 2:** *Identified group-consensus (A) EC (directed graph) and (B) FC (undirected graph) among 24 task-related ROIs for Stroop task fMRI. The connectivity is shown in black if it is not statistically significant. AG, angular gyrus; AI, anterior insula; Cb, cerebellum; CG, cingulate gyrus; FG, fusiform gyrus; IFG, inferior frontal gyrus; MTG, middle frontal gyrus; OG, occipital gyrus; P, precuneus; TG, temporal gyrus; T, thalamus; ACC, anterior cingulate cortex; FG, frontal gyrus; LN, lentiform nucleus; MTG, middle temporal gyrus; PI, posterior insula; SFG, superior frontal gyrus.* **Figure 3:** *Identified group-consensus (A) EC (directed graph) and (B) FC (undirected graph) among 33 DMN ROIs for resting-state fMRI. The connectivity is shown in black if it is not statistically significant. VMPFC, ventro-median prefrontal cortex; AMPFC, antero-median prefrontal cortex; DPFC, dorsal prefrontal cortex; PCC, posterior cingulate cortex; Rsp, retrosplenial cortex; PH, parahippocampal region; Amy, amygdala; VLPFC, ventrolateral prefrontal cortex; TP, temporal pole; MTG, middle temporal gyrus; PPC, posterior parietal cortex; T, thalamus; BF, basal forebrain; C, caudate; CbH, cerebellar hemisphere; CbT, cerebellar tonsil; MidB, midbrain.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4
## Stroop task fMRI
Age at MRI was not associated with either EC based metric (minimum value of $$p \leq 0.059$$), or FC based metric (minimum value of $$p \leq 0.065$$). Better EC based assortativity in-out (value of $$p \leq 0.043$$) and better FC based flow coefficient (value of $$p \leq 0.047$$) were found for females compared to males. Better EC based assortativity in-out and strength (value of $$p \leq 0.022$$ and 0.042, respectively) were found for white Americans compared to African Americans.
Systolic and diastolic blood pressure were associated with EC based metrics but not with any FC based metric. Greater SBP was associated with worse EC based characteristic path length (p-value = 0.038), but with better EC based modularity and small-worldness (maximum p-value = 0.018). Greater DBP was associated with worse EC based degree, clustering coefficient, transitivity, global efficiency, and characteristic path length (maximum p-value = 0.008). Instead, APOE-ε4 was not associated with any EC based metric, but with FC based metrics. Worse FC based small-worldness and strength were found in ε4 allele carriers (p-value = 0.026 and 0.032, respectively). Greater hemoglobin A1c were associated with worse FC based strength (maximum p-value = 0.029). Moreover, white matter hyperintensities volume was associated with both EC and FC based metrics. Greater WMH was associated with worse EC based strength (p-value = 0.021), FC based modularity and characteristic path length (p-value = 0.001 and 0.017, respectively). BMI, glycemic measures, and gray/white matter volume were not associated with any EC based metric (minimum p-value = 0.100), nor any FC based metric (minimum p-value = 0.115).
Vocabulary score was associated with EC based metrics but not with any FC based metric. Greater vocabulary score was associated with worse EC based clustering coefficient and transitivity (p-value = 0.017 and 0.025, respectively). Instead, digit span backwards score was not associated with any EC based metric, but with FC based metrics. Greater digit span backwards score was associated with worse FC based small-worldness and flow coefficient (p-value = 0.041 and 0.043, respectively). None the rest of the cognitive measures were associated with any EC based metric (minimum p-value = 0.068), nor any FC based metric (minimum p-value = 0.097).
## Discussion
In a cohort of nominally healthy middle aged individuals, almost all of the edges in the group-consensus EC graph were significant; however, few of the edges in the group-consensus FC graph were significant. We believe this difference is because EC can identify a wider range of significant relationships than FC can: EC can identify causal relationship across a wide range of time lags between the source region and the target region; FC, on the other hand, only accounts for synchrony (i.e., zero time lag) relationships between the regions. Also, EC and FC had differential associations with demographic, cardiometabolic, and cognitive measurements in both task-based and resting-state fMRI. The task-based results suggested that certain health-related measures associated specifically with EC metrics, and others associated specifically with FC metrics. The resting-state results similarly suggested differential associations between EC and FC, and that EC metrics had more associations with health-related measures than FC metrics did. There are several ways in which the imposition of task conditions could affect EC and FC values. First, EC and FC may have a different temporal structure in task fMRI data due to the time-varying nature of task conditions, which lead to time-varying cognitive loads on various brain regions. EC and FC may have different spatial structures as well when applied to task fMRI data, as the task demands may force the brain to recruit different brain regions for execution. Finally, the imposition of task conditions could cause greater fluctuations in the BOLD signal than are seen during rest, and this amplitude difference may by itself cause differences between task and rest connectivity measures. Therefore, the first implication of these findings is that future studies of midlife brain health should consider both EC and FC analyses to get a more complete picture of functional network related aspects of brain health. The second implication is that future studies of midlife brain health should consider collecting both task-based and resting-state fMRI scans, again to get a more complete picture of relevant aspects of brain health.
Our results shows that either EC or FC showed a significant correlation with the network metric but not both ─ there was no case where both FC and EC showing a significant correlation. Differential associations for EC compared to FC are plausible, given that they are quantifying distinct properties of the underlying fMRI signals. A key difference between EC and FC analysis is that EC analysis can assess a specific form of causal relationships, while FC analysis captures correlation (Stigler, 2005; Altman and Krzywinski, 2015; Moreau and Dumas, 2021). Altman et al. has pointed out that a causal relationship (EC) can arise between variables in the presence or absence of a correlation (FC), and therefore we cannot equate causality with correlation in either direction (Altman and Krzywinski, 2015). This key difference, in theory, could account for more statistically significant edges and differential associations for EC compared to FC. Our results suggest that this difference between what EC and FC calculates is actually relevant to real-world data sets containing fMRI and health information. Thus, we suggest calculating EC as complementary to FC analyses. Calculating both types of metrics adds nothing to acquisition time but does add to the computational burden of post-processing.
Our findings of relationships between demographic measures (age and gender) and EC or FC aligned well with previous literature. *In* general, greater degree, clustering coefficient, transitivity, modularity, global efficiency, assortativity in-out, and small-worldness; as well as lower strength, characteristic path length, and flow coefficient; have been associated with better brain health (Achard and Bullmore, 2007; Meunier et al., 2009; Sanz-Arigita et al., 2010; Petti et al., 2013; Brier et al., 2014; Parhizi et al., 2018; Sun et al., 2020). Greater degree suggests a larger number of functional connections between the current region and other regions. Greater clustering coefficient, transitivity, modularity, and lower flow coefficient suggest greater inter-regional connectivity among a set of regions. Greater modularity, global efficiency, and small-worldness, as well as lower characteristic path length, suggest greater efficiency of functional network organization from the perspective of information transfer across the network. Greater assortativity in-out coefficient suggests that the brain regions tend to connect to other brain regions that have similar degree. Lower strength suggests that a region has several weak functional connections with a large set of other regions, rather than a few strong connections. Prior studies demonstrated lower FC based degree and modularity, and lower EC based small-worldness, in middle-aged or old research participants compared to young participants, based on resting-state fMRI (Meunier et al., 2009; Petti et al., 2013; Song et al., 2014; Archer et al., 2016). Our results similarly suggested that EC based metrics were intuitively associated with age within this cohort of middle aged individuals, based on resting-state scans. Young age is usually correlated with better brain health in healthy populations. Also, resting-state FC studies have reported that women may have greater graph node degree within the default mode network, compared to men (Bluhm et al., 2008; Allen et al., 2011; Zhang et al., 2018); we similarly found better EC and FC based metrics among women, compared to men, in both task-based and resting-state scans. To our knowledge, there have been no reported observations of race differences in EC or FC to date in middle aged individuals; we report what may be the first finding of poorer task-based EC among African Americans compared to corresponding whites.
Many of our results for cardiometabolic measures (blood pressure, BMI, WMH, APOE-ε4) aligned well with previous literature. We report a lack of association between FC and blood pressure measures, similar to prior studies showing no association to blood pressure (Song et al., 2011; Zhou et al., 2011; Hinault et al., 2019) as well as others showing no differences between hypertensive and normotensive groups (Carnevale et al., 2020). We also report significant associations between blood pressure and EC, as in prior studies (Chand et al., 2017; Bu et al., 2018). Our finding of an association between worse resting-state EC and greater BMI is reminiscent of an earlier finding of worse resting-state EC among obese young adults compared to normal-weight young adults (Duan et al., 2020), although we did not replicate earlier findings of reduced resting-state FC among obese young adults (Baek et al., 2017; Meng et al., 2018; Ottino-González et al., 2021). We report that greater WMH burden is associated with poorer EC during task and rest along with poorer FC during rest. We believe that methodological differences between studies may account for many of these discrepancies. For example, some prior studies (Chen et al., 2019, 2021) focused solely on FC between the thalamus and the whole brain, while other studies explored FC solely within the default mode network. Standardizing fMRI post-processing pipelines to minimize such methodological differences has been notoriously difficult. We are providing what may be one of the first reports of significant associations between APOE-ε4 carrier status and task-based FC. APOE carrier status has previously been shown to be associated with a variety of different indicators of poorer neurobiological health, including degradation of synaptic and neuronal function (Contreras et al., 2020; Turney et al., 2020). Worse FC-task based graph metrics were found in ε4 allele carriers, suggesting that such APOE-related decrements in neuronal and synaptic health may culminate in connectivity deficits. Overall, our results align well with the intuitive notion that better graph metrics should associate with indicators of better brain health. These results have substantial agreement with prior literature, thus lending some plausibility to the current findings. Discrepancies between our findings and previous reports could be accounted for by numerous methodological and study population differences. Moreover, several prior studies reported the associations between race and APOE-e4 (Beydoun et al., 2021; Weiss et al., 2021). However, there was no statistically significant correlation between the graph metrics and the interaction terms of race and APOE-e4. Prior studies that suggest such interactions generally assessed cognitive outcome measures while our outcomes are brain connectivity variables; we speculate that interactive effects on cognition may be exerted through other mechanisms besides brain connectivity.
Our finding of a significant association between one type of cognitive measures (digit span backwards/forwards scores) and task-based FC aligns well with prior reports with task-based fMRI data (Ginestet and Simmons, 2011; Stanley et al., 2015). We are unaware of any prior reports on associations between EC and cognitive function, and provide what may be one of the first reports of such associations here. The closest we can get to this finding in the current literature is the literature on FC in disease populations, including mild cognitive impairment and Alzheimer’s disease (Grady et al., 2003; Wang et al., 2006; Zhang et al., 2017). Several of these studies reported that FC is actually greater in those with worse disease status, suggesting that elevating FC may be a compensatory mechanism triggered by the disease state. Similarly, we found that better cognitive function scores were associated with worse EC-based graph metrics. While there have been numerous reports of associations between resting-state FC and cognitive functioning or differences in resting-state FC between cognitively healthy and unhealthy groups (Van Den Heuvel and Pol, 2010; Liang et al., 2011; Zhou et al., 2013; Zhang et al., 2017), these prior studies largely did not take place entirely within a cognitively healthy middle-aged population. This difference from prior literature may account for our lack of finding of such associations in our data.
A key strength of the study is its comprehensive nature, with comparisons among multiple forms of brain connectivity (EC and FC), both task-based and resting-state fMRI, and multi-faceted assessment of individuals in terms of demographics, cardiometabolic risks, and cognition. The use of an established, deeply characterized population-based cohort is another key strength. Future studies should consider longitudinal measurement of cardiovascular and cognitive measures from as young an age as possible. One limitation to this study is the relatively small sample size of the dataset (100 participants). It would be helpful to verify the robustness of the results with a public dataset with larger sample size. Another limitation is our ROI-based approach to calculating EC and FC, i.e., we only calculated connectivity among regions previously identified as activated by the Stroop task, or among those previously identified as being in the default mode network. This approach may miss certain interesting functional connections outside of the known task-related regions, but unlike whole-brain analyses it offers a lower risk of the false positives that have contributed to the replication crisis currently roiling the fMRI field (Bennett and Miller, 2010; Sheu et al., 2012). The other possible limitation is that we did not adjust for inter-individual or inter-regional differences in the hemodynamic response function to stimuli. Some studies have suggested that such adjustments are important (Smith et al., 2012; Rangaprakash et al., 2017), while others suggest they are irrelevant (Seth et al., 2013; Wen et al., 2013).
## Conclusion
In a diverse, cognitively healthy, middle-aged community sample, graph metrics derived from EC based directed graphs and FC based undirected graphs in both task-based and resting-state scans associated differentially with recognized demographic, cardiometabolic and cognitive indicators of brain health.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Pennington Biomedical Research Center. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
K-CC and OC: experimental design, data interpretation, and manuscript drafting. K-CC, SR, KaM, JS, KeM, KG, RD, and LB: data collection. K-CC: data analysis. All authors contributed to the article and approved the submitted version.
## Funding
Funding for this work was supported by National Institutes of Health grants R01AG041200 and R01AG062309 as well as the Pennington Biomedical Research Foundation, Baton Rouge, LA.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2023.1110434/full#supplementary-material
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|
---
title: Slowed gastrointestinal transit is associated with an altered caecal microbiota
in an aged rat model
authors:
- Nabil Parkar
- Julie E. Dalziel
- Nick J. Spencer
- Patrick Janssen
- Warren C. McNabb
- Wayne Young
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC10043340
doi: 10.3389/fcimb.2023.1139152
license: CC BY 4.0
---
# Slowed gastrointestinal transit is associated with an altered caecal microbiota in an aged rat model
## Abstract
Gastrointestinal (GI) motility is largely dependent upon activity within the enteric nervous system (ENS) and is an important part of the digestive process. Dysfunction of the ENS can impair GI motility as is seen in the case of constipation where gut transit time is prolonged. Animal models mimicking symptoms of constipation have been developed by way of pharmacological manipulations. Studies have reported an association between altered GI motility and gut microbial population. Little is known about the changes in gut microbiota profile resulting specifically from pharmacologically induced slowed GI motility in rats. Moreover, the relationship between gut microbiota and altered intestinal motility is based on studies using faecal samples, which are easier to obtain but do not accurately reflect the intestinal microbiome. The aim of this study was to examine how delayed GI transit due to opioid receptor agonism in the ENS modifies caecal microbiota composition. Differences in caecal microbial composition of loperamide-treated or control male Sprague Dawley rats were determined by 16S rRNA gene amplicon sequencing. The results revealed that significant differences were observed at both genus and family level between treatment groups. Bacteroides were relatively abundant in the loperamide-induced slowed GI transit group, compared to controls. Richness and diversity of the bacterial communities was significantly lower in the loperamide-treated group compared to the control group. Understanding the link between specific microbial species and varying transit times is crucial to design interventions targeting the microbiome and to treat intestinal motility disorders.
## Introduction
Gastrointestinal (GI) motility is an integral part of digestive function. The enteric nervous system (ENS) plays a major role in control of GI motility (Spencer and Hu, 2020). Gut transit time, which refers to the transit of luminal content along the GI tract, is commonly used as a marker of gut motility and function (Corsetti et al., 2019). Measurement of gut transit time is relevant when addressing GI motility disorders such as irritable bowel syndrome (IBS) and constipation.
The mammalian GI tract is colonized by a diverse population of microbial communities. These gut microbes are vital in maintaining host health (Thursby and Juge, 2017). Numerous studies have pointed out that an association exists between the gut microbiota and GI motility, and that this relationship is likely to be bidirectional (Quigley, 2011; Zhang et al., 2021). Experiments using germ free (GF) animal models have demonstrated that a lack of microbial colonization correlates with altered ENS functions such as delayed gastric emptying and slowed GI transit (Hyland and Cryan, 2016). Conversely, alterations in GI motility can modify the resident microbial population as seen in the case of Small Intestinal Bacterial Overgrowth (SIBO), a clinical syndrome often associated with altered GI motility (Toskes, 1993). Environmental factors have also been shown to influence gut microbiota composition. In this context, it can be considered that changes in gut motility likely led to changes in microbiota composition and function. For example, some microbial taxa benefit from increases in GI motility, relative to other species adapted to conditions associated with slower motility (Pianka, 1970). This concept is consistent with ecological principles of r/K selection in response to environmental disturbance (Pianka, 1970). As GI transit time decreases, such as with diarrhea, species better adapted to grow rapidly during reduced competition (r-selected) will dominate the gut. In contrast, prolonged colonic transit may facilitate the amplification and colonization of slow-growing species, better adapted to persist in competitive environments (K-selected); these species include metabolically economical taxa. The direct effects of gut motility on specific microbial communities could cascade into broad ecosystem changes as the community is interconnected metabolically. The question of identifying which microbial species are impacted by motility is important because shifts from normal microbiota composition can lead to metabolic changes with possible physiological consequences.
Animal models mimicking symptoms of constipation have been developed by way of pharmacological manipulations. Loperamide is an opioid receptor agonist that works by activating the µ-opioid receptors located in the myenteric plexus of the ENS (Kim et al., 2014). It does not cross the blood brain barrier (Montesinos et al., 2014). Upon binding to the opioid receptors, loperamide decreases the activity of the myenteric plexus, which subsequently reduces the tone of the circular and longitudinal smooth muscles of the gut wall. This in turn reduces propulsion and extends the total stay time of luminal contents (Kim et al., 2014). Through our experiments we have previously shown that loperamide works by inhibiting enteric neuronal activity, delays GI transit, inducing constipation in aged rats (Dalziel et al., 2016). Similarly other studies have reported that loperamide inhibits colonic peristalsis and intestinal water secretion causing delayed GI transit time (Hughes et al., 1984; Wintola et al., 2010). Loperamide-induced slowed transit is therefore considered to be a model of spastic constipation due to increased colonic contractions and inhibition of stool frequency (Takasaki et al., 1994). Although the dose and time for loperamide administration has been described to vary among rodent studies, loperamide has shown to be effective in inducing constipation when administered subcutaneously (Chen et al., 2012; Lee et al., 2012; Neri et al., 2012), orally (Wintola et al., 2010) or intra-peritoneally (Jeon and Choi, 2010) at doses ranging from 0.2 to 5 mg/kg body weight for 3 to 7 days. Although longer transit times have been associated with taxa such as Akkermansia, Bacteroides and Alistipes (Asnicar et al., 2021), little is known about the changes in gut microbiota profile resulting specifically from pharmacologically-induced slowed GI motility in rats. Moreover, the relationship between gut microbiota and altered intestinal motility are based on studies using faecal samples, which are easier to obtain but do not accurately reflect the intestinal microbial community (Wang et al., 2019). The caecum serves as a bacterial reservoir that populates the large intestine (Brown et al., 2018). The aim of this study was to examine how slowed GI transit due to opioid receptor agonism in the ENS affects the caecal microbiota.
## Animals
Male Sprague Dawley rats were bred at the AgResearch Ruakura Small Animal Unit (Hamilton, New Zealand) and raised in group housing with littermates to 18 months of age (804 ± 13 g) (Dalziel et al., 2017). The rats were maintained under a 12-h light/dark cycle with water and food provided ad libitum. Rats were fed a nutritionally balanced diet (OpenStandard Rodent Diet, Research Diets, Inc., New Brunswick, NJ, USA) as previously described (Dalziel et al., 2017). They were monitored three times weekly for General Health Score (1–5; NZ Animal Health Care Standard), weight and food intake. The experiment was performed in accordance with the Animal Welfare Act, 1999 (NZ). The protocol was approved by the AgResearch Grasslands Animal Ethics Committee (Ethics approval No.: AE12933).
## Study design and pharmacological treatment
This study included a loperamide-treated group and a control group with age and weight balanced amongst treatment groups. Loperamide hydrochloride (S2480) was purchased from Selleck Chemicals (Houston, TX, USA). Rats were administered 1 mg/kg/day loperamide (in $100\%$ Dimethyl sulfoxide (DMSO) or DMSO Vehicle only (control) for seven days. The drug dose has been previously determined to be effective over seven days (Dalziel et al., 2016). The route of administration was via a subcutaneous 2 mL capacity slow-release osmotic mini pump (Durect Corporation, Alzet Osmotic Pumps, Cupertino, CA, USA) as previously described (Dalziel et al., 2016). The control group received DMSO vehicle only via the same delivery method. The control group consisted of 13 rats and the loperamide-treated group had 11. Some rats died before the end of the study from age related issues (Dalziel et al., 2017).
## Caecal microbiota
Caecal content samples were collected rapidly after euthanasia, using carbon dioxide inhalation overdose, and snap frozen in liquid nitrogen and stored at -80°C before use. Metagenomic DNA was extracted using the NucleoSpin Soil kit (Macherey-Nagel GmbH, Düren, Germany) according to the manufacturer’s instructions, using SL2 lysis buffer and SC enhancer, with the addition of bead beating for four minutes using a BioSpec Mini Beadbeater 96 (Bartlesville, OK, USA) set to maximum speed.
DNA samples were then analysed by 16S rRNA gene amplicon sequencing using the Illumina MiSeq platform with 2 × 250 bp paired-end sequencing with PCR primers targeting the V3 and V4 region (Illumina, 2013): Forward Primer: 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG Reverse Primer: 5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC PCR thermal cycler conditions were used as specified in the Illumina library preparation protocol (95°C for 3 minutes; 25 cycles of [95°C for 30 seconds, 55°C for 30 seconds, 72°C for 30 seconds]; 72°C for 5 minutes; Hold at 4°C) (Illumina, 2013). Sequence reads were quality trimmed using the following parameters in QIIME 2 (Bolyen et al., 2019): Adapter sequences were removed using the cutadapt function, paired reads joined using vsearch with a minimum overlap of 20 bp, reads were quality trimmed with a 25 q-score cut off, remaining reads denoised and chimera checked using the deblur algorithm. Single nucleotide variants were classified by aligning against the Silva 132 small subunit ribosomal RNA database. Alpha diversity was assessed using the Faith’s Phylogenetic Diversity and Chao1 index. Beta diversity was compared using Principal Coordinate Analysis (PCoA) of weighted unifrac phylogenetic distances. The sampling depth used for alpha and beta diversity analysis was 32000 reads. Differences in taxa were analysed using ANCOM-BC (Lin and Peddada, 2020) with q<0.05 considered significant. Differences in overall community profiles were analysed by permutation multivariate analysis of variation (PERMANOVA) using the anosim function in the ‘vegan’ package for R. R version 1.4.1103 was used for all statistical analyses (R Core Team, 2021). Data is presented as mean percentage +/- SEM.
Sequence reads can be downloaded from the NCBI Sequence Read Archive (SRA) under accession PRJNA819534.
## Taxonomic composition at the phylum level
A total of 10 bacterial phyla were detected in both loperamide-treated and control groups, which included four frequently detected phyla: Bacteroidetes, Actinobacteria, Firmicutes and Proteobacteria, and six minor phyla Tenericutes, Deferribacteres, Patescibacteria, Cyanobacteria, Elusimicrobia and Verrucomicrobia (Figure 1). All 24 samples contained the four frequently found phyla. No more than six samples contained each of the six minor phyla. No clear differences between treatment were observed at the phylum level (q>0.05).
**Figure 1:** *Distribution of the gut microbiota at the Phylum level.*
## Microbiota at the family and genus levels
Analysis of the microbiota at the family level showed significant differences (q<0.05) between the loperamide-treated and control groups in eleven families (Table 1). Of these, approximately half were from the Firmicutes phylum; Aerococcaceae, Carnobacteriaceae, Enterococcaceae, Streptococcaeae, Defluviitaleaceae and Lachnospiraceae, which were the most abundant of the significantly different families (loperamide $25.746\%$ ± 1.94; control $31.69\%$ ± 2.141).
**Table 1**
| Phylum | Family | Control | Loperamide | q-value |
| --- | --- | --- | --- | --- |
| Bacteroidetes | Marinifilaceae | 0.127 ± 0.022 | 0.337 ± 0.041 | 0.0009 |
| Proteobacteria | Unclassified Rhodospirillales | 0.008 ± 0.003 | 0.042 ± 0.012 | 0.03 |
| Proteobacteria | Pseudomonadaceae | 0.013 ± 0.002 | 0.008 ± 0.002 | 0.01 |
| Actinobacteria | Corynebacteriaceae | 0.010 ± 0.003 | 0.003 ± 0.001 | 0.03 |
| Cyanobacteria | Unclassified Gastranaerophilales | 0.187 ± 0.040 | 0.111 ± 0.056 | 0.02 |
| Firmicutes | Aerococcaceae | 0.025 ± 0.005 | 0.010 ± 0.003 | 0.005 |
| Firmicutes | Carnobacteriaceae | 0.187 ± 0.018 | 0.125 ± 0.017 | 0.001 |
| Firmicutes | Enterococcaceae | 0.066 ± 0.011 | 0.036 ± 0.005 | 0.005 |
| Firmicutes | Streptococcaceae | 0.329 ± 0.043 | 0.217 ± 0.030 | 0.01 |
| Firmicutes | Defluviitaleaceae | 0.043 ± 0.007 | 0.022 ± 0.004 | 0.01 |
| Firmicutes | Lachnospiraceae | 31.69 ± 2.141 | 25.746 ± 1.94 | 0.02 |
Extensive differences at the genus level were also observed with 29 genera significantly different between treatments (Table 2). Of the most abundant taxa, Roseburia (loperamide $1.527\%$ ± 0.695; control $4.489\%$ ± 0.844) and Unclassified Lachnospiraceae (loperamide $2.655\%$ ± 0.649; control $7.575\%$ ± 1.168) were significantly lower in loperamide-treated rats. Genera that were significantly more abundant in the loperamide-treated group and that had a mean relative abundance greater than $1\%$ included Bacteroides, Phascolarctobacterium, Ruminococcaceae UCG-005, Lactobacillus, Blautia, Christensenellaceae R-7 group and the Ruminococcaceae NK4A214 group.
**Table 2**
| Family | Genus | Control | Loperamide | q-value |
| --- | --- | --- | --- | --- |
| Bifidobacteriaceae | Bifidobacterium | 0.007 ± 0.002 | 0.041 ± 0.017 | 0.036 |
| Bacteroidaceae | Bacteroides | 4.964 ± 0.709 | 7.731 ± 1.113 | 0.013 |
| Ruminococcaceae | Candidatus Soleaferrea | 0.029 ± 0.006 | 0.037 ± 0.005 | 0.014 |
| Barnesiellaceae | Barnesiella | 0.027 ± 0.010 | 0.052 ± 0.012 | 0.013 |
| Ruminococcaceae | Ruminococcaceae NK4A214 group | 0.912 ± 0.108 | 1.427 ± 0.163 | <0.001 |
| Ruminococcaceae | Ruminococcaceae UCG-005 | 3.789 ± 0.751 | 7.616 ± 1.177 | 0.003 |
| Ruminococcaceae | Ruminococcaceae UCG-010 | 0.221 ± 0.036 | 0.072 ± 0.016 | 0.003 |
| Marinifilaceae | Butyricimonas | 0.096 ± 0.016 | 0.235 ± 0.033 | <0.001 |
| Marinifilaceae | Odoribacter | 0.031 ± 0.007 | 0.102 ± 0.015 | <0.001 |
| Erysipelotrichaceae | Erysipelotrichaceae UCG-003 | 0.220 ± 0.072 | 0.623 ± 0.135 | 0.013 |
| Erysipelotrichaceae | Faecalibaculum | 0.068 ± 0.045 | 0.155 ± 0.040 | 0.023 |
| Erysipelotrichaceae | Unclassified Erysipelotrichaceae | 0.536 ± 0.159 | 0.054 ± 0.041 | 0.007 |
| Acidaminococcaceae | Phascolarctobacterium | 4.701 ± 1.213 | 8.507 ± 1.172 | 0.025 |
| Unclassified Rhodospirillales | Unclassified Rhodospirillales | 0.008 ± 0.003 | 0.042 ± 0.012 | 0.003 |
| Burkholderiaceae | Parasutterella | 0.327 ± 0.074 | 0.535 ± 0.069 | 0.003 |
| Enterobacteriaceae | Kluyvera | 0.001 ± 0.0003 | 0.002 ± 0.001 | 0.015 |
| Carnobacteriaceae | Granulicatella | 0.002 ± 0.001 | 0 | 0 |
| Lactobacillaceae | Lactobacillus | 3.469 ± 0.642 | 7.088 ± 0.809 | 0.001 |
| Christensenellaceae | Christensenellaceae R-7 group | 0.940 ± 0.096 | 1.688 ± 0.259 | 0.003 |
| Atopobiaceae | Unclassified Atopobiaceae | 0.004 ± 0.001 | 0.012 ± 0.003 | 0.014 |
| Family XIII | Anaerovorax | 0.047 ± 0.010 | 0.056 ± 0.004 | 0.021 |
| Lachnospiraceae | ASF356 | 0.180 ± 0.1 | 0 | 0 |
| Lachnospiraceae | Blautia | 3.251 ± 1.132 | 5.246 ± 0.874 | 0.025 |
| Eggerthellaceae | Enterorhabdus | 0.001 ± 0.001 | 0 | 0 |
| Lachnospiraceae | GCA-900066575 | 0.259 ± 0.049 | 0.083 ± 0.028 | 0.042 |
| Eggerthellaceae | Gordonibacter | 0.009 ± 0.001 | 0.013 ± 0.002 | 0.013 |
| Lachnospiraceae | Marvinbryantia | 0.243 ± 0.069 | 0.912 ± 0.213 | <0.001 |
| Lachnospiraceae | Roseburia | 4.489 ± 0.844 | 1.527 ± 0.695 | 0.013 |
| Lachnospiraceae | Unclassified Lachnospiraceae | 7.575 ± 1.168 | 2.655 ± 0.649 | 0.014 |
## Microbiota diversity
Principal coordinate analysis of Unifrac phylogenetic distances showed strong separation overall between caecal microbiotas from control and loperamide-treated rats (Figure 2). Permutation multivariate analysis of variance (PERMANOVA) confirmed that the overall differences in communities were significant ($$P \leq 0.008$$). The microbiotas of control rats were also significantly more diverse with more observed operational taxonomic units (OTUs) ($$P \leq 0.017$$), and a higher Faith’s phylogenetic distance ($$P \leq 0.06$$) compared to loperamide-treated rats (Figure 3).
**Figure 2:** *Principal coordinate analysis (PCoA) plot of weighted unifrac phylogenetic distances of caecal microbiotas from control (yellow) or loperamide (red) groups. Plots show (A) PC1 vs PC2, (B) PC1 vs PC3, and (C) PC2 vs PC3. Percentages on axes indicate proportion of variation explained by each dimension. Communities between groups were significantly different (PERMANOVA P=0.008).* **Figure 3:** *Rarefaction curves of (A) observed OTUs and (B) Faith’s phylogenetic distance (PD) between caecal microbiotas from control (yellow) or loperamide-treated (red) groups. Error bars show SEM of 10 iterations per sampling depth. Observed OTUs P=0.017, Faith’s PD P=0.06.*
## Discussion
It is becoming increasingly important to understand the association between GI transit time and the gut microbiota, due to the potential impact of the gut microbiota on host physiology and the transition between healthy and diseased states. We previously reported that the pharmacological drug loperamide delayed GI transit in rats compared to un-treated (Dalziel et al., 2017). Delayed GI transit time, as seen in people with constipation, affects the microbiota composition by decreasing beneficial bacteria and increasing harmful bacteria (Zhao and Yu, 2016). Interestingly, Bacteroides, the predominant genus in the human gut and a beneficial symbiont/commensal (Wexler, 2007), in this study was found to be more relatively abundant in loperamide-treated rat caecal samples compared to controls. This finding is consistent with a previous study in which Bacteroides were shown to be significantly increased in constipated women compared to controls (Li et al., 2021). The loperamide-induced prolonged transit time (Dalziel et al., 2017) might have facilitated the increase in relative abundance of Bacteroides, indicating that they can adapt well in a slow and competitive environment (Based on r/K selection theory of microbial ecology) (Pianka, 1970). Increased relative abundance of Bacteroides may be associated with alteration of gut microbiota homeostasis. Given their large genome bank, Bacteroides have the ability to turn on certain genes to shift from friendly commensal to harmful bacteria (Wexler, 2007).
In contrast, loperamide-induced slowed transit caused a decrease in the relative abundance of the phylum Actinobacteria and selected genus in the phylum Firmicutes (Roseburia) that are associated with faster colonic transit (Parthasarathy et al., 2016). These findings suggest that taxa of these phyla do not adapt well in a slowed transit luminal environment. Overall, these findings suggest that normal gut motility is key in maintaining a balanced gut ecosystem and gut homeostasis.
In this study, we investigated if changes in microbiota is associated with changes in gut transit time (using data from a previous study where loperamide was effective at inhibiting GI transit compared to controls) (Dalziel et al., 2017). We found that the ecological diversity and richness in the caecal microbiota differed significantly between loperamide-treated rats and controls. Alpha-diversity analysis showed that the richness and diversity of the bacterial communities was significantly lower in the loperamide-induced slowed transit group compared to the control group. This is in line with a study by Ren et al., who showed that the control group exhibited higher bacterial diversity and richness than the constipation group, concluding that higher microbial diversity may correspond to healthier ecosystems (Ren et al., 2017). In contrast, several studies have reported the diversity and richness of bacterial communities to be higher in the constipation group compared to controls (Li et al., 2020; Müller et al., 2020). Furthermore, these studies went on to show that increased alpha diversity was significantly associated with longer colonic passage, the explanation being diversification as an adaption to a perturbed ecosystem (i.e., depletion of nutrients, switch from microbial saccharolytic to proteolytic fermentation, microbial competition and decreased water availability) (Müller et al., 2020). These contrasting findings indicate that microbial diversity should be interpreted within the physiological context and reduced microbial alpha diversity should not necessarily be represented as reduced microbiota stability.
Numerous studies have documented the role of gut microbiota-derived molecules in regulating gut motility (Dalile et al., 2019; Sun et al., 2019). Production of short chain fatty acids (SCFA), especially butyrate, by the gut microbiome was shown to influence GI motility (Cherbut et al., 1997). In our study, the butyrate producing genus Roseburia was found to be significantly reduced in loperamide-induced constipated rats. This is similar to a study by Chassard et al. in which butyrate-producing Roseburia - E. rectale group was found to be lower in the IBS-with-constipation group compared to controls. The authors concluded that a reduced relative abundance of butyrate producers makes colonic transit slower (Chassard et al., 2012). Experiments by Soret et al., and Reigstad et al., showed that butyrate producing bacteria may increase colonic motility by inducing the release of serotonin or promoting cholinergic pathways (Soret et al., 2010; Reigstad et al., 2014). Conversely, studies have reported butyrate producing genera to be associated with constipation (Yarullina et al., 2020). Butyrate has been shown to impact various colonic effects; such as inhibition of smooth muscle contractions in the colon, reduction of stool volume through stimulation of colonic electrolyte and water absorption, predisposing to constipation (Yarullina et al., 2020). These inconsistencies in the literature can be addressed by carrying out further research to identify the mechanisms and involvement of butyrate producers in prolonged colonic transit.
We speculate that a slowed gut transit might modify the spatial organization and proportion of the microbiota by creating a luminal microenvironment for the growth of specific bacterial taxa, or by affecting bacterial colonization. Moreover, the influence of luminal microenvironments might be relevant in regions where key motility patterns are initiated such as the proximal colon. In our study, a slowed GI transit time induced by loperamide led to increased relative abundance of families Bacteroidaceae and Marinifilaceae, belonging to the phylum Bacteroidetes. Several studies have proposed Bacteroidetes to be the dominant gram-negative bacteria in the GI tract (Eckburg et al., 2005). Alterations in distribution of gram-negative bacteria is associated with elevated levels of lipopolysaccharides (LPS), a cell wall component of gram-negative bacteria (Salguero et al., 2019). LPS is thought to be an important mediator of the microbiome’s influence on host physiology. Several studies have pointed out an inhibitory role of LPS on GI motility. Mikawa et al., through their experiments showed that LPS-induced nitric oxide synthase produced nitric oxide, which in turn inhibited GI motility (Mikawa et al., 2015). It would therefore follow that changes in the composition of gram-negative bacteria might further cause GI motility disturbances. We did not measure LPS levels in this study; future studies might shed more light on the possible specific association of gram-negative bacteria and LPS on GI motility.
In the present study, we used 16S rRNA gene amplicon sequencing to analyse DNA samples. Although there is valuable information gained from 16S sequencing, there are also some limitations. The sequencing depth may not be sufficient for short amplicon sequencing to capture novel or low abundance microbial species. Moreover, this method does not directly provide information about the functional capacities of the organisms. In contrast, whole genome sequencing would have revealed a strain level resolution of both microbiota abundance and functional capacity and would have given a comprehensive understanding regarding the association between varied gut transit time and dysbiosis. This could be the subject of further studies.
## Conclusion
Our findings indicate that the loperamide-induced alterations in gut transit time affected the diversity and relative abundance of caecal microbial communities. We speculate that slowed colonic transit facilitates the amplification and colonization of select genera such as Bacteroides that adapt well in slow and competitive environments (corresponding to prolonged transit and limited resources). The relationship between gut motility and microbiota is relevant in experimental models used to study several functional GI disorders associated with the gut microbial composition, such as IBS and constipation, where GI transit is also altered. Understanding the link between specific microbial species and varying transit times is crucial to design microbiota-based interventions to treat intestinal motility disorders.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA819534.
## Ethics statement
The animal study was reviewed and approved by AgResearch Grasslands Animal Ethics Committee.
## Author contributions
NP, JD, and WY contributed to conception and design of the study. WY organized the database and performed the statistical analysis. NP wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Author JD is employed by AgResearch, New Zealand. Author WY was employed by AgResearch, New Zealand.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Yiqi Huazhuo decoction increases insulin secretion in type 2 diabetic rats
by regulating the pancreatic GPR40-IP3R-1 signaling pathway
authors:
- Dongjiao Wu
- Siying Weng
- Shuyi Xu
- Yan Li
- Jianyang Zhou
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10043368
doi: 10.3389/fphar.2023.1136778
license: CC BY 4.0
---
# Yiqi Huazhuo decoction increases insulin secretion in type 2 diabetic rats by regulating the pancreatic GPR40-IP3R-1 signaling pathway
## Abstract
Objective: Yiqi Huazhuo Decoction (YD) reduces blood glucose, glycated hemoglobin, body weight, and insulin resistance in patients with type 2 diabetes mellitus (T2DM), but its exact mechanisms are unknown. This study investigated the therapeutic effects and mechanisms of YD on impaired insulin secretion in T2DM rats.
Methods: T2DM rats were randomized to the model, YD-lo (15 mg/kg/d YD, 10 weeks), YD-hi (30 mg/kg/d YD, 10 weeks), positive drug (TAK-875), and healthy control groups. The rats underwent an oral glucose tolerance test (OGTT), glucose-stimulated insulin secretion (GSIS) test, and serum lipid measurements. High-fat and high-glucose-injured RIN-m5f cells were treated with YD (30 or 150 mg/mL) for 48 h. GPR40 and IP3R-1 expression levels were determined by immunofluorescence, qRT-PCR, and western blot.
Results: Compared with the model group, the OGTT area under the curve (AUC) in the YD-hi group was decreased by $26.7\%$, the insulin release test (IRT) AUC in the YD-hi group was increased by $45.9\%$, and the GSIS AUC was increased by $33.9\%$ ($p \leq 0.05$). Compared with the model cells, the insulin secretion after glucose stimulation in the YD-hi group was increased by $24.5\%$, similar to the TAK-875 group ($23.1\%$) ($p \leq 0.05$). GPR40 and IP3R-1 mRNA in the model cells were decreased by $49.5\%$ and $51.2\%$ compared with the control cells ($p \leq 0.05$). In the YD-hi group, GPR40 and IP3R-1 mRNA levels were increased by $58.1\%$ and $39.3\%$ ($p \leq 0.05$), similar to the TAK-875 group. The changes in protein expression were similar to mRNA.
Conclusion: YD promotes insulin secretion from pancreatic islet β-cell in T2DM rats by regulating the GPR40-IP3R-1 pathway, thereby reducing blood glucose.
## Introduction
Diabetes mellitus (DM) is a chronic disease with a high incidence. According to the International Diabetes Federation (IDF) Diabetes Atlas (10th Edition), as of 2021, 537 million adults aged 20-79 ($10.5\%$) have diabetes, and China has the largest number of diabetics in the world. By the end of 2017, the number of adults with DM had reached 116 million in China, accounting for $11.2\%$ of the national population (Li et al., 2020). The potential complications of DM include cardiovascular disease, neuropathy, nephropathy, retinopathy, and increased mortality (American diabetes Association Association, 2014). As the global population is aging, an increasing proportion of people over the age of 60 will have diabetes, placing a large burden on the economy (Sharma et al., 2017; Sun et al., 2022; Wang et al., 2022).
Type 2 diabetes mellitus (T2DM) is characterized by variable degrees of insulin resistance and deficiency, resulting in hyperglycemia (American diabetes Association, 2022, 2014; Chatterjee et al., 2017). T2DM accounts for more than $90\%$ of DM cases (Tang et al., 2019). The global prevalence of T2DM is estimated at $9.0\%$ in men and $7.9\%$ in women (Collaboration, 2016). T2DM is the product of the interplay between decreased insulin secretion caused by pancreatic islet β-cell dysfunction and peripheral resistance to insulin action, leading to decreased glucose uptake. Currently, drugs used to treat type 2 diabetes include metformin, insulin secretagogues, α-glycosidase inhibitors, thiazolidinediones (TZD), dipeptidyl peptidase IV inhibitors (DPP-4i), sodium-glucose cotransporter 2 inhibitors (SGLT2i) glucagon-like peptide 1 receptor agonist (GLP-1RA), and insulin (Sharma et al., 2018a; American diabetes Association, 2022). Both sulfonylureas and non-sulfonylureas antidiabetic drugs are insulin secretagogues that directly act on pancreatic islet β-cell, while G-protein-coupled receptor 40 (GPR40) agonists have different targets on β-cell as new insulin secretagogues.
The pancreatic islet β-cell transmembrane protein GPR40 (also named the free fatty acid receptor 1 (FFAR1)) belongs to the G protein-coupled receptor family and is a natural receptor for medium- and long-chain fatty acids. GPR40 is mainly distributed in the cell membrane of islet β-cells in the human pancreas and is a transmembrane protein that is characteristically highly expressed in β-cells (Ichimura et al., 2009). GPR40 can trigger the extracellular secretion of insulin after releasing Ca2+ from the endoplasmic reticulum (ER) through the phospholipase C (PLC) pathway under the stimulation of free fatty acids (FFAs) (Shapiro et al., 2005; Feng et al., 2012; Usui et al., 2019). Nevertheless, chronically elevated FFAs can decrease GPR40 expression, insulin biosynthesis, and glucose-stimulated insulin secretion and cause β-cell dysfunction (Chueire and Muscelli, 2021). In addition, 20-hydroxy-eicosatetraenoic acid (20-HETE) can activate GPR40 and form a positive feedback loop to enhance glucose-stimulated insulin secretion (Tunaru et al., 2018). GPR40 can affect palmitate-stimulated insulin secretion by enhancing mitochondrial respiration (Kristinsson et al., 2015). GPR40 is involved in the FFA-mediated glucose-dependent insulin secretion (GDIS) effect of β cells but not in the chronic toxicity of FFAs. It is an important non-traditional target for promoting insulin secretion (Yashiro et al., 2012). The activation of GPR4 regulates glucose-stimulated insulin secretion in the high-fat obesity state (Schnell et al., 2007). Therefore, modulating the activity of GPR40 might be an attractive way to increase insulin secretion (Feng et al., 2012).
For centuries, herbal drugs have been widely used as complementary and alternative medicine (CAM) to treat chronic diseases such as diabetes. In recent decades, more and more diabetes patients ($72.8\%$) have used herbs, dietary supplements, and other CAM therapies (Chang et al., 2007). Currently, many medicinal plants have been used to treat DM and its related conditions (Qi et al., 2010; Mao et al., 2021; Mao et al., 2022), of which herbs such as Yunpi Heluo decoction and dietary supplements can be used to treat DM.
Yiqi Huazhuo Decoction (YD) is a traditional Chinese medicine (TCM) preparation consisting of four herbs: Radix Astragali (*Astragalus mongholicus* Bunge (Fabaceae)), Atractylodes Rhizoma (*Atractylodes lancea* DC), Salviae Miltiorrhizae Radix et Rhizoma (*Salvia miltiorrhiza* Bunge), and Radix Puerariae Lobatae (*Pueraria montana* var. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep). The plant names have been checked with https://wfoplantlist.org/plant-list. YD has been used for a long time in treating T2DM with mildly impaired pancreatic islet function (Gong et al., 2013). In a previous multicenter randomized controlled clinical trial on the use of YD for the treatment of T2DM, it was found that YD could reduce blood glucose, glycated hemoglobin (HbA1c), body weight, and insulin resistance in overweight patients with T2DM (Chen et al., 2013). It was also observed in vitro that YD could improve insulin secretion by decreasing the expression of miR-124-3p and decreasing the inhibition of the PLC-B1/IP3R-1 pathway in INS-1 cells (Weng et al., 2019). Nevertheless, how YD modulates insulin secretion by damaged pancreatic islet β-cells and whether GPR40 is involved remain unknown.
Therefore, this study investigated the regulatory mechanism of YD on insulin secretion of pancreatic islet β-cell in T2DM rat models. The results could provide more insights into the mechanisms of YD and provide a scientific rationale for its clinical use. In addition, it could provide additional data on the mechanisms of insulin secretion in T2DM.
## Preparation of YD
Radix Astragali (lot no. 20108) from Gansu Province of China, Atractylodes Rhizoma (lot no. 20115) from Liaoning Province of China, Radix Puerariae Lobatae (lot no. 20117) from Jiangxi Province of China, and Salviae Miltiorrhizae Radix et Rhizoma (lot no. 20201) from Zhejiang Province were purchased from Zhejiang University of Traditional Chinese Pharmaceuticals Co., Ltd. (http://www.zjzyyp.com/Default.aspx); all were inspected based on the Pharmacopoeia 2015 edition and the enterprise standard TS-ZL-03. YD comprises Radix Astragali 30 g, Atractylodes Rhizoma 30 g, Radix Puerariae Lobatae 30 g, and Salviae Miltiorrhizae Radix et Rhizoma 20 g. After the medicines were crushed, 550 g of water was added for decoction for 1 h. The filtrate was adjusted to pH 7.0 using sodium citrate and sodium carbonate. The obtained YD had a crude drug content of 3 g/mL, determined by a colorimetric method. The YD liquid was added to pure water, placed in a freeze-drying box, pre-frozen at −50°C for 4 h in a medicinal vacuum freeze dryer (TA, lyo_0.5), dried, sublimated at −20°C for 12 h, and dried at 32°C for 8 h. The YD freeze-dried powder was obtained, which was refrigerated at 4°C for later use. Before use, the powder was dissolved in the corresponding medium to the required concentration, centrifuged at 4000 ×g for 30 min to remove the precipitate, and the supernatant was used.
## Chemical analysis of YD by UHPLC-Q-Orbitrap HRMS
The chemical analysis of YD was carried out by ultra-high performance liquid chromatography-Q precision hybrid quadrupole orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS, Thermo Fisher Scientific Inc., Grand Island, NY, USA). UHPLC (Thermo Scientific Dionex Ultimate 3000) was controlled with the Chromeleon 7.2 software. The autosampler was set to 10°C and protected from light. The column was set to 40°C. A Waters ACQUITY UPLC BEH C18 Column (2.1 × 100 mm, 1.7 μm) was used. The mobile phase consisted of A ($0.1\%$ formic acid) and B (methanol) at a flow rate of 0.3 mL/min, gradient elution: 0–4 min ($4\%$ B), 4–10 min ($4\%$–$12\%$ B), 10–30 min ($12\%$–$70\%$ B), 30–35 min ($70\%$ B), 35–38 min ($70\%$–$95\%$ B), 38–42 min ($95\%$ B), 42–45 min ($95\%$ B) −$4\%$ B). The injection volume was 2 μL. The mass spectrometer Q-Orbitrap system was connected to the UHPLC system via heated electrospray ionization and controlled by the Xcalibur 4.1 software for data acquisition and analysis. The electrospray ionization source was run and optimized under positive and negative ionization models. The mass spectrometry parameters were capillary temperature: 320°C; sheath gas (N2) flow rate: 35 arbitrary units; auxiliary gas (N2) flow rate: 13 arbitrary units; purge gas flow rate: 0 arbitrary units; spray voltage: 2.8 kV (negative) and 3.2 kV (positive); S lens RF level: 50 V; auxiliary gas heater temperature, 300°C; scan mode: full MS; scan range: 80–1,200 m/z. The maximum injection time (IT) was 200 ms. The scan resolution was 70,000 FWHM (m/z/s). The automatic gain control (AGC) target was 1.0e6. The typical chromatographic fingerprint of the YD extract is shown in Figure 1.
**FIGURE 1:** *Total ion chromatograms of YD UHPLC-Q-Orbitrap HRMS in negative and positive ion modes. Salvianolic acid A (1), protocatechualdehyde (2), chlorogenic acid (3), puerarin (4), calycosin-7-O-β-D-glucopyranoside (5), salvianolic acid B (6), atractylodes Lactone I (7), astragaloside I (8), and astragaloside IV (9).*
## Animal models
Thirty-two specific pathogen-free (SPF) 8-week-old male ZDF (fa/fa) rats, weighing 200–220 g, and eight SPF 8-week-old male ZL (fa/+) rats, weighing 200–220 g, were purchased from Beijing Weitong Lihua Laboratory Animal Technology Co., Ltd. (Beijing Viton Lever Laboratory Animal Company, Beijing, China). The animals were housed in the SPF experimental rearing room at the Animal Experiment Center of Zhejiang University of Traditional Chinese Medicine. All animal studies were approved by the Institutional Animal Care Committee of Zhejiang University of Traditional Chinese Medicine (#2017A610167). All experiments were performed with humane care according to the 3R principles (Hubrecht and Carter, 2019) and in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC) (https://www.aaalac.org/) and the Institutional Animal Care and Use Committee (IACUC) guidelines.
All rats were acclimatized for 1 week. The ZDF rats were fed a high-glucose and high-fat diet ($4.0\%$ crude fiber, $7.55\%$ total fat, $23.75\%$ protein, and digestible energy of 14.6 kJ/kg) for 4 weeks, while the control ZL rats were fed a normal diet for 4 weeks. A fasting blood glucose ≥11.1 mmol/L was used as the success criterion for establishing T2DM rats. After successful modeling, the ZDF rats were randomly divided into four groups, with eight rats in each group. Referring to a previous study (Gong et al., 2013), the doses of YD in the high-concentration (YD-hi) and low-concentration (YD-lo) groups were 30 and 15 g/kg, respectively. The positive control group was given 10 mg/kg TAK-875 (No. A8339, APE x BIO, Houston, USA) according to previous studies (Negoro et al., 2010; Leifke et al., 2012). The model group was given the same amount of distilled water once a day for 8 weeks. The study drugs (including the distilled water) were given by gavage. The ZL rats were fed with normal chow for 8 weeks as a control group.
Before the end of the experiment, the rats in each group were fasted for 12 h, euthanized with pentobarbital, and blood was collected by cardiac puncture. The abdominal cavity was quickly cut open, and the pancreas was taken out. Part of the pancreatic tissue was taken and fixed in a $10\%$ formalin solution to prepare sections.
## Body weight, blood glucose, oral glucose tolerance test (OGTT), insulin release test (IRT), glucose-stimulated insulin secretion (GSIS) test, and serum lipid test
After successful modeling, each rat was weighed weekly, blood samples were collected from the tail vein once a week, and the fasting blood glucose level was measured using glucose test strips.
The OGTT test and IRT were conducted every 2 weeks. For the OGTT, the rats were fasted for 12 h, and the blood was collected from the tail vein. The glucose oxidase method (Toecho Super 2, Kagawa, Japan) was used to test the plasma fasting blood glucose levels. At the same time, 0.2 mL of blood was collected, the serum was collected after centrifugation, and the fasting insulin levels were determined by ELISA. Each group was given the corresponding drugs or distilled water for 30 min to measure the blood glucose and insulin levels, then the rats were given $20\%$ glucose by gavage at 10 mL/kg body weight, and the glucose dose was 2 g/kg. Blood glucose and insulin were measured at 30, 60, 120, and 180 min after glucose administration. The area under the curve (AUC) of blood glucose and insulin was observed at each time point after glucose administration in each group. The formula was AUC = (0 min + 30 min × 0.25 + (30 min + 60 min) × 0.25 + (60 min + 90 min) × 0.5 + (90 min + 120 min) × 0.5.
For the GSIS, the rats were fasted for 12 h and anesthetized by an intraperitoneal injection of $3\%$ pentobarbital (45 mg/kg) (Sharma K. et al., 2018; Sharma et al., 2018c). The rats were immobilized in the supine position, the right jugular vein was cannulated, and $50\%$ glucose 0.5 g/kg was infused. At 0, 3, 5, and 10 min, 0.5 mL of blood was collected. After centrifugation at 5,000 r/min for 15 min, the supernatant was collected, and insulin was measured by ELISA. During the experiment, normal saline was injected to maintain the blood volume of the rats.
The rats were sacrificed after fasting for 12 h. Blood was collected from the heart, and serum total cholesterol (CHOL) and triglyceride (TG) levels were detected using a U2700 automatic biochemical analyzer (Hitachi, Tokyo, Japan) using the manufacturer’s reagents and instructions.
## Histopathological examination of the pancreas and pathological scores
The rat pancreatic tissues were fixed in $10\%$ formalin for 5 days. The specimens were dehydrated with ethanol, treated with xylene for 30 min, and placed in paraffin for 5 h. The paraffin blocks were sectioned at 4 μm thickness. The paraffin sections were dewaxed with xylene, rehydrated with ethanol at $100\%$, $95\%$, $85\%$, and $70\%$, stained with hematoxylin, treated with $0.2\%$ ammonia, and stained with $0.5\%$ eosin. After ethanol dehydration and xylene transparency, the sections were sealed with neutral gum. The histopathological changes in the pancreas were observed under light microscopy. The nuclei were blue, the cytoplasm was pink, and the erythrocytes were orange-red. The Schmidt pathology scale was used to perform the pathological score of pancreas (Schmidt et al., 1992). The score was based on the degree of tissue edema, necrotic area, bleeding and inflammatory cell infiltration.
## Immunofluorescence for GPR40
The paraffin sections were dewaxed and rehydrated with xylene I for 20 min, xylene II for 20 min, $100\%$ ethanol I for 5 min, $100\%$ ethanol II for 5 min, $95\%$ ethanol for 5 min, $80\%$ ethanol for 5 min, and PBS three times, 3 min/time. The sections were placed in 0.01 M citrate buffer (pH 6.0) for antigen retrieval in a microwave, cooled to room temperature naturally, and washed with PBS three times, 3 min/time. The primary anti-rabbit polyclonal GPR40 antibody (1:50; C02655F; SAB Biotherapeutics, Sioux Falls, SD, USA) was incubated overnight at 4°C in a refrigerator, transferred to room temperature to equilibrate for 30 min, and washed with PBS three times, 5 min/time. Fluorescent secondary antibody donkey anti-rabbit antibody (1:300; Alexa Fluor® 488 donkey anti-Rabbit IgG secondary antibody; Life Technologies Co., Grand Island, NY, USA) was incubated at 37°C for 60 min, rinsed with PBS three times, 5 min/time. The nuclei were stained with DAPI (Abcam, Cambridge, United Kingdom) at room temperature for 10 min. After sealing with glycerol PBS sealer (IH0271; Shanghai Ruji Biotechnology Company, Shanghai, China), the sections were observed using a laser confocal microscope (Model 800, Carl Zeiss GmbH, Oberkochen, Germany).
## Cell culture
RIN-m5f cells were purchased from the National Collection of Typical Cell Cultures (Shanghai, China). The RINm5f cell line was inoculated in a 5.6 mmol/L glucose low-glucose DMEM medium (11885084; GIBCO, Invitrogen Inc., Carlsbad, CA, USA), $10\%$ FBS (GIBCO, Invitrogen Inc., Carlsbad, CA, USA) with $1\%$ penicillin/streptomycin and incubated in $5\%$ CO2 at 37°C with saturated humidity. The modeling was performed as previously described (Ji et al., 2017). The model group was added with 30 mmol/L glucose, and sodium oleate/sodium palmitate (Xi’an Kunchuang Technology Co., Ltd., Xi’an, China, SYSJKJ001) was added to the final concentration of 500 μM for 48 h. The control group was cultured in a low-glucose medium. The RINm5f cells were divided into five groups: YD low-dose group (YD-lo), YD high-dose group (YD-hi), positive control group (GPR40 agonist TAK-875 50 nmol/L), model group (equal amount of culture medium), and untreated RINm5f cells as control.
## Cell counting kit-8 (CCK-8)
The effect of different concentrations of YD on cell proliferation was determined by the CCK8 assay. YD freeze-dried powder was added in a gradient of crude drug content of $0.01\%$, $0.1\%$, $1\%$, $5\%$, $10\%$, and $100\%$. The optimum concentration and time (24, 48, and 72 h) were screened for the subsequent experiments. The RIN-m5f cells in the logarithmic growth phase were adjusted to 2.5 × 107/L and inoculated in a 96-well plate. After the cells adhered, the serum-free medium was replaced, and the culture was continued for 12 h. The cells were treated as above for 48 h. Then, 10 μL of CCK8 detection solution (UNOCI, China) was added to each well and incubated at 37°C for 2 h in the dark. The optical density was measured at 450 nm with a microplate reader. Cell viability (%) = OD value of experimental group/OD value of non-drug group×$100\%$. The experiment was repeated three times independently.
## ELISA
The cell supernatant after drug intervention was discarded, and the cells were incubated in a 5.6 mmol/L glucose medium for 1 h. Then, the supernatant was taken, and the basal insulin secretion (BIS) levels were measured by ELISA using an insulin ELISA kit (Thermo Fisher, USA). The cells were incubated in a 30 mmol/L glucose medium for 1 h, and the supernatant was taken. GSIS levels were determined by ELISA. The total protein of each well was extracted and expressed as unit mass insulin (insulin content per well/corresponding protein content).
## qPCR
Total cell RNA was extracted using a TRIZOL RNA extraction kit (Invitrogen Inc., Carlsbad, CA, USA), followed by RT-PCR amplification after reverse transcription. The primers were designed with Primer 5 software and synthesized by Beijing Liuhe Huada Gene Technology Co., Ltd. (Beijing, China). The sequences are shown in Table 1. The PCR conditions were 1) pre-denaturation at 95°C for 1 min, and 2) denaturation at 95°C for 15 s, annealing at 63°C for 25 s, and extension at 72°C for 25 s, for 40 cycles. Statistical analysis was performed based on the ΔCt value, and the average relative expression (2−ΔΔCt) represented the relative amount between groups.
**TABLE 1**
| Gene name | Forward primer (5′-3′) | Reverse primer (5′-3′) |
| --- | --- | --- |
| GPR40 | GGGCATCAACATACCCGTGAA | GCCCTGAGCTTCCGTTTGT-3′ |
| IP3R-1 | CGTTTTGAGTTTGAAGGCGTTT | CATCTTGCGCCAATTCCCG-3′ |
| GAPDH | AGGGCTGCCTTCTCTTGTGAC | TGGGTAGAATCATACTGGAACATGTAG |
## Western blot
Pancreatic tissue and RIN-m5f cells were lysed in NP-40 lysis buffer (FNN0021, Invitrogen Inc., Carlsbad, CA, USA) and centrifuged for 20 min (12,000 ×g, 4°C). The protein concentration was measured using a BCA kit (Sigma, St. Louis, MO, USA). The proteins were separated by SDS-PAGE and transferred to PVDF membranes (Millipore Corp., Billerica, MA, USA). The membrane was blocked with $5\%$ nonfat milk powder for 1 h at room temperature. Primary anti-rabbit monoclonal anti-GPR40 (32 kDa, 1:1000, SAB Biotherapeutics, Sioux Falls, SD, USA), anti-IP3R (314 kDa, 1:1000, Abcam, Cambridge, United Kingdom) and anti-GAPDH (36 kDa; 1:2000, Abcam, Cambridge, United Kingdom.) were incubated overnight at 4°C. The membranes were incubated with HRP-conjugated secondary antibody goat anti-rabbit (1:5000; Alexa Fluo, A21206:1723019; Thermo Fisher Scientific, Waltham, MA, USA) for 1 h at room temperature. The bands were visualized using enhanced chemiluminescence in the VersaDoc 4000 MP system (Bio-Rad, Hercules, CA, USA). Optical density values were analyzed using the Image-Pro Plus 6.0 software (National Institutes of Health, https://imagej.nih.gov/ij/) to determine the relative expression levels of GPR40 and IP3R proteins (Sharma et al., 2020).
## Statistical analysis
All data were shown as mean ± standard deviation. One-way ANOVA was used for comparison among multiple groups, with the least significant difference (LSD) post hoc test (consistent variance) or Dunnett’s-T3 post hoc test (uneven variance). $p \leq 0.05$ was considered statistically significant. SPSS 23.0 (IBM, Armonk, NY, USA) was used for statistical analysis. All graphs were drawn using Prism 6.02 (GraphPad Software Inc., San Diego, CA, USA) and Illustrator CS6 (Adobe Systems, San Jose, CA, USA).
## Principal component analysis of YD
Nine compounds were analyzed to obtain the characteristic spectra: salvianic acid A (characteristic component of Salviae Miltiorrhizae Radix Et Rhizoma), protocatechualdehyde (characteristic component of Salviae Miltiorrhizae Radix Et Rhizoma), chlorogenic acid (characteristic component of Atractylodis Rhizoma), puerarin (characteristic component of Puerariae Lobatae Radix), calycosin-7-O-β-D-glucopyranoside (characteristic component of Astragali Radix), salvianolic acid B (characteristic component of Salviae Miltiorrhizae Radix Et Rhizoma), atractylenolide I (characteristic component of Atractylodis Rhizoma), astragaloside IV (characteristic component of Astragali Radix) and astragaloside II (characteristic component of Astragali Radix) were used for quantitative and qualitative analysis. The contents of salvianolic acid A, protocatechuic aldehyde, chlorogenic acid, puerarin, verbascoside-7-O-β-D-glucopyranoside, salvianolic acid B, atractylodes I, astragaloside IV, astragaloside IV in YD were 0.32 mg/g, 0.35 mg/g, 0.10 mg/g, 0.34 mg/g, 0.14 mg/g, 0.35 mg/g, 0.11 mg/g, 0.10 mg/g and 0.11 mg/g (Table 2).
**TABLE 2**
| No. | Rt (min) | Molecular formula | Negative | Negative.1 | Negative.2 | Negative.3 | Positive | Positive.1 | Positive.2 | Positive.3 | Compounds |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| No. | Rt (min) | Molecular formula | Ion model | Measured mass (Da) | Calculated mass (Da) | Error (ppm) | Ion model | Measured mass (Da) | Calculated mass (Da) | Error (ppm) | Compounds |
| 1 | 4.27 | C9H10O5 | [M-H]- | 197.04456 | 197.04445 | 0.559 | [M+H]+ | 199.06003 | 199.06010 | −0.351 | Salvianic acid A |
| 2 | 6.43 | C7H6O3 | [M-H]- | 137.02296 | 137.02332 | −2.631 | — | — | — | — | Protocatechualdehyde |
| 3 | 9.72 | C16H18O9 | [M-H]- | 353.08783 | 353.0867 | 3.176 | [M+H]+ | 355.10205 | 355.10236 | −0.869 | Chlorogenic acid |
| 4 | 13.06 | C21H20O9 | [M-H]- | 415.10339 | 415.10236 | 2.485 | [M+H]+ | 417.11743 | 417.11801 | −1.387 | Puerarin |
| 5 | 17.16 | C22H22O10 | [M-H]- | 445.11377 | 445.11292 | 1.902 | [M+H]+ | 447.12814 | 447.12857 | −0.969 | Calycosin-7-O-β-D-glucopyranoside |
| 6 | 22.31 | C36H30O16 | [M-H]- | 717.1463 | 717.1463 | 1.797 | [M+Na]+ | 741.14209 | 741.14260 | −0.696 | Salvianolic acid B |
| 7 | 34.09 | C15H18O2 | — | — | — | — | [M+H]+ | 231.13779 | 231.13795 | −0.720 | Atractylenolide I |
| 8 | 40.30 | C41H68O14 | [M+HCOO]- | 829.45966 | 829.45801 | 1.987 | [M+Na]+ | 807.44965 | 807.45013 | −0.592 | Astragaloside IV |
| 9 | 40.55 | C43H70O15 | [M+HCOO]- | 871.46997 | 871.46857 | 1.599 | [M+Na]+ | 849.45990 | 849.46069 | −0.933 | Astragaloside II |
## YD decreases the glucose and lipid levels and increases GPR40 in T2DM rats
After 8 weeks of treatment, the body weight of the rats in the model group increased from $141.3\%$ of the control group to $148.8\%$, and the weight of the YD-hi group was $136.8\%$ of the control group. The body weight of the YD-hi group was significantly lower compared with the model group ($p \leq 0.05$). The YD-lo and TAK-875 groups (positive control) were similar to the model group ($p \leq 0.05$). Hence, YD appeared to prevent weight gain in the YD-hi group (Figure 2A; Table 3). Moreover, the FBG of the rats in the model group increased from $287.3\%$ of the control group to $332.8\%$ after 8 weeks of treatment, while the YD-hi and TAK-875 groups were $196.6\%$ and $191.4\%$ of the control group, respectively. The FBG of the YD-hi and TAK-875 groups were reduced compared with the model group ($p \leq 0.05$). The YD-lo group was similar to the model group. These results indicated that YD-hi and TAK-875 could reduce FBG in rats (Figure 2B; Table 4).
**FIGURE 2:** *(A) Body weight of the rats after modeling. (B) Fasting blood glucose after modeling. (C) *The area* under the curve (AUC) of the glucose oral tolerance test (OGTT) after modeling. (D) The AUC of the insulin release test (IRT) after modeling. (E) Insulin levels of glucose-stimulated insulin secretion (GSIS) after modeling. (F) AUC of GSIS. (G) Triglycerides (TG) and total cholesterol (CHOL) levels. $$n = 8$$/group). ∗$p \leq 0.05$ vs. control group; #
$p \leq 0.05$ vs. model group; Δ
$p \leq 0.05$ vs. the TAK-875 group (positive control group).* TABLE_PLACEHOLDER:TABLE 3 TABLE_PLACEHOLDER:TABLE 4 After 8 weeks of treatment, it was observed that the blood glucose levels (AUC value of OGTT) of the YD-lo, YD-hi, and TAK-875 groups were all decreased compared with the model group after 8 weeks of treatment ($p \leq 0.05$), while the blood glucose levels in the YD-hi and TAK-875 groups were decreased more than in YD-lo group, and these two groups were similar ($p \leq 0.05$) (Figure 2C; Table 5).
**TABLE 5**
| Unnamed: 0 | 0W | 2W | 4W | 6W | 8W |
| --- | --- | --- | --- | --- | --- |
| control | 12.54 ± 1.06 | 12.35 ± 1.34 | 12.61 ± 2.07 | 12.62 ± 1.31 | 12.71 ± 1.24 |
| model | 37.15 ± 3.41∗ | 38.06 ± 3.52∗ | 37.94 ± 3.67∗ | 37.79 ± 4.05∗ | 37.58 ± 4.33∗ |
| YD-lo | 37.05 ± 3.62 | 35.14 ± 3.91 | 33.84 ± 3.54# | 32.77 ± 4.19#Δ | 32.17 ± 3.56#Δ |
| YD-hi | 36.85 ± 3.54 | 35.12 ± 3.81 | 30.07 ± 2.85# | 28.64 ± 2.61# | 27.51 ± 2.49# |
| TAK-875 | 37.29 ± 1.67 | 34.10 ± 1.45 | 31.12 ± 2.57# | 26.87 ± 1.95# | 26.34 ± 1.82# |
The levels of insulin release (AUC value of IRT) in the YD-lo, YD-hi, and TAK-875 groups were increased compared with the model group after 8 weeks of treatment ($p \leq 0.05$), while the insulin release levels in the YD-hi and TAK-875 groups were higher than in YD-lo group, and these two groups were similar ($p \leq 0.05$) (Figure 2D; Table 6).
**TABLE 6**
| Unnamed: 0 | 0W | 2W | 4W | 6W | 8W |
| --- | --- | --- | --- | --- | --- |
| control | 44.65 ± 4.03 | 43.43 ± 3.75 | 46.13 ± 3.95 | 41.44 ± 4.62 | 43.73 ± 3.86 |
| model | 24.93 ± 2.75∗ | 25.47 ± 2.67∗ | 26.13 ± 2.95∗ | 25.45 ± 3.21∗ | 25.74 ± 2.86∗ |
| YD-lo | 25.65 ± 2.66 | 27.12 ± 2.81 | 28.84 ± 3.44 | 31.03 ± 2.77#Δ | 30.61 ± 2.95#Δ |
| YD-hi | 25.89 ± 3.21 | 28.14 ± 3.30 | 33.05 ± 3.23# | 35.64 ± 2.61# | 37.57 ± 2.14# |
| TAK-875 | 25.17 ± 2.64 | 31.12 ± 3.54 | 33.19 ± 3.41# | 36.88 ± 3.58# | 38.93 ± 3.13# |
The GSIS AUC values in the YD-hi and TAK-875 groups were $133.9\%$ and $134.2\%$ of the model group, respectively, which were significantly higher than in the model group ($p \leq 0.05$); the YD-lo group was $106.4\%$ of the model group (Figure 2F). The insulin secretion levels after glucose stimulation in the YD-hi, YD-lo, and TAK-875 groups were higher than in the model group at 3 and 5 min (all $p \leq 0.05$), while those in the YD-lo group were lower than in the YD-hi and TAK-875 groups. The insulin secretion levels in the YD-hi and TAK-875 groups were close ($p \leq 0.05$) (Figure 2E; Table 7). Hence, compared with the control group, the model group showed higher glucose levels and lower insulin secretion (all $p \leq 0.05$), while YD decreased glucose and increased insulin levels (all $p \leq 0.05$). YD-hi had similar effects to TAK-875 ($p \leq 0.05$).
**TABLE 7**
| Unnamed: 0 | 0min | 3min | 5min | 10min |
| --- | --- | --- | --- | --- |
| Control | 2.48 ± 0.36 | 6.65 ± 0.64 | 3.85 ± 0.93 | 3.48 ± 0.88 |
| Model | 3.78 ± 1.02∗ | 3.63 ± 0.97∗ | 2.13 ± 0.89∗ | 1.43 ± 0.43∗ |
| YD-lo | 3.67 ± 0.75 | 4.06 ± 0.81Δ | 2.82 ± 0.46# | 3.17 ± 0.57#Δ |
| YD-hi | 3.89 ± 0.67 | 5.07 ± 0.84# | 3.17 ± 0.59# | 2.18 ± 0.36# |
| TAK-875 | 3.83 ± 0.72 | 5.15 ± 0.78# | 2.95 ± 0.42# | 2.06 ± 0.34# |
The serum TG and CHOL levels in the YD-hi group were decreased at $60.8\%$ and $77.4\%$ of the model group, respectively ($p \leq 0.05$). The TG levels in the YD-lo and TAK-875 groups were $88.6\%$ and $96.7\%$ of the model group, respectively, higher than in the YD-hi group ($p \leq 0.05$). The CHOL levels in the YD-lo and TAK-875 groups were $93.4\%$ and $98.8\%$ of the model group, respectively, which were also higher than in the YD-hi group ($p \leq 0.05$) (Figure 2G).
Histopathological observation of the pancreas in the control group showed that the structure of the pancreas in the control group was normal, the cell morphology was intact, and no obvious pathological changes were found. In the model group, the acinar cells in the pancreatic tissue of the model group were normal, there were flaky fat deposits between the acinar lobules, and acinar cell hyperplasia and telangiectasia were observed in the pancreatic islets, and the pathology scores were higher than those in the control group ($p \leq 0.05$). A small amount of fat deposition was observed in the YD-hi group, and no acinar cell hyperplasia or telangiectasia was found. Partial fat deposition was seen in the YD-lo and TAK-875 groups, and a small amount of acinar cell dysplasia and telangiectasia were found. The pathological scores of YD-lo, YD-hi and TAK-875 groups were lower than those of the model group ($p \leq 0.05$), while those of YD-hi group were lower than those of TAK-875 group ($p \leq 0.05$) (Figure 3).
**FIGURE 3:** *Histological examination and pathological scores of the pancreas in each group. n = 3/group. ∗p < 0.05 vs. the control group; #
p < 0.05 vs. model group; Δ
p < 0.05 vs. the TAK-875 group (positive control group). The yellow arrow indicates the cell nucleus.*
The GPR40 protein was observed using immunofluorescence (Figure 4). The fluorescence in the control group was the strongest, and the fluorescence in the YD-hi and TAK-875 groups was slightly weaker than in the control group, accounting for $79.6\%$ and $85.8\%$, respectively ($p \leq 0.05$). The YD-lo and model groups showed the weakest GPR40 expression, accounting for $53.8\%$ and $42.6\%$ of the control group, respectively ($p \leq 0.05$). There were no significant differences between the YD-lo and YD-hi groups. Therefore, the increased insulin levels observed in the YD-hi group might be due to a higher expression of GPR40.
**FIGURE 4:** *Fluorescence microscopy for the GPR40 protein. n = 3/group. ∗p < 0.05 vs. the control group; The GPR40 protein was shown in green fluorescence and the nucleus was shown in blue fluorescence. #
p < 0.05 vs. model group; Δ
p < 0.05 vs. the TAK-875 group (positive control group).*
## Screening of YD concentration and intervention time in cells
The results of drug concentration screening showed that $100\%$ YD had the strongest influence on cell proliferation, with YD $10\%$, $5\%$, and $1\%$ also having an effect (all $p \leq 0.05$), but YD at $0.1\%$ and $0.01\%$ had no effect ($p \leq 0.05$) (Figure 5A). Because the YD components could interfere with optical density measurement, $10\%$, $5\%$, and $1\%$ YD were selected for the subsequent experiments. The original decoction concentration was 3 g/mL; therefore, the $1\%$ dose was 30 mg/mL, and the $5\%$ dose was 150 mg/mL. Then, $1\%$ YD was selected for drug intervention time screening, and the results showed that the cell survival rates at 48 and 72 h were better than at 24 h. Although the density of cells in the 72-h group was higher, the cell state was far inferior to that in the 48-h group (Figure 5B). Therefore, 48 h was selected.
**FIGURE 5:** *(A) Drug concentration screening. (B) Drug intervention time screening. n = 8/group). ∗p < 0.05 vs. control group.*
## YD increases insulin secretion, GPR40, and IP3R-1 gene and protein expression in RINm5f cells
The BIS and GSIS levels of the YD-hi group (150 mg/mL YD) were $124.5\%$ and $178.3\%$ of the model group, respectively ($p \leq 0.05$), and were $123.1\%$ and $172.6\%$ of the model group in the TAK-875 group, respectively ($p \leq 0.05$), without significant differences between the YD-hi and TAK-875 groups. The levels of BIS and GSIS in the YD-lo (30 mg/mL YD) group were not significantly different from the model group ($p \leq 0.05$) (Figures 6A, B).
**FIGURE 6:** *(A) Basal insulin secretion (BIS). (B) Glucose-stimulated insulin secretion (GSIS) test. ∗p < 0.05 vs. control group; #
p < 0.05 vs. model group; Δ
p < 0.05 vs. the TAK-875 group (positive control group).*
The relative expression levels of the GPR40 mRNA in the YD-hi and TAK-875 groups were higher than in the model group ($p \leq 0.05$), the YD-lo group was close to the model group ($p \leq 0.05$), and the TAK-875 group was higher than the YD-hi group ($p \leq 0.05$) (Figure 7A). The protein expression levels of GPR40 were similar to that of mRNA, but there were no significant differences between the TAK-875 and YD-hi groups ($p \leq 0.05$) (Figure 7C). The relative expression of IP3R-1 mRNA in the YD-hi and TAK-875 groups was higher than in the model group ($p \leq 0.05$). There were no differences between the YD-lo and model groups. There were no differences in mRNA expression between the YD-hi and TAK-875 groups (Figure 7B). The trend of IP3R-1 protein expression was close to the mRNA expression but was higher in the TAK-875 group than in the YD-hi group ($p \leq 0.05$) (Figure 7D).
**FIGURE 7:** *(A) GPR40 mRNA expression levels. (B) IP3R-1 mRNA expression levels. (C) GPR40 protein expression levels. (D) IP3R-1 protein expression levels. n = 8/group). ∗p < 0.05 vs. control group; #
p < 0.05 vs. model group; Δ
p < 0.05 vs. the TAK-875 group (positive control group).*
## Discussion
YD reduces blood glucose, glycated hemoglobin, body weight, and insulin resistance in patients with T2DM (Chen et al., 2013), but the exact mechanisms are unknown. Therefore, this study aimed to investigate the therapeutic effects and mechanism of YD on impaired insulin secretion in T2DM rats. The results indicated that YD promotes insulin secretion from pancreatic islet β-cells in T2DM rats by regulating the GPR40-IP3R-1 pathway, thereby reducing blood glucose. These results provide, at least in part, how YD increases insulin and decreases glucose levels in T2DM.
In a previous study, YD was found to significantly improve blood glucose, blood lipids, and body weight in patients with T2DM (Chen et al., 2013). YD also improved glucose tolerance and early-phase insulin secretion in T2DM rats and reduced body weight (Gong et al., 2013). Its safety has been proven in long-term clinical use and animal experiments (Chen et al., 2013; Gong et al., 2013). In the present study, different doses of YD were tested (50, 30, 15, and 5 g/kg) on T2DM rats, and 30 g/kg was the best dose for blood glucose control. No animals died during the study, and no obvious physical impairment or ill effects were observed. The CCK-8 assay in RIN-m5f cells also showed that YD had no obvious toxic effects on pancreatic β cells in vitro.
The effects of YD, a natural plant compound decoction, were investigated on blood glucose, blood lipids, insulin secretion, and pancreatic GPR40 expression in T2DM rats. YD reduced blood glucose, improved glucose tolerance, and improved insulin secretion in the first phase of T2DM rats and partially restored the decreased expression of pancreatic GPR40 induced by high glucose and high fat. The effect of a high concentration of YD was close to that of the GPR40 agonist TAK-875. These results were supported by Nagasumi et al. ( Nagasumi et al., 2009), who found that transgenic mice with increased GPR40 gene expression had increased insulin secretion and glucose tolerance. Since GPR40 is a transmembrane protein specifically distributed in the pancreatic islet β-cell membrane, pancreatic tissue can be observed by confocal microscopy, and its protein expression can be calculated by measuring the optical density level. Unfortunately, the downstream IP3R-1 protein cannot be verified by immunohistochemistry, immunofluorescence, or western blot because it is not specific to β-cells and is widely expressed in the whole pancreas. Therefore, the RINm5f cell line had to be used to study IP3R-1.
In vitro, the BIS of RINm5f rat pancreatic β cells and the early-phase insulin secretion function after glucose stimulation were impaired to varying degrees after high-glucose and high-fat injury, and intracellular GPR40 and IP3R-1 expressions were both decreased, as previously observed (Robertson, 2009; Cerf, 2013). In the present study, YD could restore the insulin secretion function of cells to a certain extent, and the expression levels of GPR40 and IP3R-1 were increased to varying degrees. The effect of a high concentration of YD was similar to the positive control drug TAK-875, a drug already known for its effects on GPR40, glucose, and insulin in β-cells (Yashiro et al., 2012; Kaku et al., 2016). It indicated that the YD mixture could improve the damage of islet β cells caused by high-glucose and high-fat environments and promote basal insulin secretion and early-phase insulin secretion after glucose stimulation. The results of the in vivo and in vitro experiments were consistent.
Therefore, it could be hypothesized that high-glucose and high-fat injury inhibits the expression of GPR40 in the pancreatic islet β cell membrane, inhibiting the signal transduction of the GPR40-IP3R-1 pathway, resulting in decreased insulin secretion by β cells. YD could increase the expression of GPR40 and IP3R-1, leading to increased insulin secretion by islet β cells injured by high glucose and high fat.
This study had limitations. YD is a complex mixture of four herbs and several active compounds. Whether a single compound or the additive or synergistic effect of several compounds is responsible for the effects is unknown. Although YD was shown to increase GPR40 and IP3R-1, how YD increases GPR40 and IP3R-1 could not be determined in the present study. Future studies using knockdown and overexpression of the upstream and downstream proteins involved in GPR40 and IP3R-1 signaling will be necessary. YD could increase the insulin secretion of pancreatic β-cells and reduce blood glucose through the GPR40-IP3R-1 signaling pathway. It can be hypothesized that the GPR40-IP3R-1 signaling pathway plays an important role in the impaired insulin secretion and pathogenesis of T2DM.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.
## Author contributions
DW and SW carried out the studies, participated in collecting data, and drafted the manuscript. SX and YL performed the statistical analysis and participated in its design. DW, SW, and JZ participated in the acquisition, analysis, or interpretation of data and drafted the manuscript. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Effects of a games-based physical education lesson on cognitive function in
adolescents
authors:
- Luke M. Gilbert
- Karah J. Dring
- Ryan A. Williams
- Ruth Boat
- Caroline Sunderland
- John G. Morris
- Mary E. Nevill
- Simon B. Cooper
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10043371
doi: 10.3389/fpsyg.2023.1098861
license: CC BY 4.0
---
# Effects of a games-based physical education lesson on cognitive function in adolescents
## Abstract
Despite the importance of physical education (PE) lessons for physical activity in adolescents, the acute cognitive responses to PE lessons have not been explored; a gap in the literature that this study addresses. Following familiarisation, 76 (39 female) adolescents (12.2 ± 0.4 y) completed two trials (60 min games-based PE lesson and 60 min academic lesson) separated by 7-d in a counterbalanced, crossover design. Attention, executive function, working memory, and perception were assessed 30 min before, immediately post, and 45 min post-lesson in both trials. Participants were split into high-and low-fit groups based on a gender-specific median split of distance run on the multi-stage fitness test. Furthermore, participants were split into high and low MVPA groups based on a gender-specific median split of MVPA time (time spent >$64\%$ HR max) during the PE lesson. Overall, a 60 min games-based PE lesson had no effect on perception, working memory, attention, or executive function in adolescents (all $p \leq 0.05$) unless MVPA time is high. The physical activity-cognition relationship was moderated by MVPA, as working memory improved post-PE lesson in adolescents who completed more MVPA during their PE lesson (time*trial*MVPA interaction, $p \leq 0.05$, partial η2 = 0.119). Furthermore, high-fit adolescents displayed superior cognitive function than their low-fit counterparts, across all domains of cognitive function (main effect of fitness, all $p \leq 0.05$, partial η2 0.014–0.121). This study provides novel evidence that MVPA time moderates the cognitive response to a games-based PE lesson; and emphasises that higher levels of fitness are beneficial for cognitive function in adolescents.
## Introduction
Chief Medical Officer guidance states that young people aged 5–18 years should participate in an average of 60 min per day moderate-to-vigorous physical activity (MVPA) to enhance health and well-being (UK Chief Medical Officers, 2019); yet recent data suggest that only $44.6\%$ of children and adolescents in England achieve these recommendations (Sport England, 2021). Given the large proportion of time that young people spend in school, schools present a unique opportunity to assist young people in meeting the daily physical activity recommendations (Hills et al., 2015). Consequently, schools are often designated as a promising environment for increasing physical activity in all young people, irrespective of their background (Slingerland et al., 2012; van Slujis et al., 2021). Within the school environment, as it is compulsory in most Western school systems, physical education (PE) lessons have been identified as an important source of physical activity (Fairclough and Stratton, 2005) and provide adolescents with the opportunity to achieve the recommended 60 min of MVPA per day (Hills et al., 2015). Physical activity opportunities provided by PE lessons are of particular significance for the least active students (Fairclough and Stratton, 2005), with evidence that $30\%$ of adolescents from Western Europe derive all of their daily MVPA from PE lessons (Westerstahl et al., 2005); suggesting that PE lessons are indeed the only form of MVPA for some adolescents (Association for Physical Education, 2020).
Despite the significance of PE as an opportunity for adolescents to participate in MVPA (Zhou and Wang, 2019), the time allocated for PE (and physical activity more broadly) in the school curriculum has often been reduced to accommodate increased instructional time for academic subjects (Rasberry et al., 2011; van den Berg et al., 2016). In the UK for example, the prioritisation of academic subjects is illustrated by a $13\%$ reduction in the number of taught hours for PE since $\frac{2011}{12}$; during which time mathematics, English, and science have increased by 13, 11, and $14\%$, respectively (National Statistics, 2021). However, the reduction in teaching hours for PE is somewhat counterintuitive given that physical activity is positively associated with cognition and academic achievement (Sneck et al., 2019; Garcia-Hermoso et al., 2021). Furthermore, evidence suggests that increasing PE time does not negatively impact academic achievement, even when less time is dedicated to subjects other than PE (Rasberry et al., 2011); thus, reducing PE time (and subsequently physical activity) could be counter-productive for enhancing academic achievement and cognitive function.
Cognitive function can be defined as a variety of brain-mediated functions and processes (Schmitt et al., 2005). These functions allow us to perceive, evaluate, store, manipulate, and use information from external (e.g., environment) and internal (e.g., experiences, memory) sources, before responding to this information (Schmitt et al., 2005). Cognitive functions are clustered into the six domains of executive function, memory, attention, perception, language, and psychomotor functions (Schmitt et al., 2005). A recent meta-analysis by Haverkamp et al. [ 2020] concluded that acute exercise interventions improved cognitive outcomes (Hedges’ $g = 0.31$). Specifically, processing speed ($g = 0.39$), attention ($g = 0.34$) and inhibition (a component of executive function; $g = 0.32$), were enhanced in adolescents following physical activity. This is of importance given that these cognitive domains are the foundation of academic ability (Esteban-Cornejo et al., 2015), Furthermore, as adolescence is a critical stage for the development of cognitive function (Romeo and McEwen, 2006), and a period during which cognitive function and academic achievement are a key focus of the education sector, physical activity opportunities in schools are of particular importance for this population.
The positive effects of physical activity on cognitive function are influenced by several factors such as physical fitness (Garcia-Hermoso et al., 2021), the characteristics of the physical activity (intensity, duration, and modality) and the domain of cognitive function assessed (Williams et al., 2019). Despite the influence of these moderating factors, running is the most common exercise modality in research examining the acute effects of physical activity on adolescent cognitive function. Whilst running appears to be an effective modality (e.g., Budde et al., 2010; Cooper et al., 2012; 2016), it does not reflect typical activity patterns in adolescents (Rowlands et al., 2008) and does not foster long-term adherence (Howe et al., 2010). Recent research has attempted to replicate the high-intensity and intermittent activity patterns that are preferred by adolescents (Bailey et al., 1995) and has demonstrated the positive acute effects of games-based activity (such as basketball) on subsequent cognitive function (Cooper et al., 2018). The positive influence of games-based activity, an activity that requires cognitive engagement, is consistent with the belief that cognitively engaging physical activities elicit a greater effect on subsequent cognitive function (Crova et al., 2014). However, previous studies that have examined games-based activities have done so through direct intervention of researchers and the provision of a games-based activity that was not delivered within the PE curriculum. The applicability of such findings to curriculum delivered PE lessons is thus unknown. Therefore, developing an understanding of the activity patterns of PE, and how PE influences subsequent cognitive function, is an important gap in existing research that should be addressed.
Previous research investigating PE in adolescents has primarily focussed on the activity volume, intensity, and patterns (Fairclough and Stratton, 2005; Slingerland et al., 2011; Hollis et al., 2017; Lyyra, et al., 2017; Mooses et al., 2017; Cheung, 2019; Zhou and Wang, 2019; Wallace et al., 2020); with no examination of the acute cognitive response to a single PE lesson in adolescents. Whilst a recent systematic review and meta-analysis concluded that there were no statistically significant effects of secondary PE interventions on cognitive function in adolescents (i.e., aged >11 years; Garcia-Hermoso et al., 2021), the review was only able to analyse the effect of chronic (> 12 weeks) PE interventions on adolescent cognitive function due to a paucity of research that has examined the acute cognitive responses to a single bout of PE.
To date, the only study to investigate the acute cognitive response to a single PE lesson was conducted in primary school-aged children (aged 8–9 years) and reported no effect of a single PE lesson on memory and attention, when compared to no physical activity (Pirrie and Lodewyk, 2012). However, as the PE lesson, through various activities, required students to undertake 20 min of MVPA by moving around the room performing specific movements (e.g., hopping), it was not reflective of the national curriculum for PE (Department for Education, 2013) or a typical PE lesson. Additionally, cognitive function testing was not administered simultaneously for all participants, with testing ranging from 10 min to 60 min post lesson. The timing of post-exercise cognitive function testing is an important consideration given that this timing has been shown to moderate the subsequent effects on cognition (e.g., Cooper et al., 2018; Hatch et al., 2021). Therefore, the lack of control of this key variable in previous work limits the conclusions that can be drawn regarding the acute effects of a PE lesson on subsequent cognitive function.
Therefore, the primary aim of the study was to examine the acute effects of a curriculum-based PE lesson on subsequent cognitive function in adolescents. A secondary aim was to quantify the physical activity characteristics of a game-based PE lesson, given that these activity patterns are likely to influence the subsequent effects on cognition. Finally, the third aim was to analyse whether there was a moderating effect of physical fitness, or the amount of MVPA completed during the lesson, on subsequent cognitive performance.
## Participant characteristics
To estimate our sample size, an a priori power analysis was conducted using G*Power version 3.1.9.7 (Faul et al., 2007). For a repeated measures approach (two groups, three measurements; two-tailed test; ⍺ = 0.05, power = 0.80), and a small effect size of 0.14 [based on the previous work by Cooper et al. [ 2018]], the minimum sample size was $$n = 84$.$ Subsequently, eighty-five young people (aged 12–13 years) were recruited to participate in the study. However, nine participants failed to complete the study due to absence from school for one of the experimental trials. Therefore, a total of 76 participants completed the study (37 male, 39 female). During familiarisation, all participants underwent anthropometric measures of height, body mass, sitting height, waist circumference and skinfold thickness. A Leicester Height Measure (Seca, Germany), accurate to 0.1 cm, was used to measure height and a Seca 770 digital scale (Seca, Germany), accurate to 0.1 kg, was used to measure body mass. An estimation of maturity offset was made by measuring sitting height, to subsequently estimate years from peak height velocity using methods previously described (Moore et al., 2015). Body mass index (BMI) was calculated and subsequently age-and gender-specific centiles for BMI were derived based on national reference values (Cole et al., 2000). Four skinfold sites were measured (triceps, subscapular, supraspinale, and front thigh) using previously described methods, as a marker of body composition (Dring et al., 2018). Participants were split into high-fit and low-fit groups based on a gender-specific median split of distance run on the multi-stage fitness test (as per previous research (Cooper et al., 2018; Williams et al., 2020)). Likewise, participants were split into high MVPA and low MVPA groups based on the gender-specific median split on MVPA time during the PE lesson. Descriptive participant characteristics are presented in Table 1.
**Table 1**
| Variable | Overall | High-fit (n = 38) | Low-fit (n = 38) | p value a | High-MVPA (n = 35) | Low-MVPA (n = 36) | p value b |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age (y) | 12.2 ± 0.4 | 12.2 ± 0.4 | 12.2 ± 0.4 | 0.695 | 12.2 ± 0.4 | 12.2 ± 0.4 | 0.959 |
| Height (cm) | 157.3 ± 8.1 | 157.9 ± 7.7 | 156.7 ± 8.5 | 0.547 | 156.6 ± 7.4 | 158.0 ± 9.1 | 0.484 |
| Body mass (kg) | 49.0 ± 10.3 | 46.3 ± 7.1 | 51.6 ± 12.2 | 0.025* | 47.6 ± 9.7 | 51.2 ± 11.1 | 0.159 |
| Body mass index (BMI; kg.m2) | 19.7 ± 3.4 | 18.5 ± 1.8 | 20.9 ± 4.0 | 0.002* | 19.3 ± 3.1 | 20.4 ± 3.6 | 0.175 |
| BMI percentile | 64.5 ± 25.7 | 56.9 ± 22.0 | 71.6 ± 27.1 | 0.012* | 61.2 ± 28.1 | 70.7 ± 22.5 | 0.12 |
| Waist circumference (cm) | 66.8 ± 8.1 | 63.6 ± 4.6 | 69.7 ± 9.6 | < 0.001* | 66.1 ± 8.0 | 68.4 ± 8.2 | 0.242 |
| Sum of 4 skinfolds (mm) | 64.2 ± 24.7 | 54.5 ± 16.4 | 73.4 ± 27.8 | < 0.001* | 61.5 ± 23.2 | 68.1 ± 27.1 | 0.273 |
| Maturity offset c | −0.30 ± 0.91 | −0.26 ± 0.89 | −0.36 ± 0.93 | 0.645 | −0.44 ± 0.93 | −0.20 ± 0.86 | 0.245 |
| MSFT distance (m) | 880 ± 320 | 1,100 ± 260 | 660 ± 180 | < 0.001* | 900 ± 300 | 860 ± 340 | 0.655 |
## Study design
Following approval from the institution’s ethical advisory committee (approval number SST-659), participants were recruited from a secondary school in the East Midlands, UK. As per the guidelines for school-based research, head teacher consent was gained. Additionally, written informed consent from parents/guardians and a health screen questionnaire were completed for each participant; this determined each participant’s eligibility for participation by screening for health conditions which may be affected by participation (e.g., exercise-induced asthma). Participants also provided their written assent to participate in the study.
The study employed a randomised, order-balanced, crossover, within-subjects design, consisting of two main experimental trials (PE lesson and academic lesson). A familiarisation took place ~7 d before the first main trial, whereby the protocol of the study was explained to the participants, and they were provided with the opportunity to practice and become familiar with the procedures to be used, including the cognitive function tests. The procedures of the study were also provided to parents/guardians before the study via both written information and a phone call from a member of the research team. Opportunities were provided for participants/parents/guardians to ask questions to clarify any aspect of the study they did not fully understand.
To assess physical fitness participants completed the multi-stage fitness test (MSFT; Ramsbottom et al., 1988) during the familiarisation trial. The MSFT involves progressive 20 m shuttle runs in time with an audio signal, until volitional exhaustion or the point at which participants could not maintain the required running speed to keep time with the audio signal. The MSFT commenced at a speed of 8.0 km.h−1, increased by 1.0 km.h−1 to 9.0 km.h−1 for stage two, and increased by 0.5 km.h−1 for every completed stage thereafter. To monitor heart rate throughout the MSFT (and record maximum heart rate upon completion), participants were fitted with a chest-worn heart rate monitor (Firstbeat Team Sport System; Firstbeat Technologies Ltd., Finland). To encourage maximum effort from the participants, the research team provided verbal encouragement and participants were paced by an experienced member of the research team familiar with the test. Performance on the MSFT was determined by the total distance covered (m). Using a gender-specific median split of distance ran on the MSFT, participants were split into high-fit and low-fit groups [as per previous research (Cooper et al., 2018; Williams et al., 2020)].
As outlined in Figure 1, 60 min following breakfast, participants attended either a 60 min PE lesson or a 60 min academic lesson. A battery of cognitive functions tests were completed 30-min pre-, immediately post-and 45-min post-each lesson.
**Figure 1:** *Experimental protocol.*
## Pre-trial control
The evening before their first experimental trial, participants consumed a meal of their choice and repeated this for their subsequent experimental trial. Subsequently, participants were asked to fast from 10 pm the evening before each experimental trial. To maintain euhydration, water was allowed ad libitum during this time. Additionally, for 24 h prior to each experimental trial participants were also asked to avoid any unusually vigorous physical activity. Parents/guardians were contacted by telephone the evening prior to each experimental trial to ensure compliance with these requirements. All participants followed the pre-trial requirements. On the morning of each experimental trial, participants reported to the school (between 8:45 am and 8:55 am) and consumed a standardised breakfast consisting of cornflakes, milk, and toast; providing 1.5 g carbohydrate per kg of body mass, identical to the breakfast of Williams et al. [ 2020]. A standardised breakfast was provided to control for the potential of breakfast and exercise to interact and affect cognitive function in young people (Cooper et al., 2015).
## Lesson protocol
The single-gender PE lessons consisted of a 60 min football session, completed outdoor on a rubber crumb pitch. Football was selected as the activity given its popularity among young people and within the PE curriculum. A single researcher was present during the PE lesson to facilitate heart rate and GPS data collection and to provide a description of the lesson. The single researcher present played no active part in the lesson, and they did not interact with the participants or teacher. The PE lessons consisted of a warm-up, skill-based drills, and small-sided games. All lessons were delivered by the participants’ normal PE teacher and the research team did not influence the nature or focus of the session. Throughout both experimental trials, participants were fitted with a heart rate monitor (Firstbeat Team Sport System; Firstbeat Technologies Ltd., Finland). Heart rate was monitored continuously throughout both trials. Maximum heart rate and average heart rate were recorded for each trial. Participants were removed from analyses where heart rate data was incomplete or missing ($$n = 5$$), thus 71 participants were included for heart rate analysis. For the PE trial only, participants were also fitted with a PlayerTek Global Position System (GPS) unit (Catapult Sports, Melbourne, Australia). The units were placed outside and left stationary to enable an accurate number of satellite signals to be obtained (> 6 satellites). Once satellite signals were obtained, units were placed between the scapulae using an elasticated shoulder harness. The mean satellite signal strength was 9 ± 1 and horizontal dilution of precision was 1.00 ± 0.16. Participants were removed from analyses where GPS data was incomplete or missing ($$n = 13$$), thus 63 participants were included for GPS analysis. Variables of interest were total distance covered (m) and distance covered at low (<9 km.h−1), moderate (9–13 km.h−1), and high-speed (>13 km.h−1) (based on the speed zones of previous research; Randers et al., 2014). MVPA time was calculated as the percentage of the timetabled lesson time spent above $64\%$ HR max, in accordance with ACSM guidelines (American College of Sports Medicine, 2017). For the academic lesson, participants attended their timetabled 60 min lesson in mathematics ($$n = 32$$), geography ($$n = 16$$), philosophy and ethics ($$n = 13$$), or personal development ($$n = 14$$); as per their normal school timetable. As outlined in Figure 1, both trials followed a time-matched protocol, with the only difference being the lesson attended (i.e., PE or academic).
## Cognitive function tests
The battery of cognitive function tests lasted approximately 12 min and consisted of the Stroop test, Sternberg paradigm, and visual search task; completed in that order on a laptop computer (Lenovo ThinkPad T450; Lenovo, Hong Kong). Preceding each cognitive function test and level, the instruction was presented on the screen to each participant and participants completed 3–6 practice stimuli to re-familiarise with the test, negating any potential learning effects; data for these practice stimuli were discarded. The battery of cognitive function tests were completed in silence, in a classroom, and participants were separated such that they could not interact during the tests. To minimise external disturbances, participants wore sound cancelling headphones and the room lights were dimmed to enhance screen visibility. For all cognitive function tests, participants were instructed to respond as quickly and accurately as possible. This testing procedure has been previously used successfully in a similar study population (e.g., Cooper et al., 2018; Williams et al., 2020). For each cognitive function test, the variables of interest were response time (ms) of correct responses and the proportion (%) of correct responses made. To prevent the influence of unusually slow or fast responses on the analyses, response times were filtered in accordance with procedures previously conducted (Cooper et al., 2018), with minimum (< 100 ms) and maximum (2,000–10,000 ms, depending on task complexity) response time cut-offs applied.
Data for each of the cognitive function tests, across both trials, are displayed in Table 3 (overall), Table 4 (split by fitness group) and Table 5 (split by MVPA group).
## Stroop test
To measure selective attention and executive function, the Stroop test was administered (Miyake et al., 2000). The Stroop test consists of two levels, simple and complex. Both levels of the Stroop test involved a test word being presented in the centre of the laptop screen, with a target and a distractor randomly presented on the left and right sides of the screen. Using the appropriate arrow key (left or right), participants were instructed to select their responses. For the simple level, the test word, target word and distractor word were all presented in a white font; a total of 20 stimuli were presented. On the complex level (colour-interference) there were 40 stimuli, with the participant selecting the colour the word was written in rather than the word itself (e.g., if ‘green’ was written in blue font, the correct response would be blue). Choices remained on the screen until the participant responded, with an inter-stimulus interval of 1 s.
## Sternberg paradigm
The Sternberg paradigm is a commonly used test that measures the domain of working memory (Sternberg, 1969). The test consists of three levels of ascending complexity that utilise a different working memory load (one, three or five items). The one item level consisted of 16 test stimuli and the number ‘3’ is always the target. Whereas on the three and five item levels, the target is three (e.g., ‘A F P’) or five (e.g., ‘B E H R V’) randomly generated letters, respectively; with each containing 32 test stimuli. At the start of each level, the target items were displayed along with instructions to press the right arrow key if a target item was presented and the left arrow key otherwise. The correct response was counterbalanced between the left and right arrow keys for each level. On all levels, the choice stimuli were presented in the centre of the screen, with an inter-stimulus interval of 1 s.
## Visual search
The visual search test comprised two levels; simple and complex. When completing both levels of the visual search test, participants were instructed to press the space key as soon as they could detect a triangle on the screen. Following each response, a new target would appear following a random delay (minimum 500 ms delay). The simple level assessed simple visuomotor speed and required participants respond to 20 targets, which were triangles drawn in solid green lines on a black background. For the complex level, participants responded to 40 targets. The additional complex visual processing component of a background distractor was introduced, induced by random moving dots on the screen (to induce the distracting visual effect of a flickering background, a new set of distractor dots were re-drawn on the screen every 250 ms). Target triangles were initially drawn with just a few visible dots of each line, and the density of these points increased linearly with time until the participants responded.
## Statistical analysis
Response time and accuracy analyses for the cognitive function tests were conducted using R (www.r-project.org). Analyses were conducted using a two-way (trial*time) repeated measures analysis of variance (ANOVA) for each test level, as each level requires a different level of cognitive processing. Prior to analyses, response times were log transformed to exhibit the right-hand skew, typical of human response times. To assess the moderating effect of fitness and MVPA on the exercise-cognition relationship, three-way (trial*time*fitness and trial*time*MVPA) repeated measures analysis of variance (ANOVA) were conducted for each variable from the cognitive function tests. Collinearity between fitness and MVPA time was assessed using Spearman’s rank-order correlation. Where statistically significant three-way interactions existed, post-hoc two-way (trial * time) ANOVA were conducted separately for high-and low-fit adolescents (for trial * time * fitness interactions) and high-and low-MVPA adolescents (for trial * time * MVPA interactions). For all statistically significant effects, partial eta squared effect sizes are included and interpreted as per convention (i.e., 0.01: small; 0.06: medium; 0.14: large).
Maximum heart rate, average heart rate, total distance covered, and distance covered at low, moderate, and high speed during the PE lesson were compared between groups (high-vs low-fit; high-vs low-MVPA) using SPSS (version 28; SPSS Inc., Chicago, IL., USA) using independent samples t-tests. All data are presented as mean ± standard error of the mean (SEM), unless stated otherwise. Statistical significance was accepted as $p \leq 0.05.$
## Lesson characteristics
Descriptive data for the PE lessons are presented in Table 2. During the PE lessons, time spent in MVPA ($$p \leq 0.445$$), average heart rate ($$p \leq 0.093$$), total distance covered ($$p \leq 0.094$$), and the distance covered at low (<9 km.h−1; $$p \leq 0.181$$), moderate (9–13 km.h−1; $$p \leq 0.096$$), and high (>13 km.hr.−1; $$p \leq 0.200$$) speeds were similar between the high and low fit adolescents. However, the maximum heart rate during the PE lessons was significantly higher for the high fit adolescents when compared to low fit adolescents (high-fit; 200 ± 8 beats.min−1, low fit; 195 ± 11 beats.min−1; t[69] = 0.768, $$p \leq 0.046$$, $d = 0.48$). Furthermore, during the PE lesson, total distance ($$p \leq 0.807$$), and the distance covered at low ($$p \leq 0.398$$), moderate ($$p \leq 0.884$$), and high ($$p \leq 0.290$$) speeds were similar between the high and low MVPA adolescents. There was no relationship between fitness and MVPA time during the PE lesson (rs = 0.130, $$p \leq 0.281$$).
**Table 2**
| Variable | Overall | High-fit | Low-fit | p value a | High-MVPA | Low-MVPA | p value b |
| --- | --- | --- | --- | --- | --- | --- | --- |
| MVPA [% total time] | 67 ± 14 | 68 ± 15 | 65 ± 13 | 0.445 | 78 ± 5 | 56 ± 11 | |
| Average heart rate [beats.min−1] | 149 ± 12 | 151 ± 12 | 146 ± 12 | 0.093 | 157 ± 8 | 140 ± 9 | < 0.001** |
| (% HR max) | 72 ± 6 | 73 ± 6 | 70 ± 6 | 0.093 | 75 ± 4 | 67 ± 4 | < 0.001** |
| Maximum heart rate [beats.min−1] | 197 ± 10 | 200 ± 8 | 195 ± 11 | 0.046* | 204 ± 7 | 191 ± 9 | < 0.001** |
| (% HR max) | 95 ± 5 | 96 ± 4 | 94 ± 5 | 0.046* | 98 ± 3 | 92 ± 4 | < 0.001** |
| Total distance [km] | 2.49 ± 0.46 | 2.58 ± 0.44 | 2.39 ± 0.47 | 0.094 | 2.47 ± 0.47 | 2.50 ± 0.48 | 0.807 |
| Distance at low speed [km] | 1.88 ± 0.26 | 1.92 ± 0.21 | 1.84 ± 0.29 | 0.181 | 1.85 ± 0.28 | 1.90 ± 0.25 | 0.398 |
| Distance at moderate speed [km] | 0.41 ± 0.16 | 0.44 ± 0.17 | 0.37 ± 0.16 | 0.096 | 0.41 ± 0.16 | 0.41 ± 0.18 | 0.884 |
| Distance at high speed [km] | 0.20 ± 0.12 | 0.22 ± 0.13 | 0.18 ± 0.11 | 0.200 | 0.21 ± 0.12 | 0.18 ± 0.13 | 0.290 |
## Response times
Response times on the simple level of the Stroop test were similar between the PE and academic lesson trials (main effect of trial, $$p \leq 0.811$$), but did get quicker across the morning (main effect of time, F[2,148] = 23.79, $p \leq 0.001$, partial η2 = 0.077). Additionally, response times on the simple level of the Stroop test were quicker overall in high-fit adolescents (main effect of fitness, F[1,74] = 141.73, $p \leq 0.001$, partial η2 = 0.067); and in those who spent less time in MVPA during the PE lesson (main effect of MVPA, F[1,69] = 40.70, $p \leq 0.001$, partial η2 = 0.021). However, the pattern of change in response times was similar between trials (trial * time, $$p \leq 0.823$$); and the pattern of change was not affected by fitness (trial * time * fitness, $$p \leq 0.476$$) or MVPA time (trial * time * MVPA, $$p \leq 0.416$$).
Overall, response times on the complex level of the Stroop test were quicker on the academic lesson trial than on the PE lesson trial (main effect of trial, F[1,74] = 38.59.4, $p \leq 0.001$, partial η2 = 0.008) and got quicker across the morning (main effect of time, F[2,148] = 33.39, $p \leq 0.001$, partial η2 = 0.078). Furthermore, response times on the complex level of the Stroop test were quicker overall in high-fit adolescents when compared to low-fit adolescents (main effect of fitness, F[1,74] = 294.11, $p \leq 0.001$, partial η2 = 0.074); and in those who spent more time during the PE lesson in MVPA compared to those who spent less time in MVPA (main effect of MVPA, F[1,69] = 106.52, $p \leq 0.001$, partial η2 = 0.034). However, the pattern of change in response times was similar between the academic lesson and PE lesson trials (trial * time, $$p \leq 0.232$$); and was not affected by fitness (trial * time * fitness, $$p \leq 0.933$$) or the time spent in MVPA (trial * time * MVPA, $$p \leq 0.128$$).
Overall, response times on the one item level of the Sternberg paradigm were quicker in the academic lesson trial than PE trial (main effect of trial, F[1,74] = 4.20, $$p \leq 0.04$$, partial η2 = 0.006) and got quicker across the morning (main effect of time, F[2,148] = 19.45, $p \leq 0.001$, partial η2 = 0.122). Additionally, response times on the one item level of the Sternberg paradigm were quicker overall in high-fit adolescents (main effect of fitness, F[1,74] = 175.84, $p \leq 0.001$, partial η2 = 0.121); and in those who spent less time in MVPA compared to those who spent more time in MVPA during the PE lesson (main effect of MVPA, F[1,69] = 73.11, $p \leq 0.001$, partial η2 = 0.052). The pattern of change in response times was similar between trials (trial * time, $$p \leq 0.114$$) and not affected by MVPA (trial * time * MVPA, $$p \leq 0.191$$). However, there was a trial * time * fitness interaction (F[2,148] = 3.88, $$p \leq 0.021$$, partial η2 = 0.017; Figure 2). Upon further inspection, in high fit adolescents the improvement in response times across the morning was greater on the academic trial (trial * time, F[2,72] = 6.80, $$p \leq 0.001$$, partial η2 = 0.089; Figure 2A); whilst in low fit adolescents the pattern of change in response times was similar between the PE and academic trial (trial * time, $$p \leq 0.746$$).
**Figure 2:** *Response times across the morning on the one item level of the Sternberg paradigm on the PE and academic lesson trials for the high-fit (trial * time, p = 0.001; A) and low-fit (trial * time, p = 0.746; B) groups (trial * time * fitness, p = 0.021).*
Response times on the three item level of the Sternberg paradigm were quicker in the academic lesson trial than PE trial (main effect of trial, F[1,74] = 13.07, $$p \leq 0.001$$, partial η2 = 0.041) and got quicker across the morning (main effect of time, F[2,148] = 10.13, $p \leq 0.001$, partial η2 = 0.044). Response times on the three item level of the Sternberg paradigm were also quicker overall in high-fit adolescents when compared to low-fit adolescents (main effect of fitness, F[1,74] = 260.28, $p \leq 0.001$, partial η2 = 0.100), and were influenced by MVPA, with high MVPA adolescents demonstrating a greater reduction in response times when compared with low MVPA adolescents (main effect of MVPA, F[1,69] = 31.28, $p \leq 0.001$ partial η2 = 0.012). Furthermore, response time improved immediately post-lesson in both trials, with a tendency for further improvement 45 min post-lesson in the PE trial, this did not reach statistical significance (trial * time, F[2,148] = 2.97, $$p \leq 0.052$$). Additionally, the pattern of change in response times was not affected by fitness (trial * time * fitness, $$p \leq 0.745$$). However, the pattern of change in response time across the morning was affected by the amount of MVPA completed in the PE lesson (time * trial * MVPA, F[2,138] = 3.10, $$p \leq 0.045$$, partial η2 = 0.025; Figure 3). Upon further inspection, whilst response times improved across the morning on the PE trial in those who completed more MVPA during the PE lesson (trial * time, F[2,68] = 4.41, $$p \leq 0.012$$, partial η2 = 0.063; Figure 3A), the pattern of change in response times across the morning was similar between the PE and academic trials for those who completed less MVPA (trial * time, $$p \leq 0.443$$; Figure 3B).
**Figure 3:** *Response times across the morning on the three item level of the Sternberg paradigm on the PE and academic lesson trials for the high MVPA (trial * time, p = 0.012; A) and low MVPA (trial * time, p = 0.443; B) groups (trial * time * MVPA, p = 0.045).*
Overall, response times on the five item level of the Sternberg paradigm were similar between the academic lesson trial and the PE lesson trial (main effect of trial, $$p \leq 0.693$$), but response times got quicker across the morning (main effect of time, F[2, 148] = 34.98, $p \leq 0.001$, partial η2 = 0.157). Additionally, response times on the five item level were quicker overall in high-fit adolescents when compared to low-fit adolescents (main effect of fitness, F[1,74] = 26.15, $p \leq 0.001$, partial η2 = 0.014); and were influenced by MVPA time during the PE lesson (main effect of MVPA, F[1,69] = 12.76, $p \leq 0.001$, partial η2 = 0.005) as high MVPA adolescents had quicker response time overall. Whilst the pattern of change in response times was similar between the academic lesson and PE lesson trials (trial * time, $$p \leq 0.071$$), and it was not affected by MVPA (trial * time * MVPA, $$p \leq 0.203$$); the pattern of change in response times was significantly affected by fitness (trial * time * fitness, F[2,148] = 3.76, $$p \leq 0.023$$, partial η2 = 0.028; Figure 4). Upon further inspection, response times improved immediately and 45 min following the PE lesson in high fit adolescents when compared to the academic trial (trial * time, F[2,72] = 6.59, $$p \leq 0.001$$, partial η2 = 0.093; Figure 4A). However, there was no difference in the change in response times between the PE and academic trials in the low fit adolescents (trial * time, $$p \leq 0.863$$).
**Figure 4:** *Response times across the morning on the five item level of the Sternberg paradigm on the PE and academic lesson trials for the high-fit (trial * time, p = 0.001; A) and low-fit (trial * time, p = 0.863; B) groups (trial * time * fitness, p = 0.023).*
Overall, response times on the simple level of visual search were similar for the academic lesson and PE lesson trials (main effect of trial, $$p \leq 0.534$$) and did not improve across the morning (main effect of time, $$p \leq 0.822$$). Response times on the simple level of the visual search were quicker overall in high-fit adolescents when compared to low-fit adolescents (main effect of fitness, F[1,74] = 34.74, $p \leq 0.001$, partial η2 = 0.043); and quicker in those who spent more time during the PE lesson in MVPA compared to those who spent less time in MVPA (main effect of MVPA, F[1,69] = 16.69, $p \leq 0.001$, partial η2 = 0.022). Whilst the pattern of change in response times was similar between the academic lesson and PE lesson trials (trial * time, $$p \leq 0.305$$), the pattern of change in response times was affected by fitness (trial * time * fitness, F[2,148] = 4.02, $$p \leq 0.018$$, partial η2 = 0.021; Figure 6). Upon further inspection, in high fit adolescents response times were slower immediately following the PE lesson and 45 min following the academic lesson (trial * time, F[2,72] = 3.34, $$p \leq 0.036$$, partial η2 = 0.049; Figure 6A); whilst the pattern of change in response times was similar following the PE and academic lesson in low fit adolescents (trial * time, $$p \leq 0.130$$; Figure 6B).
**Figure 6:** *Response times across the morning on the simple level of the visual search on the PE and academic lesson trials for the high-fit (trial * time, p = 0.036; A) and low-fit (trial * time, p = 0.130; B) groups (trial * time * fitness, p = 0.018).*
Additionally, response times on the simple level were also affected by the amount of MVPA completed during the PE lesson (trial * time * MVPA, F[2,138] = 10.18, $p \leq 0.001$, partial η2 = 0.071; Figure 7). Upon further inspection, in those who completed more MVPA response times were slower immediately following the PE lesson and 45 min following the academic lesson (trial * time, F[2, 68] = 7.53, $p \leq 0.001$, partial η2 = 0.117; Figure 7A); whilst in those who completed less MVPA, response times were maintained across the morning on the PE trial, and improved 45 min following the academic lesson (trial * time, F[2,72] = 3.64, $$p \leq 0.026$$, partial η2 = 0.046; Figure 7B).
**Figure 7:** *Response times across the morning on the simple level of the visual search on the PE and academic lesson trials for the high MVPA (trial * time, p < 0.001; A) and low MVPA (trial * time, p = 0.026; B) groups (trial * time * MVPA, p < 0.001).*
Overall, response times on the complex level of the visual search test were quicker on the academic lesson trial than in the PE lesson trial (main effect of trial, F[1,69] = 7.97, $$p \leq 0.005$$, partial η2 = 0.024) and improved across the morning (main effect of time, F[2,138] = 16.39, $p \leq 0.001$, partial η2 = 0.115). Response times on the complex level of the visual search test were not affected by fitness (main effect of fitness, $$p \leq 0.147$$) or MVPA time during the PE lesson (main effect of MVPA, $$p \leq 0.616$$). Whilst the pattern of change in response times was similar between the academic lesson and PE lesson trials (trial * time, $$p \leq 0.062$$), and was not affected by fitness (trial * time * fitness, $$p \leq 0.334$$); the pattern of change was affected by MVPA (trial * time * MVPA, F[2,138] = 3.31, $$p \leq 0.037$$, partial η2 = 0.030; Figure 8). Specifically, the pattern of change in response times was similar across the PE and academic trials in adolescents who completed more MVPA during the PE lesson (trial * time, $$p \leq 0.265$$; Figure 8A). However, in those adolescents who completed less MVPA, response times improved immediately following the PE lesson (trial * time, F[2,72] = 5.72, $$p \leq 0.003$$, partial η2 = 0.093; Figure 8B).
**Figure 8:** *Response times across the morning on the complex level of the visual search on the PE and academic lesson trials for the high MVPA (trial * time, p = 0.265; A) and low MVPA (trial * time, p = 0.003; B) groups (trial * time * MVPA, p = 0.037).*
## Accuracy
Accuracy on the simple level of the Stroop test was similar between trials (main effect of trial, $$p \leq 0.691$$); however, accuracy did improve across the morning (main effect of time, F[2,148] = 10.13, $p \leq 0.001$, partial η2 = 0.119). Additionally, accuracy for the simple level of the Stroop test was not affected by fitness (main effect of fitness, $$p \leq 0.849$$) or MVPA (main effect of MVPA, $$p \leq 0.946$$). The pattern of change in accuracy was similar between the academic lesson trial and the PE lesson trial (trial * time, $$p \leq 0.129$$); and it was not affected by time spent in MVPA during the PE lesson (trial * time * MVPA, $$p \leq 0.483$$) or fitness (trial * time * fitness, $$p \leq 0.269$$).
Overall, accuracy on the complex level of the Stroop test was similar between trials (main effect of trial, $$p \leq 0.120$$) and did not change across the morning (main effect of time, $$p \leq 0.351$$). Additionally, accuracy for the complex level of the Stroop test was not affected by fitness (main effect of fitness, $$p \leq 0.220$$) or MVPA time during the PE lesson (main effect of MVPA, $$p \leq 0.150$$). Furthermore, the pattern of change in accuracy was similar between trials (trial * time, $$p \leq 0.987$$); and it was not affected by MVPA (trial * time * MVPA, $$p \leq 0.946$$) or fitness (trial * time * fitness, $$p \leq 0.746$$).
Accuracy on the one item level of the Sternberg paradigm was similar between the PE and academic trials (main effect of trial, $$p \leq 0.839$$), but did improve across the morning (main effect of time, F[2,148] = 6.86, $$p \leq 0.011$$, partial η2 = 0.002). In addition, accuracy on the one item level of the Sternberg paradigm was not different between low and high-fit adolescents (main effect of fitness, $$p \leq 0.297$$). Neither was accuracy affected by MVPA when comparing those adolescents with a high MVPA against those with low MVPA in the PE lesson (main effect of MVPA, $$p \leq 0.774$$). The pattern of change was similar between trials (trial * time, $$p \leq 0.659$$) and was not affected by fitness (time * trial * fitness, $$p \leq 0.453$$) or MVPA during the PE lesson (trial * time * fitness, $$p \leq 0.312$$).
Overall, on the three item level of the Sternberg paradigm, accuracy was similar between trials (main effect of trial, $$p \leq 0.850$$) and did not change across the morning (main effect of time, $$p \leq 0.987$$). Additionally, when comparing high and low-fit adolescents, accuracy was not different (main effect of fitness, $$p \leq 0.767$$), nor was it different when comparing adolescents with high and low MVPA during the PE lesson (main effect of MVPA, $$p \leq 0.344$$). The pattern of change for accuracy was similar between trials (trial * time, $$p \leq 0.633$$) and the pattern of change was not affected by fitness (time * trial * fitness, $$p \leq 0.807$$) or MVPA time during the PE lesson (time * trial * MVPA, $$p \leq 0.206$$).
For the five item level of the Sternberg paradigm, accuracy was similar across trials (main effect of trial, $$p \leq 0.669$$), but accuracy was reduced across the morning (main effect of time, F[2,148] = 9.99, $p \leq 0.001$, partial η2 = 0.117). Accuracy on the five item level of the Sternberg paradigm was not affected by fitness (main effect of fitness, $$p \leq 0.175$$) or by MVPA (main effect of MVPA, $$p \leq 0.407$$). Whilst the pattern of change for accuracy was similar between trials (trial * time, $$p \leq 0.128$$), and it was not affected by fitness (trial * time * fitness, $$p \leq 0.805$$); it was affected by MVPA during the PE lesson (trial * time * MVPA, F[2,138] = 3.41, $$p \leq 0.036$$, partial η2 = 0.047; Figure 5). Upon further inspection, accuracy improved immediately following the PE lesson in those who completed more MVPA during the PE lesson when compared to the academic lesson (trial * time, F[2,68] = 4.30, $$p \leq 0.023$$, partial η2 = 0.119; Figure 5A). However, there was no different in accuracy between the PE and academic trials in those who completed less MVPA during the PE lesson (trial * time, $$p \leq 0.946$$; Figure 5B).
**Figure 5:** *Accuracy presented as the proportion of correct responses across the morning on the five item level of the Sternberg paradigm for the high MVPA (trial * time, p = 0.023; A) and low MVPA (trial * time, p = 0.946; B) groups (trial * time * MVPA, p = 0.036).*
Accuracy on the simple level of the visual search was similar between the PE and academic lesson trials (main effect of trial, $$p \leq 0.212$$), and did not change across the morning (main effect of time, $$p \leq 0.525$$). Additionally, when comparing high-fit to low-fit adolescents, accuracy was not affected by fitness (main effect of fitness, $$p \leq 0.681$$). Likewise, when comparing those adolescents with high MVPA during the PE lesson against those with low MVPA, accuracy was similar (main effect of MVPA, $$p \leq 0.812$$). The pattern of change was similar between trials (time * trial, $$p \leq 0.701$$) and the pattern of change between trials was not affected by fitness (time * trial * fitness, $$p \leq 0.405$$) or by MPVA during the PE lesson (time * trial * MVPA, $$p \leq 0.415$$).
Overall, accuracy on the complex level of the visual search was similar between the PE and academic lesson trials (main effect of trial, $$p \leq 0.665$$), and did not change across the morning (main effect of time, $$p \leq 0.475$$). Furthermore, when comparing high-fit to low-fit adolescents, accuracy was not affected (main effect of fitness, $$p \leq 0.358$$). However, adolescents who completed more MVPA in the PE lesson had greater overall accuracy than those who completed less MVPA (main effect of MVPA, F[1,69] = 3.96, $$p \leq 0.050$$, partial η2 = 0.054). The pattern of change was similar between trials (time * trial, $$p \leq 0.355$$) and the pattern of change between trials was not affected by fitness (time * trial * fitness, $$p \leq 0.358$$) or by MPVA during the PE lesson (time * trial * fitness, $$p \leq 0.227$$).
## Discussion
The main findings of the present study are that a 60 min games-based PE lesson had no effect on perception, working memory, attention, or executive function in adolescents, when compared to a standard academic lesson. However, adolescents who spent a higher percentage of their PE lesson undertaking MVPA experienced some cognitive benefits, as evidenced by improvements in the speed (three item level) and accuracy (five item level) of working memory (as assessed by the Sternberg paradigm). Additionally, the cognitive response to the PE lesson was influenced by fitness as high-fit adolescents demonstrated improvements in speed of working memory, as evidenced by improved response times on the five-item level of the Sternberg paradigm. Furthermore, the present study also shows that high fit adolescents display superior cognition when compared to their lower fit counterparts, across all domains of cognitive function. Finally, MVPA time in the single-gender games-based PE lessons observed in this study was greater than that previously reported for adolescents. On average, adolescents spent $67\%$ of the lesson time undertaking MVPA; although considerable inter-individual variation was evident as time spent in MVPA ranged from $23\%$ in the least active, to $90\%$ in the most active, adolescents.
The present study is the first to examine the acute cognitive response to a games-based PE lesson in adolescents. Overall, perception, working memory, and attention were unchanged following a games-based PE lesson. These results are consistent with those previously reported by Williams et al. [ 2020], whereby, overall, cognitive function remained unchanged in adolescents following a 60 min football session. The unaffected cognitive response to the 60 min games-based activity reported in this study, and by Williams et al. [ 2020], could be attributed to the duration of the exercise protocol. Haverkamp et al. [ 2020] recently reported a larger effect on cognitive function when the acute exercise interventions are of a shorter duration. However, Cooper et al. [ 2018] previously reported enhanced working memory and executive function in adolescents following a 60 min games-based activity (basketball). The inconsistent findings could be attributed to the activity patters of the respective exercise protocols, as Cooper et al. [ 2018] reported higher average heart rate than that reported by Williams et al. [ 2020] and the current study. The pedagogical requirement of a PE lesson could have attributed to a less intense exercise protocol, and resultantly, a lower average heart rate in the current study. Future work should investigate the acute cognitive response to games-based activity of varying intensity and duration.
The present study also demonstrated that high fit adolescents displayed superior cognition compared to their lower fit counterparts, with high fit adolescents exhibiting quicker response times for all domains of cognitive function assessed. The effect of fitness on perception (visual search test) is consistent with the findings of Williams et al. [ 2022], whereby, the beneficial effect of fitness on visual processing speed was first demonstrated. High fit adolescents also demonstrated quicker response times for attention and working memory, which is consistent with recent evidence of the enhancing effect of fitness on these domains of cognitive function (Cooper et al., 2016; Aadland et al., 2017; Haverkamp et al., 2020; Williams et al., 2020). It has previously been stated that participation in exercise modifies the capacity of the nervous system to adapt its organisation to altered demands and environment, termed neuroplasticity (Hötting and Röder, 2013). Engaging in repetitive aerobic physical activity induces increases in angiogenesis (Best, 2010) and the availability of certain neurotrophins, especially brain-derived neurotrophic factor (BDNF; Cho et al., 2012), which are prerequisites for neuroplasticity (Hötting and Röder, 2013). Consequently, increased angiogenesis and BDNF have been suggested as an explanation for the beneficial effect of fitness on cognitive function (Haverkamp et al., 2020). However, it has recently been demonstrated that there is no association between fitness and BDNF concentration in young people (Williams et al., 2022). Therefore, whilst the current study supports the beneficial effect of fitness on cognitive function, future work should seek to explore the potential mechanisms for this association.
The acute cognitive response to exercise was influenced by the participant’s fitness in the present study, with high-fit adolescents demonstrating greater improvements in working memory following the PE lesson. This finding is consistent with the previous evidence that cognitive function is enhanced to a greater extent in high-fit adolescents following an acute bout of games-based activity, when compared to low-fit adolescents (Cooper et al., 2018; Williams et al., 2020). However, in the present study, high-fit adolescents only demonstrated improved working memory following their PE lesson, whereas Cooper et al. [ 2018] were also able to evidence improved attention and executive function in high-fit adolescents following an acute bout of games-based activity. As the acute exercise-cognition relationship is influenced by moderating factors (e.g., age, physical fitness) and exercise characteristics (e.g., duration, intensity) (Williams et al., 2019), a potential explanation for the discrepant findings could be the higher exercise intensity reported by Cooper et al. [ 2018] than in the present study. Furthermore, the enhanced cognitive function reported by Williams et al. [ 2020] in low-fit adolescents when not undertaking exercise, was not reflected in the current study. A potential explanation for not replicating these findings could be that on average, overall fitness was higher for adolescents in the present study when compared with the overall average fitness reported by Williams et al. [ 2020]; and, the difference between fitness for the low and high fit groups was smaller for the present study than previously reported (Williams et al., 2020). The current study presents the novel finding that games-based PE lessons improve cognitive function in high-fit adolescent. Also highlighting that the exercise protocol and individual participant characteristics influence the cognitive response and should be considered in future research.
Supporting the concept that the intensity of physical activity is important for the subsequent cognitive effects, a novel finding of the present study is that the acute cognitive response to a games-based PE lesson is enhanced for those adolescents who spent more time undertaking MVPA during their PE lesson. Specifically, adolescents with high MVPA during the PE lesson demonstrated improved working memory immediately post-PE lesson; working memory was also enhanced 45-min post-PE lesson. The absence of collinearity between fitness and MVPA amongst participants in the present study, when combined with the positive effect of MVPA on working memory, suggests MVPA enhances acute cognitive function, independent of fitness. An explanation for these findings could be the potential influence of physical activity intensity on functional connectivity across brain regions, primarily those involved in memory and executive function (Moore et al., 2021); resultantly, the efficiency of evaluating the stimulus is increased (Chang et al., 2013). The findings from previous studies investigating the role of physical activity intensity on cognitive function are equivocal, with positive (Syväoja et al., 2013; Lee et al., 2014), inconsistent (Aadland et al., 2017), and negative (Cadenas-Sanchez et al., 2020; Ludyga et al., 2020; Williams et al., 2022) results. The discrepancy between our findings and those previously reported could be attributed to methodological differences, such as utilising self-report measures of physical activity (Syväoja et al., 2013; Aadland et al., 2017); or, the assessment of habitual physical activity intensity (Lee et al., 2014; Cadenas-Sanchez et al., 2020; Williams et al., 2022). Whereas, due to the dynamic relationship between exercise and circulating neurotrophic factors that enhance cognitive function (Cho et al., 2012), the present study investigated the impact of physical activity intensity on cognitive function immediately following a bout of physical activity. Therefore, to the authors’ knowledge, our study is the first to examine the effect of device-measured intensity during a single bout of physical activity on acute cognitive function in adolescents, demonstrating that physical activity behaviour impacts working memory in adolescents.
The present study provides valuable insight into the activity patterns during 60 min single-gender, games-based PE lessons. Overall, across the four lessons observed, MVPA was undertaken for 67 ± $14\%$ (40 ± 8 min) of the timetabled lesson time (60 min). Therefore, the recommended minimum $50\%$ of PE lesson time spent in MVPA was exceeded (Association for Physical Education, 2020). Interestingly, MVPA time did not differ between high and low fit adolescents, suggesting that MVPA time during a PE lesson is independent of fitness. Whilst average MVPA time exceeded $50\%$, time spent in MVPA ranged from $23\%$ in the least active to $90\%$ in the most active adolescents. Consequently, the opportunity for PE to allow all students meet the 60 min of MVPA per day recommendation was not maximised and future work should explore how PE can be modified to increase the amount of MVPA time for the least active students. The MVPA time reported in the present study is higher than the $48.6\%$ (41.3–$55.9\%$) of lesson time previously reported for adolescent PE lessons by Hollis et al. [ 2017] in their systematic review and meta-analysis. It has previously been stated that MVPA time varies according to the type of activity students engaged in, with team invasion game lessons (e.g., basketball and football) eliciting higher MVPA time than dance, gymnastics, or individual direct competition lessons (Fairclough and Stratton, 2005). However, the MVPA time in the present study is higher than that reported by Fairclough and Stratton [2005] for team invasion game lessons ($46\%$). The higher MVPA reported in the present study could be a result of changes in PE over time and/or the recommendation by the Association for Physical Education [2020] to increase MVPA time in lesson; or, methodological inconsistencies, including the use of observational methods to monitor MVPA time in previous work (Fairclough and Stratton, 2005). Whilst the present study provides a novel contribution to the understanding of MVPA during PE lessons using device-based measures of activity, future studies should examine device measured MVPA in PE lessons across all domains of the national curriculum.
Whilst the present study provides novel insight regarding the effects of curriculum PE lessons on subsequent cognitive function in adolescents, it is not without limitations. Firstly, the present study only recruited participants from a single school year (UK year 8), and given the changes in physical activity and fitness that occur across adolescence, the influence of the PE lesson on cognitive function might be different across stages of adolescence. Additionally, as the present study observed football PE lessons at a single school, the generalisability is limited. Future work should observe PE lessons across several secondary schools to better reflect PE nationally. Moreover, future work across multiple secondary schools would permit further exploration of fitness and MVPA as continuous variables, rather than categorical variables as used in the present study. Furthermore, whilst a games-based PE lesson is a domain of the national curriculum for PE in the United Kingdom (team direct competition), the cognitive response to a PE lesson was not assessed across the remaining domains of the national curriculum; and it cannot be assumed that the responses to all types of PE would be the same. Therefore, future work should explore the influence of all domains of the national curriculum for PE on cognitive function, across all stages of adolescence. Likewise, the examination of MVPA time and exercise intensity was limited to a games-based activity lesson; MVPA and the intensity for all domains of PE lessons should be established in future works.
## Conclusion
In summary, the present study highlights that the acute cognitive responses to a games based-PE lesson are moderated by physical activity intensity, whereby adolescents who completed more MVPA during the PE lesson experienced greater cognitive benefits. The findings of the present study would suggest that, if physical activity intensity during PE lessons is high, those lessons will enhance subsequent cognition and ultimately contribute to enhancing academic achievement. Furthermore, the acute cognitive response to a games based-PE lesson is moderated by fitness, whereby high-fit adolescents experienced improved cognitive function compared to their lower-fit peers. Finally, the present study contributes to the growing body of evidence that high fit adolescents demonstrate superior cognition than their lower fit counterparts; highlighting the importance of interventions aimed at improving fitness in this population.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
This study was approved by Nottingham Trent University School of Science and Technology Human Invasive Ethics Committee.
## Author contributions
LG, KD, RW, and SC contributed to the conception and design of the study. LG, KD, RW, RB, CS, JM, and SC contributed to data collection. LG and SC performed the statistical analysis. LG wrote the first draft of the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Antibacterial properties of antimicrobial peptide HHC36 modified polyetheretherketone
authors:
- Weijia Gao
- Xiao Han
- Duo Sun
- Yongli Li
- Xiaoli Liu
- Shihui Yang
- Zhe Zhou
- Yuanzheng Qi
- Junjie Jiao
- Jinghui Zhao
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10043374
doi: 10.3389/fmicb.2023.1103956
license: CC BY 4.0
---
# Antibacterial properties of antimicrobial peptide HHC36 modified polyetheretherketone
## Abstract
### Introduction
Polyetheretherketone (PEEK) is considered to be a new type of orthopedic implant material due to its mechanical properties and biocompatibility. It is becoming a replacement for titanium (Ti) due to its near-human-cortical transmission and modulus of elasticity. However, its clinical application is limited because of its biological inertia and susceptibility to bacterial infection during implantation. To solve this problem, there is an urgent need to improve the antibacterial properties of PEEK implants.
### Methods
In this work, we fixed antimicrobial peptide HHC36 on the 3D porous structure of sulfonated PEEK (SPEEK) by a simple solvent evaporation method (HSPEEK), and carried out characterization tests. We evaluated the antibacterial properties and cytocompatibility of the samples in vitro. In addition, we evaluated the anti-infection property and biocompatibility of the samples in vivo by establishing a rat subcutaneous infection model.
### Results
The characterization test results showed that HHC36 was successfully fixed on the surface of SPEEK and released slowly for 10 days. The results of antibacterial experiments in vitro showed that HSPEEK could reduce the survival rate of free bacteria, inhibit the growth of bacteria around the sample, and inhibit the formation of biofilm on the sample surface. The cytocompatibility test in vitro showed that the sample had no significant effect on the proliferation and viability of L929 cells and had no hemolytic activity on rabbit erythrocytes. In vivo experiments, HSPEEK can significantly reduce the bacterial survival rate on the sample surface and the inflammatory reaction in the soft tissue around the sample.
### Discussion
We successfully loaded HHC36 onto the surface of SPEEK through a simple solvent evaporation method. The sample has excellent antibacterial properties and good cell compatibility, which can significantly reduce the bacterial survival rate and inflammatory reaction in vivo. The above results indicated that we successfully improved the antibacterial property of PEEK by a simple modification strategy, making it a promising material for anti-infection orthopedic implants.
## Introduction
With the increasing incidence of trauma, joint degeneration and bone tumors, more and more orthopedic implants are being used in orthopedic surgery (Mahyudin et al., 2016). These orthopedic implants play an important role in promoting fracture healing and restoring bone-to-joint anatomy and function. Polyetheretherketone (PEEK) is a high-strength semi-crystalline nonmetallic polymer. The elastic modulus of PEEK is 3–4 Gpa, which is closer to human cortical bone than pure titanium, which can effectively eliminate stress shielding and prevent implants from falling off due to bone absorption (Ma et al., 2021). In addition, PEEK has good chemical stability and does not release harmful by-products such as metal ions. As a non-metallic material, PEEK does not interfere with magnetic resonance imaging (MRI) (Ma et al., 2021). These excellent properties make it a potential substitute for titanium. Orthopedic implants such as intervertebral cages, joint replacement devices and fracture anchors made of PEEK have been gradually applied to clinical practice (Stratton-Powell et al., 2016; He et al., 2021).
Orthopedic implant-related infection (OII) is one of the main causes of orthopedic implant failure, which has destructive effects on patients and health systems. In most cases, it is necessary to remove or revise infected implants for treatment, which will increase the pain and death risk of patients. In addition, it is a huge economic burden to the health system (Nie et al., 2022). Gorth et al. showed that under the same conditions, the biofilm formation rate of PEEK surface was significantly higher than that of Ti, and its affinity for biofilm was 1–6.7 times that of Ti (Gorth et al., 2012). Once free bacteria in the blood or tissue colonize the surface of PEEK, biofilm formation rapidly leads to implant-related infection and eventually implant failure (Croes et al., 2018). In addition, biofilms are difficult to be eliminated by the immune system, and systemic antibiotic therapy is unsatisfactory (Yan and Bassler, 2019). Surgical removal of the implant may lead to disability and impose significant pain and economic burden to patients. Therefore, in order to expand the clinical application of PEEK and make it a better alternative to titanium as an orthopedic implant, it is necessary to improve its antibacterial performance to prevent infection related to PEEK-based implants.
Surface antibacterial modification is a widely used measure of antibacterial modification that can be implemented by preparing antibacterial morphologies or by direct deposition of antibacterial agents on the surface of materials. Many scholars have successfully carried out surface antibacterial modification on different materials, such as stainless steel, glass, metal, cotton fabric, quartz and polyurethane (Xu et al., 2015, 2017, 2018; Guo et al., 2018; Li et al., 2020; Wu et al., 2021), by depositing antibacterial agents directly. In the process of surface antibacterial modification, antibacterial agents play an important role as components to endow the material with antibacterial properties, which usually include antibiotics, naturalextracts, antimicrobial peptides, polymers, metalsandmetaloxides, selenium, fluoride, silicon nitride, graphene oxide and so on (Gao et al., 2022). Among these antibacterial agents, antimicrobial peptides (AMPs) with excellent properties are gradually applied for surface antibacterial modification of biomaterials. AMPs are polypeptides with antibacterial activity, which have positive charge and amphiphilic structure (Yan et al., 2021). Some of them are important components of the innate immunity of most organisms against pathogens, possessing excellent broad-spectrum antibacterial activity and low biological toxicity. AMPs can bind to negatively charged bacterial cell membranes through non-specific electrostatic interactions, leading to cell death by destroying cell membranes. Such antimicrobial mechanisms make it difficult for bacteria to develop resistance. In addition, AMPs have highly selective toxicity to bacterial cell membranes and are not easy to damage mammalian cells (Xi et al., 2016; Zhang et al., 2021). Recently, many studies have confirmed that AMPs could improve the antibacterial properties of biological materials (Pranantyo et al., 2016, 2018, 2019, 2021; Albada and Metzler-Nolte, 2017; Yang et al., 2017; Cao et al., 2018; Zhang et al., 2020; Wang H. et al., 2021; Yan et al., 2021; Jin et al., 2022). HHC36 (KRWWKWWRR) is a highly effective AMP containing nine amino acids predicted and designed by the artificial neural network. It has high antibacterial activity against a variety of multi-resistant “superbugs,” such as MRSA. It exhibits better antibacterial properties than conventional antibiotics (tobramycin, ciprofloxacin, imipenem, ceftazidime) and clinical candidate AMPs (such as MX226 and hLF1-11) (Cherkasov et al., 2009; Kazemzadeh-Narbat et al., 2013). In recent years, some reports have shown that the antibacterial properties of biomaterials have improved after the integration of HHC36 (Lim et al., 2018; Chen et al., 2019, 2020, 2021; Cheng et al., 2021; Guo et al., 2022; Wang et al., 2022).
In this work, we prepared a 3D porous structure on the surface of PEEK by etching with concentrated sulfuric acid, and removed the residual sulfuric acid by hydrothermal treatment. Then, HHC36 was immobilized on the sulfonated and hydrothermal-treated PEEK surface by a simple solvent volatilization method. Staphylococcus aureus (S. aureus) and *Escherichia coli* (E. coli) were used to evaluate the in vitro antibacterial properties of the samples. Mouse fibroblast cells (L929) and rabbit erythrocytes were used for in vitro cytocompatibility evaluation of samples. In addition, a rat subcutaneous infection model was constructed to evaluate the in vivo anti-infection performance and biocompatibility of the samples.
## Sample preparation
The disk-shaped Ti and PEEK (10 mm × 1 mm) were ultrasonically washed in acetone, ethanol and ultra pure water after being polished with several grades of silicon carbide sandpaper (800 #, 1,200 #, 2000 # and 5,000 #). The dried PEEK disks were placed in conical flasks containing $98\%$ concentrated sulfuric acid and magnetically stirred for 5 min, and then put into deionized water to terminate the reaction. A hydrothermal treatment was then performed at 120°C for 4 h to remove residual sulfuric acid from the surface, and the treated PEEK was designated SPEEK. Subsequently, 50 μL of a 1.5 mg/mL solution of HHC36 in ethanol (KRWWKWWRR, $98.91\%$ pure lyophilized powder, chinapeptides Co, Ltd., China) was applied dropwise to SPEEK and gently air-dried. The loading process was performed in a sterile environment and repeated 20 times. After the HHC36 loading was complete, the samples were gently rinsed three times with Phosphate buffered solution (PBS, Solarbio, China) to remove loosely adhered peptides from the surface. The SPEEK that loaded HHC36 was named HSPEEK. Using the same procedure, HHC36 was loaded onto the untreated PEEK surface. The PEEK that loaded HHC36 was named HPEEK.
## Sample characterization
Field emission scanning electron microscope (FESEM, Hitachi S-4800, Japan) and energy dispersive X-ray spectrometer (EDS, Genesis2000, AMETEK, United States) were used to obtain FESEM images and EDS spectra of Ti, PEEK, SPEEK and HSPEEK. A contact angle meter (DSA25, KRUSS,Germany) was utilized to evaluate the surface hydrophilicity or hydrophobicity of the samples. Ultrapure water (5 μL) was dropped on the surface of the sample, and a digital camera was used to take photographs and record the contact angle.
The surface microstructure and chemical composition of the samples were examined by FESEM and EDS. FESEM images showed that Ti has a relatively flat surface. PEEK is slightly rougher than Ti, and slight protrusions could be observed. Compared with relatively flat Ti and PEEK, a 3D network structure could be observed on the surface of SPEEK (Figure 1A). The pore size of HSPEEK is significantly smaller than that of SPEEK, which may result from the loading of HHC36 (Yuan et al., 2019). Nitrogen peaks were identified by EDS on the HSPEEK surface (Figure 1B), indicating that HHC36 was successfully immobilized on the HSPEEK surface. The water contact angle was used to evaluate the hydrophilicity or hydrophobicity of the sample surface (Figure 1C). The water contact angle of Ti was 75.60° ± 2.81°. The water contact angle of PEEK was 84.33° ± 1.29°, which illustrates that PEEK is more hydrophobic than titanium ($p \leq 0.01$). After sulfonation and hydrothermal reaction, the water contact angle of SPEEK increased to 93.90° ± 2.43°and became more hydrophobic ($p \leq 0.001$). After loading HHC36, the water contact angle of HSPEEK decreased significantly to 41.47° ± 0.85° and became hydrophilic ($p \leq 0.001$), which may result from the hydrophilic residues in HHC36 (Wang et al., 2022).
**Figure 1:** *(A) FESEM morphology at low and high power of Ti, PEEK, SPEEK and HSPEEK, (B) EDS spectra of Ti, PEEK, SPEEK and HSPEEK, (C) Water contact angles of Ti, PEEK, SPEEK and HSPEEK, and (D) Release of HHC36 from HSPEEK and HPEEK in PBS. (n = 3, **p < 0.01, ***p < 0.001).*
## Release profile of AMP HHC36
One HSPEEK and one HPEEK were immersed in 10 ml of PBS at pH 7.4 respectively, and incubated at 37°C with constant shaking (30 rpm/min) for different times (4, 12, 24, 48, 72, 120, and 168 h). 0.3 mL of the supernatant was collected at each time point and subsequently supplemented with PBS to keep the total volume of solution constant. Then, the HHC36 content in the supernatant was detected with a BCA protein assay kit (BCA, Biosharp, China). To determine the total amount of HSPEEK-loaded HHC36, one HSPEEK was sonicated in $2\%$ Sodium dodecyl sulfate (SDS, Solarbio, China) solution for 2 h in reference to Shen et al. [ 2019]. After centrifugation for 5 min (12,000 rpm/min), the supernatant was assayed for HHC36 using the BCA protein assay kit. Each experiment was repeated 3 times.
HHC36 was eluted from the HSPEEK using SDS solution under sonication, and the actual total loading of HHC36 was measured to be 685.34 μg/cm2. According to the actual loading capacity, it could be calculated that the cumulative release rate of HHC36 was $71.15\%$ at 24 h and $92.39\%$ at 72 h. As shown in Figure 1D, HHC36 on the HSPEEK was slowly released until 10 d after an initial fast release. For HPEEK, only trace amounts of HHC36 remained on its surface and no sustained release was observed. Since the loading of HHC36 required 20 cycles, the theoretical total loading after 20 loading cycles was further calculated to be 954.93 μg/cm2, and the loading efficiency was $71.82\%$, which might be due to the loss of HHC36 during the loading process.
## Bacterial preparation
Single colony of S. aureus (ATCC 29213) and E. coli (ATCC 25922) were separated by plate scribing respectively, and then the single colony was added to 3 mL of LB liquid medium and shaken overnight at 37°C (150 rpm/min). Subsequently, 100 μL of each bacteria suspension was added to 6 mL of LB liquid medium. To obtain bacteria in the logarithmic growth period, the bacteria suspension was incubated at 37°C for 3 h, so that its OD600nm was between 0.6 and 1.0. The concentration of bacterial suspension was determined by the plate counting method, and the bacterial concentration required for subsequent experiments was obtained by appropriately diluting the bacterial suspension.
## Bacterial morphology observation
Ti, PEEK, SPEEK, and HSPEEK were placed in a 24-well plate, and 1 mL of a 1 × 107 CFU/mL suspension of S. aureus and E. coli was added to each well and incubated at 37°C for 24 h. The samples were taken out and rinsed 3 times with sterile PBS to remove non-adherent bacteria from the samples’ surface. The samples of each group were then placed in $2.5\%$ glutaraldehyde stationary liquid and fixed at 4°C for 8 h. The samples of each group were successively placed in ethanol solution (30, 40, 50, 60, 70, 80, 90, 95, and $100\%$) for gradient dehydration, with each gradient dehydration lasting for 15 min. Then the samples were dried and sprayed with gold. Finally, the morphology of bacteria adhering to the surface of the samples was observed by FESEM.
## Bacterial counting assay
Ti, PEEK, SPEEK and HSPEEK were placed in test tubes and 2 mL of 1 × 107 CFU/mL suspension of S. aureus and E. coli was added to each tube. After incubation at 37°C for 24 h, the turbidity of the bacterial suspension was recorded by photography, and the OD600nm of the bacterial suspension was measured using an enzyme-labeled instrument (BL340, Biotech, United States). The bacterial suspension was then appropriately diluted with PBS, and 100 μL of the diluted bacterial suspension was uniformly coated on LB plates. After incubation at 37°C for 24 h, the bacterial viability rate was calculated using the following formula: P%=A÷B×$100\%$, where A represents the average number of colonies on PEEK, SPEEK and HSPEEK, and B represents the average number of colonies on Ti.
## Agar diffusion assay
100 μl of S. aureus and E. coli suspension with the concentration of 1 × 108 CFU/mL were evenly spread on LB solid medium. Ti, PEEK, SPEEK and HSPEEK were uniformly arranged on the surface of the solid medium. After incubation at 37°C for 24 h, the ability to inhibit bacterial growth was evaluated by generally observing the width of the inhibitory band around the samples.
## Live/dead bacterial assay
Ti, PEEK, SPEEK and HSPEEK were placed in a 24-well plate, and 1 mL of a 1 × 107 CFU/mL suspension of S. aureus and E. coli was added to each well. After incubation at 37°C for 48 h, samples were gently washed 3 times with PBS. Fluorescent staining was then performed using a live/dead bacterial staining kit (BBcellProbe, China) with the addition of 300 μL per well mixed solution containing $0.2\%$ N01 and $0.5\%$ propidium iodide (PI) solution and then incubated for 30 min at 37°C in the dark. Finally, the results were observed under a confocal laser scanning microscope (Olympus, Japan).
## MTT assay
Ti, PEEK, SPEEK and HSPEEK were placed in a 24-well plate, 1 mL of a 1 × 107 CFU/mL suspension of S. aureus and E. coli was added to each well, and after incubation at 37°C for 48 h, the samples were transferred to a new 24-well plate and washed with PBS. Then, 1 mL of MTT (Biosharp, China) solution was added to each well and incubated at 37°C for 1.5 h. Subsequently, an equal amount of dimethyl sulfoxide (DMSO, Solarbio, China) was added and incubated on a shaker in the dark for 20 min (100 rpm/min). Finally, the solution was collected, and the OD540 nm was detected by an ELISA reader (Bio-Tek, United States).
## Cell culture
L929 cells (Jilin Key Laboratory of Dental Development and Bone Reconstruction, Changchun, China) were cultured in H-DMEM (Hyclone, United States) at 37°C and $5\%$ CO2 atmosphere, supplemented with $10\%$ fetal bovine serum (FBS, GIBCO, United States) and $1\%$ penicillin streptomycin (Penicillin Streptomycin Solution, Hyclone, United States). L929 cells were passaged by pancreatin after reaching $80\%$ confluence.
## Live/dead cell assay
The toxicity of the extracts of each sample to L929 cells was evaluated by cell live/dead staining. The extracts of Ti, PEEK, SPEEK and HSPEEK were prepared according to the standard of Biological evaluation of medical devices-Part 12: Sample preparation and reference materials (ISO 10993-12: 2012, IDT). All samples were sterilized by ultraviolet irradiation for 1 h, and then turned over after 30 min of irradiation. The sample was then transferred to a new centrifuge tube and extracted with H-DMEM containing $10\%$ fetal bovine serum at a ratio of 3 cm2/mL. The samples were put into a CO2 constant temperature cell culture incubator at 37°C for 24 h to prepare the extract. L929 cells at a density of 2 × 103 cells per well were then seeded in a 96-well plate. After 24 h of incubation, the supernatant was discarded, and 100 μL extracts were incubated again for 24 h. The live/dead assay was performed using a calcein AM/PI double staining kit (Beyotime, China). The cells were observed using a fluorescence microscope (Olympus, Japan). Live cells showing green fluorescence were stained only by calcein AM, whereas dead cells showing red fluorescence were stained only by PI.
## CCK8 assay
The Cell Counting Kit-8 (CCK8, Biosharp, China) was used to evaluate the effect of Ti, PEEK, SPEEK and HSPEEK extracts on L929 cells proliferation on 1, 3, and 5 d. L929 cells at a density of 2 × 103 cells per well were inoculated in a 96-well plate and the supernatant was discarded after incubation at 37°C for 24 h. Then 100 μL extract of the dip was added to each well and continued to culture until 1, 3, and 5 d. At each time point, H-DMEM/CCK8 premixed solution with a volume ratio of 10:1 was used to replace the culture medium. After incubation at 37°C for 1 h, 100 μL of each group was transferred to a 96-well plate. The OD450nm was measured using an enzyme-labeled instrument (BL340, Biotech, United States).
## Haemolysis test
The hemolytic activity of samples in each group was evaluated using rabbit red blood cells. 8 mL of blood was collected from the rabbit ear vein, anticoagulated with heparin, and then diluted in 10 mL of $0.9\%$ normal saline (NS). Ti, PEEK, SPEEK, and HSPEEK were added to diluted rabbit blood containing anticoagulant, in which normal saline was the negative control group ($0\%$ hemolysis), and distilled water (DW) was the positive control group ($100\%$ hemolysis). The above test tubes were placed in a thermostatic water bath at a temperature of 37°C for 30 min. 0.2 mL diluted anticoagulant rabbit blood was then added to each tube, mixed and placed in a thermostatic water bath for 60 min. After 1,500 rpm/min centrifugation for 5 min, the degree of hemolysis was recorded by a camera. Then, 100 μL supernatant was taken out of each tube and placed in a 96-well plate, and hemoglobin release from the supernatant was detected using a microplate reader at OD545nm.
Hemolyticrate%=ODsamples−ODnegativeODpositive−ODnegative×$100\%$,Where samples represent Ti, PEEK, SPEEK, and HSPEEK. Negative represents NS, and positive represents DW.
## Establishment of rat subcutaneous infection model
The experiment was approved by the Institutional Animal Care and Use Committee of Jilin University (No. 20220456). For the rat subcutaneous infection model, 16 Wistar rats (4 rats per group for Ti, PEEK, SPEEK, and HSPEEK) were used. All surgical instruments are high-temperature and high-pressure sterilized. The back hair of these rats was shaved off preoperatively and disinfected with povidone iodine. Intraoperatively, they were anesthetized by inhalation of $1.5\%$ isopentane, and a 1 cm incision was made on the back, after which the samples (10 mm × 1 mm) were inserted subcutaneously. The skin incision was then carefully sutured. Finally, 100 μl dilution of S. aureus (1 × 107 CFU/mL) was injected into the subcutaneous sample surface by a microinjector.
## In vivo anti-infection performance evaluation
After 7 d of implantation, the samples under the skin were removed and soaked in PBS. The test tube containing the samples was vortexed for 30 s and sonicated for 10 min to allow bacteria adhering to the sample surface to fall off. PBS containing bacteria was appropriately diluted and plated on agar plates for incubation at 37°C for 24 h. The bacterial viability rate was calculated using the formula: P%=A÷B×$100\%$, where A represents the average number of colonies on PEEK, SPEEK and HSPEEK samples, and B represents the average number of colonies on Ti samples.
Figure 5A showed that the bacterial colonies on Ti, PEEK and SPEEK covered most of the solid medium, and only a few colonies were found in the HSPEEK. Figure 5B showed the bacterial viability rate on the surface of samples in each group. No significant differences in bacterial viability were observed for the Ti, PEEK, and SPEEK surfaces ($p \leq 0.05$). The bacterial viability rate of $7.62\%$ in the HSPEEK group was significantly lower than that of the other groups ($p \leq 0.001$), indicating that HSPEEK could significantly reduce the number of S. aureus on its surface, which was consistent with the results of the in vitro experiments.
**Figure 5:** *(A) Images of S. aureus colonies on the surface of Ti, PEEK, SPEEK and HSPEEK, (B)
S. aureus viability rate on the surface of each group of samples, (C) H&E staining results of soft tissue surrounding each group of samples. Black arrows represent inflammatory cells and yellow arrows represent healthy tissue cells, and (D) The density of inflammatory cells in the surrounding soft tissue of each group of samples. (n = 4, ***p < 0.001).*
## In vivo biocompatibility evaluation
After the removal of implants, the skin around the samples was collected and fixed in paraformaldehyde. Skin tissue was subsequently embedded in paraffin for histological sectioning. H&E staining was used to stain inflammatory cells in skin tissue. Representative images were taken using a light microscope (Olympus, Japan). The inflammatory cells were counted by ImageJ software, and the biocompatibility of each group was evaluated according to the density of inflammatory cells.
Figure 5C showed the severe inflammatory response of the skin in contact with the surfaces of Ti, PEEK, and SPEEK, where a large infiltration of inflammatory cells was observed. Skin in contact with the HSPEEK surface had only a few inflammatory cells. Figure 5D showed that the density of inflammatory cells in the skin in contact with the HSPEEK surface was significantly lower than Ti, PEEK, and SPEEK groups ($p \leq 0.001$), suggesting that HSPEEK reduced not only the number of bacteria but also the inflammatory response caused by bacteria.
## Statistical analysis
All data are expressed as means ± standard deviation (SD) from triplicate independent experiments. One-way analysis of variance (ANOVA) and Tukey’s multiple comparison tests were used to evaluate the statistically significant differences among groups. All statistical analyses were performed using SPSS 19.0 software (SPSS, Chicago, IL, United States). * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001.$ All experiments were performed in triplicate and repeated at least three times.
## In vitro antibacterial experiments
The effects on the cell morphology of S. aureus and E. coli on different samples were determined by FESEM. As shown in Figure 2A, the two bacteria on the surfaces of Ti, PEEK and SPEEK were complete. Two bacteria on the HSPEEK surface became severely distorted. These results indicated that HHC36 could destroy the cell membrane integrity of S. aureus and E. coli, and Ti, PEEK and SPEEK did not possess this ability.
**Figure 2:** *(A) Morphology of S. aureus and E. coli after 24 h coculture with Ti, PEEK, SPEEK and HSPEEK, (B) Photos of the turbidity of the bacterial suspension after 24 h of coculture with each group of samples, (C) The OD600nm value of the bacterial suspension, (D) Typical images of colonies incubated for 24 h with each group of samples, (E) Viability rate of S. aureus and E. coli, and (F) The bacterial inhibition zone of each group of samples. (n = 3, ***p < 0.001).*
The effect of each group of samples on the turbidity of S. aureus and E. coli suspensions could be seen in Figure 2B. The suspensions around Ti, PEEK and SPEEK were turbid. In contrast, the suspensions around HSPEEK were relatively clear. Figure 2C showed that the OD600nm of bacterial suspension in HSPEEK group was significantly lower than that of Ti, PEEK and SPEEK groups ($p \leq 0.001$), while there was no significant difference among Ti, PEEK and SPEEK groups ($p \leq 0.05$).
As shown in Figure 2D, the bacterial colonies on Ti, PEEK, and SPEEK covered almost the entire solid medium, with several colonies observed in HSPEEK. Figure 2E showed that the bacterial viability rate of HSEEK was significantly lower than that of Ti, PEEK and SPEEK ($p \leq 0.001$), and the bacterial viability rate of the S. aureus and E. coli suspension cocultured with HSEEK was less than $1\%$. The bacterial viability rates of Ti, PEEK and SPEEK were close to $100\%$, and there was no significant difference among the three groups ($p \leq 0.05$), suggesting that Ti, PEEK, and SPEEK had no antibacterial activity against S. aureus and E. coli suspension, which was consistent with the turbid condition of the bacterial suspension.
The agar diffusion test showed a prominent inhibition zone around the HHC36 immobilized sample (Figure 2F). There was no bacterial growth in this area, indicating that HHC36 could be released from the porous structure and inhibited the growth of surrounding bacteria. In addition, there was no inhibition zone around Ti, PEEK, and SPEEK, indicating that they did not possess the ability to inhibit the growth of the surrounding bacteria.
Effects of samples in each group on E. coli and S. aureus biofilms as shown in Figures 3A,B, live bacteria were stained green fluorescence by N01, and dead bacteria were stained red fluorescence by PI. The surface of HSPEEK showed red fluorescence and no green fluorescence, which illustrated that its surface was almost covered by dead bacteria without living bacteria, implying that HSPEEK could prevent biofilm formation by killing bacteria. The surfaces of Ti, PEEK and SPEEK were almost all covered by viable bacteria with green fluorescence. At the same time, there was no red area, indicating that they did not have any antibacterial activity.
**Figure 3:** *(A) Live/dead staining image of S. aureus biofilm after 48 h coculture with Ti, PEEK, SPEEK and HSPEEK, (B) Live/dead staining image of E. coli biofilm after 48 h coculture with Ti, PEEK, SPEEK and HSPEEK, and (C) Metabolic activity of S. aureus and E. coli biofilms after 48 h coculture on the surface of samples from each group. (n = 3, **p < 0.01, ***p < 0.001).*
The results of MTT analysis were shown in Figure 3C. The biofilm metabolic activities of both S. aureus and E. coli in the HSPEEK group were significantly lower than those in the other groups ($p \leq 0.001$), indicating that HSPEEK could significantly reduce the metabolic activity of bacterial biofilms. In addition, it was found that the biofilm metabolic activity of the Ti surface was significantly lower than that of PEEK ($p \leq 0.05$).
## In vitro cytological experiments
The toxicity of the extracts of the samples to L929 cells was determined by live/dead staining. The results showed that a large number of living cells emitting green fluorescence could be seen in all groups, while it was difficult to find dead cells emitting red fluorescence (Figure 4A). The results showed that the extracts of Ti, PEEK, SPEEK, and HSPEEK had almost no cytotoxicity.
**Figure 4:** *(A) Live/dead staining images of L929 cells after coculture with Ti, PEEK, SPEEK and HSPEEK extracts for 24 h, (B) The proliferation of L929 cells after 1, 3 and 5 d of coculture with sample extracts from each group, (C) Photographs of rabbit blood supernatant after 1 h of incubation with samples from each group, and (D) Hemolytic rate of samples from each group. (n = 3, **p < 0.01, ***p < 0.001).*
The effect of each group on cell proliferation was confirmed by measurement of CCK8 assay, and the results was shown in Figure 4B. The cell proliferative viability of the Ti group was close to or slightly higher than that of the control group on 1 d and 3 d, and significantly higher than the control group on 5 d ($p \leq 0.01$), indicating that Ti could promote cell proliferation as time progresses. The cell proliferative activity of PEEK was always slightly higher than that of the control group at each time point, still no significant differences were observed ($p \leq 0.05$), suggesting that PEEK did not significantly promote cell proliferation. The cell proliferative activity of SPEEK was significantly higher on 3 d than the control group ($p \leq 0.01$), and this trend was more pronounced on 5 d ($p \leq 0.001$). This phenomenon may be related to the fact that 3D network structures can promote cell proliferation (Ouyang et al., 2016). Compared with the control group, the cell proliferative activity of HSPEEK was not significantly different at each time point ($p \leq 0.05$), proving that HSPEEK had no significant effect on cell proliferation.
In the hemolysis test, obvious hemolysis in the positive control group could be observed (Figure 4C). Ti, PEEK, SPEEK, HSPEEK and the negative control group had no macroscopic hemolysis, and the hemolytic rate was less than $5\%$ (Figure 4D), indicating that all samples had low hemolytic activity.
## Discussion
In this work, HHC36 was added to SPEEK by simple sulfonation technique, hydrothermal treatment and solvent volatilization method. The sulfonation process is a simple and controllable modification technology. In industry, sulfonation treatment of PEEK has been used to produce SPEEK membranes with excellent proton conductivity, and it is mainly used in fuel cells (Montero et al., 2017). Sulfonation treatment of PEEK can also produce desiccants with porous structures whose surface micropores contribute to the adsorption of moisture in humid air (Akhtar et al., 2021). Traditionally, sulfonation reactions require a long time at high temperature (Yee et al., 2013). However, Zhao et al. found that SPEEK surfaces modified by short-time sulfonation (5 min under ultrasonic stirring) have shown biocompatible (Zhao et al., 2013). The sulfonation reaction could form a 3D network structure on the surface of the polymer and serve as a drug loading platform to play a role in bone formation, anti-inflammation and anti-bacteria (Sun et al., 2018; Wang et al., 2019; Zhu et al., 2019; Wang et al., 2020; Wang X. J. et al., 2021; Yu et al., 2021a,b; Zheng et al., 2022). There have been many researchers sulfonating the surface of PEEK and utilizing its porous structure to load antimicrobial agents, endowing PEEK with antimicrobial properties (He et al., 2019; Yang et al., 2019, 2021; Yuan et al., 2019). In addition to the porous structure, the electrostatic attraction also contributed to the loading of HHC36 on the surface of SPEEK. HHC36 is a polypeptide with 5 positive charges, an isoelectric point of 12.31, and is highly positive at pH 7.4. Kazemzadeh-Narba et al. found that HHC36 could be loaded on calcium phosphate (CaP) coating with porous structures, and the positively charged residues of HHC36 and negatively charged phosphate groups of CaP had an electrostatic affinity, which to some extent conferred sustained release HHC36 from CaP coating (Kazemzadeh-Narbat et al., 2012). Ouyang et al. measured the Zeta potential of PEEK surface after sulfonation and hydrothermal reaction treatment and found that its surface was negatively charged (Ouyang et al., 2016). We speculate that similar electrostatic affinities exist between positively charged residues in HHC36 and negatively charged sulfonic acid groups on the surface of SPEEK, which may help explain why HHC36 was still released gently up to 10 d after an initial burst release. Based on the unique release profile of HSPEEK, HHC36 could be released rapidly early in the procedure to control infection and then slowly to prevent potential infection.
Bacterial infection is considered to be an important factor leading to implant failure. During orthopedic surgery, implants are susceptible to contamination by bacteria in the surrounding environment. Planktonic bacteria adhere to the surface of the implant and rapidly evolve into the biofilm. Once the biofilm on the surface of the implant matures, it will be difficult for the human immune system and external antibiotics to eliminate them, which will lead to implant failure (Alonso et al., 2020; Escobar et al., 2020; Jalil et al., 2020). Therefore, inhibiting biofilm formation by killing plankton bacteria is an effective measure to prevent bacterial infection. In vitro antibacterial tests showed that HSPEEK killed more than $99\%$ of the bacteria in suspensions of S. aureus and E. coli. The result was consistent with the clarity and OD600nm of the bacterial suspension surrounding the HSPEEK group. The death of bacterioplankton could be attributed to the effect of HHC36 on bacterial membrane destruction. In this work, FESEM results showed that HHC36 could effectively destroy the integrity of S. aureus and E. coli, which might cause the outflow of bacterial contents and death. Chen et al. monitored the binding of HHC36 to bacteria in real time using aggregation-induced emission probes. They observed the aggregation of HHC36 on bacterial membranes and the destruction of membrane structure, which resulted in the subsequent efflux of nucleic acids or protein from within the bacteria (Chen et al., 2018). Some scholars found that HHC36 has the ability to disrupt the integrity of bacterial cell membranes through FESEM (Chen et al., 2021). HHC36 has this non-specific antibacterial mechanism that destroys cell membranes so it may have advantages over traditional antibiotics against resistant bacteria such as MARS (Chen et al., 2020, 2021; Guo et al., 2022). Reducing the bacterial viability in the bacterial suspension may inhibit the formation of bacterial biofilm on the material surface. The results of bacterial live/dead staining and MTT assay supported this view. The HSPEEK surface is covered with dead bacteria and almost no live bacteria. MTT assay showed that HHC36 significantly reduced the metabolic activity of biofilm and the formation of biofilm. It could be noted that the biofilm metabolic activity of the Ti surface was significantly lower than that of PEEK and SPEEK, which might be related to the lower affinity of titanium for biofilm than that of PEEK (Gorth et al., 2012; Krätzig et al., 2021).
An ideal orthopedic implant should not only have excellent antibacterial capacity but also good cytocompatibility. Cell live/dead staining and CCK8 assay showed that HSPEEK extract was non-toxic to L929 cells and did not negatively affect cell proliferation. It also showed low hemolytic activity, which was not different from other groups, demonstrating the good cytocompatibility of HHC36. The good cytocompatibility of HSPEEK is related to the unique antibacterial mechanism of AMPs. Unlike bacterial cell membranes with large amounts of negatively charged phospholipids, mammalian cell membranes are only rich in electrically neutral zwitterionic phospholipids and cholesterol, which make AMPs less likely to bind to and cause damage to mammalian cells. HHC36 has been shown to have very low toxicity to mammalian cells (Kazemzadeh-Narbat et al., 2013; He et al., 2018; Miao et al., 2021).
In animal experiments, the bacterial viability rate of the HSPEEK surface was significantly lower than other groups, which was consistent with in vitro colony count results. However, there was little difference in bacterial viability between Ti and PEEK. The extent of the inflammatory response in rat skin was almost consistent with the bacterial survival results for each group of materials. Rochford et al. implanted PEEK and Ti into two strains of mice (C57BL/6 and BALB/C). They found that the number of bacteria on the surface of the two materials and the inflammatory response in the surrounding tissue did not show significant differences over time. The findings suggest that the choice of Ti and PEEK has little effect on the progression of infection once implant-associated infection occurs in vivo (Rochford et al., 2019). Interestingly, although not significant, the bacterial viability rate on the SPEEK surface was found to be greater than that of Ti and PEEK in this study. This phenomenon may be because SPEEK has a larger surface area, which is conducive to bacterial adhesion. S. aureus can escape from the immune system and mislead the immune system, causing serious inflammation (Zhang et al., 2022). H&E staining images showed dense infiltration of inflammatory cells in the soft tissues around Ti, PEEK, SPEEK and HSPEEK, consistent with the high number of S. aureus adhering to the surfaces of these samples. HSPEEK had significantly lower inflammatory cell density in the surrounding soft tissue than Ti, PEEK and SPEEK, indicating the good biocompatibility of HSPEEK.
## Conclusion
The AMP HHC36 was immobilized on the 3D porous structure of SPEEK by simple sulfonation technology, hydrothermal treatment and solvent volatilization method. The in vitro antibacterial results showed that HSPEEK exhibited excellent antibacterial properties against S. aureus and E. coli, killing plankton bacteria and inhibiting the formation of bacterial biofilm. In vitro cytological experiments results indicated that HSPEEK had good cytocompatibility. Moreover, HSPEEK exhibited excellent anti-infection performance and biocompatibility in the rat subcutaneous infection model, reducing not only the bacterial viability rate but also the inflammation. This study provided a new strategy for improving the antibacterial properties of PEEK, which has a broad application prospect in orthopedic implants. The limitation of this study is that the effect of HHC36 on osseointegration of implants has not been evaluated. This evaluation will be carried out in our further study. In addition, how to further improve the biocompatibility of HSPEEK by modification will also be the focus of our future research.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Jilin University.
## Author contributions
WG and JZ: conceptualization. XH: methodology. ZZ: software. XH and DS: validation. YL: formal analysis. XL: investigation. SY: resources. WG: data curation and writing—review and editing. DS: writing—original draft preparation. YQ and JJ: visualization. XH and JZ: supervision. JZ: project administration and funding acquisition. All authors contributed to the article and approved the submitted version.
## Funding
This research was funded by Project of Development Plan of Science and Technology of Jilin Province (no. 20200403094SF) and Natural Science Foundation of Jilin Province (no. YDZJ202201ZYTS078).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Relationship of blood heavy metals and osteoporosis among the middle-aged
and elderly adults: A secondary analysis from NHANES 2013 to 2014 and 2017 to 2018'
authors:
- Zengfa Huang
- Xiang Wang
- Hui Wang
- Shutong Zhang
- Xinyu Du
- Hui Wei
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043376
doi: 10.3389/fpubh.2023.1045020
license: CC BY 4.0
---
# Relationship of blood heavy metals and osteoporosis among the middle-aged and elderly adults: A secondary analysis from NHANES 2013 to 2014 and 2017 to 2018
## Abstract
### Objective
This study aimed to assess the relationship between blood heavy metals and a higher prevalence of osteoporosis in middle-aged and elderly US adults using the National Health and Nutritional Examination Surveys (NHANES).
### Methods
The secondary data analysis was performed using the data of NHANES 2013–2014 and 2017–2018. We used the information, including physical examination, laboratory tests, questionnaires, and interviews, provided by participants in NHANES. Logistic regression and weighted quantile sum (WQS) regression models were used to explore the relationships between levels of blood heavy metals and a higher prevalence of osteoporosis.
### Results
A total of 1,777 middle-aged and elderly participants were analyzed in this study, comprising 115 participants with osteoporosis and 1,662 without osteoporosis. Adjusted model 1 showed a significant positive relationship between cadmium (Cd) levels and a higher prevalence of osteoporosis (quartile 2, OR = 7.62; $95\%$ CI, 2.01–29.03; $$p \leq 0.003$$; quartile 3, OR = 12.38; $95\%$ CI, 3.88–39.60; $p \leq 0.001$; and quartile 4, OR = 15.64; $95\%$ CI, 3.22–76.08; $$p \leq 0.001$$). The fourth quartile of selenium (Se) level (OR = 0.34; $95\%$ CI, 0.14–0.39; $p \leq 0.001$) led to a lower prevalence of osteoporosis and exerted a protective effect on model 1. Other models produced similar results to those of model 1. A subgroup analysis showed that Cd levels were positively related to a higher prevalence of osteoporosis in all three models in women, while this relationship was not found in men. The fourth quartile of the Se level was related to a lower prevalence of osteoporosis in both male and female analyses. A significant positive relationship was found between the blood Cd level and a higher prevalence of osteoporosis in the non-smoking subgroup. Blood Se level showed a protective effect on the fourth quartile in both the smoking and non-smoking subgroups.
### Conclusion
Blood Cd level aggravated the prevalence of osteoporosis, while blood Se level could be a protective factor in osteoporosis among the US middle-aged and older populations.
## Introduction
Osteoporosis is a systemic metabolic disease and remains a global health problem, which is increasingly becoming common in both developing and developed countries [1]. It is estimated that there were 10.2 million cases of osteoporosis among the US population aged over 50 years in 2010 and that this number will reach 13.5 million by 2030 [2, 3]. Osteoporosis is characterized by a loss of bone mineral density (BMD), which leads to an increased risk of fragility fractures and thus an increased economic and medical burden on the patient [4]. As the definition of osteoporosis either by experts or by an explanation based on histology did not prove to be practical for patient care, a panel of the World Health Organization (WHO) defined osteoporosis as BMD values of 2.5 standard deviations (SDs) or more below the mean of the young adult reference group [5]. There are many risk factors for osteoporosis and BMD reduction, including older age and female gender [6]. In recent years, it has been hypothesized that heavy metals may be associated with the risk of degenerative diseases and fractures [7].
Heavy metals have been demonstrated to be associated with adverse health effects. Moreover, exposure to heavy metals in the environment will affect genes and increase disease susceptibility [8]. The accumulation of heavy metals in the human body will change hormone metabolism and lead to vasoconstriction, thus leading to adult diseases [7]. Accordingly, a recent study revealed that the accumulation of blood heavy metals in bones increases bone resorption and changes bone mineral content, which will eventually lead to osteoporosis and bone fracture [9]. Several studies indicated a negative correlation between daily or long-time exposure to cadmium (Cd), lead (Pb), mercury (Hg), and BMD [10]. However, no significant correlation between dietary intake of these heavy metals and bone parameters was observed [11].
The relationship between blood heavy metals and the risk of osteoporosis has only been reported in observational studies involving small sample sizes [12]. Moreover, several bodies of research determined the exposure to heavy metals based on the urinary or environmental levels of heavy metals (13–15). Nevertheless, it is unclear whether blood heavy metal levels are associated with osteoporosis in the general aging population. Therefore, this study aimed to assess the relationship of blood heavy metals with a higher prevalence of osteoporosis in a US population of middle-aged and elderly people using the National Health and Nutritional Examination Surveys (NHANES). Investigation of the correlation between blood heavy metals and osteoporosis is important as people may experience cumulative exposure in some circumstances and osteoporosis is a threat to the aging population. An analysis of the relationship between aging and osteoporosis could help prevent osteoporosis and reduce the exposure of the aging population to the risk factors.
## Study subjects
This study was performed as a secondary analysis using the data collected in NHANES 2013–2014 and 2017–2018, and the data were collected by physical examination, laboratory tests, questionnaires, and interviews. Details of the NHANES can be found on the website of the American Centers for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes). In the present study, we first enrolled all participants from NHANES 2013 to 2014 ($$n = 10$$,175) and 2017 to 2018 ($$n = 9$$,254). Then, we excluded participants with incomplete information on blood heavy metals ($$n = 7$$,330), BMD data ($$n = 8$$,347), and missing basic information as well as those aged below 40 years ($$n = 1$$,975). In the end, a total of 1,777 individuals were included in the final analysis (Figure 1).
**Figure 1:** *Flowchart of the study population selection.*
## Evaluation of osteoporosis
The BMD values at different sites (the total femur, the femur neck, the trochanter, and the trochanter intertrochanter) were measured using dual-energy x-ray absorptiometry (DXA) with Hologic QDR-4500A fan-beam bone densitometers (Hologic, Inc., Bedford, MA, USA). The regions of the proximal femur of the left hip were routinely examined. An examination of the left hip was replaced by the right hip on the condition that the participant reported having replacement or metal objects in the left leg. Any participant who was pregnant, who weighed over 300 pounds, or had a history of radiographic contrast material, fractures, replacements, or pins in both hips was excluded from the DXA examination.
Osteoporosis was defined as BMD values of 2.5 standard deviations (SDs) or more below the mean of the young adult reference group according to the guidelines of the World Health Organization (WHO) [5]. This study assessed osteoporosis in four regions of the femur: the total femur, the femur neck, the trochanter, and the intertrochanter, and the thresholds were 0.67 g/cm2, 0.56 g/cm2, 0.46 g/cm2, and 0.79 g/cm2, respectively [16]. Osteoporosis in any femoral region was defined as overall osteoporosis.
## Assessment of heavy metals
After performing the step involving a simple dilution sample preparation, blood heavy metals, such as Pb, Cd, total Hg, selenium (Se), and manganese (Mn), were directly measured in whole blood samples by mass spectrometry. To carry out a uniform distribution of cellular components, a small amount of whole blood was extracted from a larger sample of whole blood after mixing during the dilution phase. Dilution of blood includes simple dilution of 1 part sample + 1 part water + 48 parts diluent during sample preparation before analysis. Liquid samples were introduced into the mass spectrometer through the inductively coupled plasma ionization ion source [17].
## Ascertainment of covariates
Information on demography and lifestyle factors was collected by trained personnel, according to the statement mentioned on the NHANES website. Demographic characteristics included age (years), gender (male or female), race/ethnicity (Mexican, non-Hispanic white, non-Hispanic black, Mexican American, and other races), educational level (less than 9th grade, 9–11th grades, high school, some college, or college graduate), and physical activity (Yes and No). Alcohol consumption was defined as < 12 or ≥12 alcoholic drinks per year. Smoking status was categorized into never smokers or current smokers. Exposure to secondhand smoke was indicated as no one in the household is a smoker or ≥1 one member in the household is a smoker. Sedentary behavior was defined as sitting for more than 6 h a day, which does not include time spent sleeping. The medical examinations were carried out in mobile centers. Body mass index (BMI, kg/m2) was classified as < 25, 25–30, or >30. Diabetes was defined as reporting a previous diagnosis or reaching a fasting glucose level of ≥126 mg/dl. Hypertension status was defined as reporting a previous diagnosis (yes or no). Arthritis was defined as a doctor ever diagnosing one to have had arthritis. A thyroid problem was defined as a doctor ever diagnosing one to have had a thyroid problem. Hypercholesterolemia was defined as total cholesterol values ≥240 mg/dl. The estimated glomerular filtration rate (GFR) was calculated based on age, gender, and serum creatinine according to the Chronic Kidney Disease Epidemiology Collaboration equation [18]. The annual household income was classified as < $20,000, $20,000–$34,999, $34,999–$74,999, or ≥$75,000.
## Statistical analysis
All analyses considered complex survey design factors, including sample weights, clustering, and stratification, with instructions for using NHANES data. Four-year sampling weights were calculated by multiplying the sampling weights provided by NHANES for 2-year cycles by two. Data were expressed as mean ± standard derivation (SD) for continuous variables and numbers (percentages) for categorical variables. We used Student's t-test for continuous variables and the Chi-square test for categorical variables. As the blood heavy metals displayed non-normal distribution, categorical groups rather than continuous values were used in statistical analysis. The levels of blood heavy metals (Pb, Cd, Hg, Mn, and Se) were categorized into one of the four groups based on quartiles (quartile 1: < 25th percentile, quartile 2: 25th−50th percentile, quartile 3: 50th−75th percentile, and quartile 4: >75th percentile). Categorical groups and continuous analysis of blood heavy metals of logistic regressions with weights were used to estimate the odds ratio (ORs) with $95\%$ confidence intervals ($95\%$ CIs) for the relationships between blood heavy metals and the prevalence of osteoporosis. Model 1 was adjusted for gender, age, and race, and model 2 was further adjusted for all the covariates. Blood heavy metals were evaluated by using quartile 1 as the reference. We further used weighted quantile sum regression (WQS) models with positive and negative directionality modes for the mixed effects. A two-sided value of P of < 0.05 was considered statistically significant. All the analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R software version 4.2.2 (Vienna, Austria).
## Characteristics of participants
The present study included a total of 1,777 participants, involving 115 of them with osteoporosis and 1,662 without osteoporosis. The weighted average age was 58.9 ± 0.4 years and $50.4\%$ of them were men. Table 1 shows the basic characteristics of the study participants. Compared with the non-osteoporosis group, participants with osteoporosis were older and were more likely to be women. These participants also had a higher prevalence of normal BMI, arthritis, thyroid problems, a lower GFR, and a lower annual household income. There were no significant differences in race, education, smoking status, exposure to secondhand smoke, alcohol consumption, physical activity, sedentary behavior, hypercholesterolemia, or history of diabetes or hypertension between the osteoporosis group and the non-osteoporosis group (Table 1). Characteristics of participants based on the levels of heavy metals in their blood are listed in Supplementary Tables S1–S5. The weighted geometric mean (GM) and quartiles of concentrations of blood heavy metals are listed in Table 2.
## Relationships of blood heavy metals with osteoporosis
Tables 3, 4 show the relationships between levels of blood heavy metals and osteoporosis using univariate logistic regression and multivariate logistic regression, respectively. Cd levels had a positive relationship with osteoporosis. Moreover, there is a negative relationship between Se levels and osteoporosis. The adjusted model 1 (adjusted by age, gender, and race) showed a significant positive relationship between Cd levels and osteoporosis (quartile 2, OR = 7.62; $95\%$ CI, 2.01–29.03; $$p \leq 0.003$$; quartile 3, OR = 12.38; $95\%$ CI, 3.88–39.60; $p \leq 0.001$; and quartile 4, OR = 15.64; $95\%$ CI, 3.22–76.08; $$p \leq 0.001$$). In Se element analysis, taking the first quartile as a reference, the fourth quartile (OR = 0.34; $95\%$ CI, 0.14–0.39; $p \leq 0.001$) led to a lower prevalence of osteoporosis and exerted a protective effect on model 1. However, the second (OR = 0.52; $95\%$ CI, 0.27–1.02; $$p \leq 0.056$$) and third quartiles (OR = 0.46; $95\%$ CI, 0.21–1.03; $$p \leq 0.059$$) were associated with a numerically decreased prevalence of osteoporosis with borderline significance. The third quartile of the Mn level showed a borderline negative significant relationship with osteoporosis (OR = 0.47; $95\%$ CI, 0.22–0.99; $$p \leq 0.049$$) in model 1. Pb and Hg have no relationship with osteoporosis. Model 2 (adjusted by age, gender, race, education, BMI, arthritis, thyroid problems, GFR, and annual household income) and model 3 (adjusted by age, gender, race, education, smoke, diabetes, hypertension, physical activity, BMI, alcohol consumption, exposure to secondhand smoke, sedentary behavior, arthritis, thyroid problems, hypercholesterolemia, GFR, and annual household income) produced similar results to those of model 1 for the relationship between blood heavy metals and the prevalence of osteoporosis.
Blood heavy metals that make a major contribution to the whole relationship of the mixture (Pb, Cd, Hg, Se and Mn) were analyzed using the WQS models. The ranking of blood heavy metals was based on the probability of the maximum weight of blood heavy metals in the mixture. WQS with a positive directional mode showed that Cd was positively related to a higher prevalence of osteoporosis, while Se was negatively related to the higher prevalence of osteoporosis (Figure 2).
**Figure 2:** *Identification of blood heavy metals in the mixture using the WQS model. (A) positive WQS model; (B) negative WQS model.*
## Subgroup analysis
In the subgroup analysis stratified by gender, logistic regression analysis revealed that the third and fourth quartiles of the Se level were associated with a lower prevalence of osteoporosis (OR = 0.31; $95\%$ CI, 0.10–0.92; $$p \leq 0.035$$, OR = 0.16; $95\%$ CI, 0.04–0.63; $$p \leq 0.009$$, respectively) in model 3 in men. There was no relationship between Cd levels and the prevalence of osteoporosis in men (Supplementary Table S6). However, in women, Cd levels were shown to be positively related to a higher prevalence of osteoporosis (quartile 2, OR = 14.11; $95\%$ CI, 2.12–94.13; $$p \leq 0.006$$; quartile 3, OR = 30.55; $95\%$ CI, 5.90–158.11; $p \leq 0.001$; and quartile 4, OR = 27.00; $95\%$ CI, 3.34–218.29; $$p \leq 0.002$$) in model 3. The fourth quartile of the Se level was related to a lower prevalence of osteoporosis (OR = 0.27; $95\%$ CI, 0.14–0.53; $p \leq 0.001$) in model 3 in women (Supplementary Table S7). Model 1 and model 2 produced similar results to those of model 3 in both the men and women subgroup analyses. In the subgroup analysis stratified by smoking status, a significantly positive relationship was found between the blood Cd level and the prevalence of osteoporosis in the non-smoking subgroup, while no significant relationship was found in the smoking subgroup. Blood Se level showed a protective effect in the fourth quartile in both the smoking and non-smoking subgroups (Supplementary Tables S8, S9).
## Discussion
The present study explored the correlation between blood heavy metals and the higher prevalence of osteoporosis in a US population of middle-aged and elderly people. Based on the representative sample of the US population in NHANES (2013–2014 and 2017–2018), we found that Cd was independently associated with a higher prevalence of osteoporosis, while Se was independently associated with a lower prevalence of osteoporosis, and Pb, Hg, and Mn showed no statistically significant effect on the prevalence of osteoporosis.
Age, sex, and BMI are traditional risk factors for osteoporosis. The amount of bone in an individual peaks in young adulthood and one experiences subsequent loss with aging [19]. Women lose bone more rapidly due to the lack of estrogen with aging, while men experience a slow loss of bone [20]. Guidelines have recommended BMD screening for osteoporosis in women aged 65 years or older but clinical risk assessment tools for screening osteoporosis in younger women [21, 22]. In the present study, the average age of participants in the osteoporosis group was older than that of participants in the non-osteoporosis group, which was consistent with that mentioned in previous studies. Studies demonstrated that aging may cause interstitial inflammation and fibrosis in renal tubuli, which are closely related to the excretion of heavy metals [23]. A recent study revealed that the renal burden of Hg increases with age [24]. Another study in southwestern China showed that higher blood heavy metals were found in older individuals compared with younger adults [25]. A previous NHANES 2005–2006 study revealed a positive association between BMI and BMD [26], which was similar to the results of the present study. The NHANES 99-02 data showed that environmental exposure to Cd was negatively correlated with BMI [27]. Another NHANES study reported that blood Hg levels were inversely correlated with BMI for adults [28]. The present study showed similar results.
The relationship between blood Cd level and osteoporosis has been revealed in a small number of cross-sectional studies [29]. Moreover, a recent study reported that Cd exposure was associated with an up to $23\%$ increase in the incidence of osteoporosis, and the absolute cost of the burden of osteoporosis-related fractures caused by *Cd is* estimated to range between EUR€ 0.12 and 2.6 billion [30]. Furthermore, Chung revealed that blood Cd concentrations of >1.0 μg/L and >0.5 μg/L were independent risk factors for incident osteoporosis in 243 participants and in 121 women, respectively, from the 2001 to 2002 Korea Genome and Epidemiology Study [31]. The present study showed similar results. However, the sample size of the present study is relatively large and the research population is middle-aged and elderly populations, those who are more likely to have osteoporosis since it is a threat related to the aging population. A recent NHANES (2011–2018) study of young adults from 20 to 35 years revealed that blood Cd was independently negatively associated with lumbar BMD in women rather than men [32]. However, a few studies explored the relationship between blood Cd and osteoporosis in men. This positive relationship was found in women in the present study but not in men. Moreover, the smoking subgroup was first discussed, the results revealed that smoking did not affect the relationship between blood Cd and the prevalence of osteoporosis. Furthermore, a recent study involving 488 women showed no correlation between blood Cd and osteoporosis [33]. The potential mechanisms underlying the relationship between Cd and osteoporosis have been explored, including impairing the viability, proliferative ability, and osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs) through the NF-κB and P2X7-PI3K-AKT signaling pathways [14, 34]. Thus, the dysfunction of BMMSCs might be the main cause of Cd-related osteoporosis. In vitro studies showed that Cd stimulated osteoclastogenesis by increasing RANKL expression [35]. Moreover, recent studies suggested that Cd induces bone osteoblast apoptosis via ROS [36]. In addition, a further study demonstrated that Cd suppressed osteogenesis by inhibiting the Wnt/β-catenin pathway [37]. In vivo studies further demonstrated that Cd induces a decreased expression of Runx2 and matrix proteins such as ALP, OCN, and COL1a2 [38]. Another study found that Cd affected BMMSC differentiation by stimulating adipogenesis at the expense of osteoblastogenesis [39]. Furthermore, other potential mechanisms, including Cd-related NF-κB and P2X7-PI3K-AKT signaling pathways, have recently been demonstrated to impair the viability, proliferative ability, and osteogenic differentiation of BMMSCs [14, 34].
Low blood *Se status* has been demonstrated to be correlated with skeletal disease, especially female the prevalence of osteoporosis [40, 41]. A significant and positive relationship was observed between BMD and Se in a study involving 280 Spanish women [15]. Beukhof et al. demonstrated that *Se status* was positively associated with BMD in a cohort of 387 healthy aging European men [42]. In addition, some other studies revealed that Se was negatively associated with fractures induced by osteoporosis (43–45). Our findings were consistent with the findings of these studies. Furthermore, the present study also demonstrated that blood Se reduced the prevalence of osteoporosis in men. Moreover, this relationship was found in both the smoking group and the non-smoking subgroups. However, some previous studies did not suggest a relationship between Se and BMD in healthy women [46]. In addition, no relationship between Se and osteoporosis has been reported in either an Asian or a European population (47–49). These results might be observed due to the differences in sample characteristics and loss of power (91–290 subjects). The potential mechanism for this viewpoint has been demonstrated in vitro, with evidence suggesting that Se enhances the osteoblastic differentiation of BMMSCs by downregulating the differentiation and formation of mature osteoclasts [50]. Other in vitro studies have demonstrated that Se influences osteoblastic differentiation and subsequent bone resorption by regulating oxidative stress [51, 52]. Previous studies revealed that inadequate levels of Se may alter bone metabolism and delay bone growth. In vitro studies showed that Se had a positive effect on osteoblastic differentiation and subsequent bone resorption by regulating oxidative stress [53]. In addition, Wnt/LRP8/ApoER2 pathway was suggested as a fundamental intracellular Se transportation pathway for altering bone metabolism [54]. Animal studies also found that bone metabolism changed with Se deprivation. Such effects were related to a decrease in GPX1 activity, blood concentrations of calcium, plasma insulin-like growth factor, pituitary growth hormone, and an increase in blood 1,25-dihydroxyvitamin D3, parathyroid hormone, and urinary calcium concentration [52]. These changes were demonstrated to be associated with bone volume and BMD reduction, impairing bone microarchitecture [55].
The relationship between blood Pb and bone health has been reported in several epidemiological studies, but with inconsistent conclusions. A previous NHANES study (NHANES III) of adults aged ≥50 years showed that blood Pb was inversely correlated with BMD among white participants [56]. In contrast, a significant inverse relationship between Pb and osteoporosis has been reported in the Korea National Health and Nutrition Examination Survey (2008–2011) [7]. However, another previous study showed that blood Pb was not associated with BMD [57], which was consistent with that mentioned in the present study. In addition, we performed further analysis with gender and smoking subgroups and observed no relationship. A previous NHANES study (2005–2010) showed that a low blood Hg level was associated with an elevated risk of osteoporosis in young men (20–29 years) and women (30–39 years) [58]. However, this relationship between the middle-aged population and the elderly population remains unclear. Our study showed that blood Hg was not associated with an increased prevalence of osteoporosis in low or medium blood Hg levels. However, a high blood Hg level was found to show a positive relationship with a higher prevalence of osteoporosis in men but not in women. High blood Hg levels were found to be associated with reduced BMD in the femur neck in the Korean National Health and Nutrition Examination Survey (2008–2010) [59]. These inconsistent findings on the relationships of blood Hg with osteoporosis may be due to the heterogeneity between these studies. The relationship between blood Mn and osteoporosis remains unclear. A previous study of 91 elderly men showed no correlations between blood Mn level and BMD [47]. Another research of 304 retired workers revealed that a high Mn exposure level was correlated with a higher risk of osteoporosis [60]. No relationship was observed in the present study. This finding was similar to that of a previous study with a small sample size [61]. However, further subgroup analysis showed that a higher blood Mn level was positively associated with a higher prevalence of osteoporosis in men and non-smoking subjects. These inconsistent findings may have contributed to the different biological specimens and the variation in Mn exposure levels.
The present studies showed that elevated blood Pb, Hg, and Mn levels were not correlated with a higher prevalence of osteoporosis. However, some studies showed either a positive or a negative relationship between these heavy metals and BMD [11, 62]. The possible mechanism for the positive relationship may be attributed to oxidative stress-related toxicity in inhibiting the function of osteoblasts [63]. Thus, it remains controversial as to whether the contents of Pb, Hg, and Mn can directly influence BMD and affect the pathogenesis of osteoporosis. The positive relationship between Cd exposure and a higher prevalence of lower BMD were proven in both animal models and human-based studies. These biological mechanisms are complex and are not fully understood. Excessive Cd exposure will reduce the production of calcitriol, decompose the collagen matrix in the bone, interfere with the mineralization of bone cells, inhibit the activity of osteoblasts, and stimulate the activity of osteoclasts, thus damaging bone health [10]. The relationship between Se and bone health has been widely studied. As an essential component of selenoprotein, Se plays an important role in the maintenance of bone homeostasis through cell proliferation regulation and antioxidant protection [55]. Further studies are worth being conducted to determine the relationship between Se exposure and osteoporosis and to explore the underlying mechanism.
However, there are several limitations to the present study. First, the present study used a cross-sectional design, and no causal inference between blood heavy metals and the prevalence of osteoporosis can be made. Second, although demographic, medical history, and lifestyle variables have been adjusted using logistic regression in the present study, confounding variables may still exist and affect the correction between blood heavy metals and the prevalence of osteoporosis. In addition, other variables, such as diet and hypertriglyceridemia, were not included in this analysis. Third, blood heavy metals were measured only one time and this type of measurement might not reflect a continuous exposure, thus measurement errors were inevitable. Finally, the number of participants in the osteoporosis group was relatively small and other treatment variables (vitamin D and bisphosphate) were not included in the analysis; further larger sample studies are needed to confirm the results. However, our study also carries some strengths. First, the present study was based on a relatively large dataset from the US population. Second, DXA is more accurate for the diagnosis of osteoporosis, and, finally, we performed further subgroup analysis on the relationship between blood heavy metals and the prevalence of osteoporosis.
## Conclusion
In conclusion, our study demonstrated that blood Cd level aggravated the prevalence of osteoporosis, while blood Se level could be a protective factor for the prevalence of osteoporosis among the US middle-aged and older populations. However, the results need to be confirmed in a prospective study.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
## Ethics statement
The NHANES protocol is approved by the National Center for Health Statistics Institutional Review Board, and written informed consent is obtained. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
HWe and ZH: conception and design. XW: administrative support. HWa: provision of study materials or patients. XD and SZ: collection and assembly of data, data analysis, and interpretation. All authors wrote the manuscript and approved the final manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1045020/full#supplementary-material
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|
---
title: Demographic variables, anthropometric indices, sleep quality, Metabolic Equivalent
Task (MET), and developing diabetes in the southwest of Iran
authors:
- Seyed Ahmad Hosseini
- Samira Beiranvand
- Kourosh Zarea
- Kourosh Noemani
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043384
doi: 10.3389/fpubh.2023.1020112
license: CC BY 4.0
---
# Demographic variables, anthropometric indices, sleep quality, Metabolic Equivalent Task (MET), and developing diabetes in the southwest of Iran
## Abstract
### Propose
The present study has sought to investigate the prevalence of diabetes and its related risk factors, to examine the relationship between demographic variables, anthropometric indices, sleep quality, and Metabolic Equivalent Task (MET) with diabetes in Khuzestan province, southwest Iran.
### Methods
The present study has a cross-sectional design (the baseline data of the Hoveyzeh cohort study as a sub-branch of the Persian Prospective Cohort Study). Comprehensive information from 10,009 adults (aged 35–70 years) was collected from May 2016 to August 2018 through a multi-part general questionnaire containing general characteristics, marital status, education, smoking, sleep quality, MET, and anthropometric indices. Data analysis was performed by SPSS software version 19.
### Results
The mean age of the sample was 52.97 ± 8.99 years. $60.3\%$ of the population were women and $67.7\%$ were illiterate. Out of the 10,009 people surveyed, 1,733 stated that they have diabetes ($17\%$). In 1,711 patients ($17\%$) the amount of FBS was ≥126 mg/dl. There is a statistically significant relationship between diabetes and MET. More than $40\%$ had BMI above 30. Anthropometric indices in diabetic and non-diabetic individuals were different. Also, there was a statistically significant difference between the mean duration of sleep and the use of sleeping pills in diabetic and non-diabetic groups ($p \leq 0.05$). Based on logistic regression, marital status [OR = 1.69 ($95\%$ CI, 1.24, 2.30)], education level [OR = 1.49 ($95\%$ CI, 1.22, 1.83)], MET [OR = 2.30 ($95\%$ CI, 2.01, 2.63)], height [OR = 0.99 ($95\%$ CI, 0.98, 0.99)], weight [OR = 1.007 ($95\%$ CI, 1.006, 1.012)], wrist circumference [OR = 1.10 ($95\%$ CI, 1.06, 1.14)], waist circumference [OR = 1.03 ($95\%$ CI, 1.02, 1.03)], waist-to-hip ratio [OR = 3.41 ($95\%$ CI, 2.70, 4.29)], and BMI [OR = 2.55 ($95\%$ CI, 1.53, 4.25)], are good predictors for diabetes.
### Conclusion
The results of this study showed that the prevalence of diabetes in Hoveyzeh city, Khuzestan, Iran, was almost high. and emphasize that preventive interventions should focus on risk factors, especially socioeconomic status, and anthropometric indicators along with lifestyle.
## Background
Among the Eastern Mediterranean Region (EMR) countries, *Iran is* the second-most populous country after Pakistan and is expected to face a large increase in the prevalence of diabetes by 2030. According to statistics the prevalence of diabetes and related risk factors in Iran was $10.9\%$ [1]. Diabetes is considered a serious threat to global health, which is associated with a sedentary lifestyle and excessive calorie intake, and stress. The World Health Organization (WHO) has declared diabetes to be a latent epidemic [2]. Prevention at different levels can reduce the complications and incidence of this disease, especially at the initial stages. So, identifying and controlling its risk factors is important [3]. Ting et al. stated that recognizing the early markers for predicting diabetes and implementing related prevention strategies are among the critical steps to mitigate this problem worldwide [4].
Based on the literature, there is a strong association between demographic variables, lifestyle variables, clinical variables, and future diabetes. The risk of developing diabetes increases if there are some risk factors such as positive family history, old age, obesity, and lack of physical activity (4–6). It has also been shown that ethnic and social differences lead to different prevalences of diabetes [7]. Ramezankhani et al. stated that the prevalence of diabetes is related to some demographic and social factors such as gender, urbanization, level of education, marital status, and occupation [8]. Besides, Rahimian et al. reported that the prevalence of diabetes was only related to education level and not to gender and marital status [9].
On the other hand, one of the important and independent risk factors for diabetes is obesity, which is measured by various anthropometric indices such as waist circumference, hip circumference, and body mass [10, 11]. Some studies have also shown that four anthropometric indices of Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR), as obesity indicators which can be used to estimate the risk of diabetes in the future. It should be noted that the emphasis of studies on each of the above indicators in the forecasts has been different and some other indicators have been proposed along with these four main indicators (4, 12–15).
As a new risk factor, sleep disorders also play an important role in the development of diabetes through the neuro-metabolic pathway. Following sleep deprivation, cortisol levels rise and inhibit insulin production, and may lead to a diabetic or pre-diabetic condition in the long run. In addition, following sleep disorders, insulin sensitivity decreases and an increase in blood glucose levels leads to a decrease in the quality and quantity of sleep, which in turn leads to the progression of diabetes [16, 17]. The results of studies in this field also confirm the relationship between sleep quality and the incidence of diabetes. However, in this regard, there are differences in the results obtained from different populations [11, 18, 19].
Moreover, many studies have examined the relationship between physical activity and diabetes. In a systematic review and meta-analysis, Aune et al. reviewed 87 studies in this area and found that most studies indicated that there was a significant inverse relationship between different types of physical activity and the incidence of diabetes. In addition, the intensity of activity should be moderate to severe and every week (5–7 h per week). However, the reduction of accumulated fat in the body in this case plays a mediating role [20].
According to the recommendation of the International Diabetes Federation (IDF), one of the ways to reduce the impact of diabetes at the local, regional, and global levels is to improve the quality of epidemiological research on diabetes by strengthening surveys and regular monitoring systems [21]. Although the general risk factors used in disease risk calculations are fixed and reliable in different populations, they are not ideal in different ethnicities and diseases such as diabetes due to the presence of some specific or different risk factors [22]. Besides, in a situation where up-to-date data from a national information system are not yet available, population-based studies must be conducted periodically to obtain new information on the country's healthcare needs [23]. In this regard, prospective cohort studies can be an ideal choice for examining multiple outcomes, different and simultaneous risk factors for non-communicable diseases, examining the relationships between them, and designing preventive strategies by policymakers [24, 25]. Therefore, considering the lack of comprehensive and up-to-date information on the prevalence of diabetes and related risk factors in Khuzestan province, the present study has sought to investigate the prevalence of diabetes and its related risk factors, to examine the relationship between demographic variables, anthropometric indices, sleep, and physical activity with diabetes in Khuzestan province.
## Method
The present study was a cross-sectional study that was performed to determine the risk factors for diabetes in Khuzestan, southwest Iran. This study is a part of the Hoveyzeh Cohort Study (HCS) which is also a sub-branch of the Persian cohort study [26]. Comprehensive information on the design and baseline characteristics of the HCS was published in 2020 [22]. In this population-based study, 10,009 adults (aged 35–70 years) were recruited from May 2016 to August 2018. The eligible individuals were included from both sexes. They lived in urban and rural areas. Those who were unable to communicate or respond, and individuals with mental disorders, intellectual disabilities, and any psychiatric illness (e.g., psychosis) in an acute stage who were under medical treatment, were excluded from the study. Informed consent was obtained from all participants, and they were enrolled using their national ID cards. According to a standard protocol [22], blood and urine samples from subjects were taken by trained laboratory staff, and immediately after that, anthropometric data (weight, height, waist, hip, and wrist circumferences, and blood pressure) were also measured. BMI and WHR were calculated based on anthropometric data. In the third stage, general data were collected through interviews with individuals or, if necessary, with their relatives. This questionnaire included general demographics (gender, age, marital status, and education), 2 questions about alcohol and tobacco use, 5 questions about sleep (amount of sleep, night work, use of sleeping pills), and one question with 28 items about MET (duration and intensity of physical activity, daily activities, exercise). Follow-ups began 1 year after the first day of enrollment and data entry and they continued annually by telephone. Data analysis was performed by SPSS software version 19. Data were presented as (mean ± standard deviation) and frequency (%) for quantitative and qualitative variables, respectively. An independent t-test was used to compare the values of variables between men and women. Pearson correlation coefficient was used to determine the correlation between each of the anthropometric indices and risk factors for diabetes. $P \leq 0.05$ were considered statistically significant. The individuals with incomplete data and those who did not take part in different stages of follow-up were excluded from the analysis. The study was approved by the Ethics Committee of Ahvaz University of Medical Sciences.
## Results
The mean age of the diabetic subjects was 52.97 ± 8 8.99 years. More than $60\%$ of the population were women ($60.3\%$) and illiterate ($67.7\%$). Out of the 10,009 people surveyed, 1,733 people stated that they have diabetes ($17.3\%$). It was also found that in 1,711 patients ($17\%$) the amount of FBS was ≥126 mg/dl. Two thousand three hundred and eleven of the subjects ($20.94\%$) had FBS = 100–125 mg/dl and were classified as pre-diabetic. A total of 2,226 people (based on self-report and FBS check) were diagnosed with diabetes. The prevalence of diabetes in the studied population was $22.2\%$. In addition, the results of the Chi-square test showed that there is a statistically significant relationship between diabetes and MET. Other demographic and lifestyle information is provided in Table 1.
**Table 1**
| Variables | Variables.1 | Variables.2 | Frequency | Percent | P-value* |
| --- | --- | --- | --- | --- | --- |
| Gender | Female | Female | 1343.0 | 60.3 | 0.54 |
| Gender | Male | Male | 883.0 | 39.7 | 0.54 |
| Age (Mean ± SD) | Diabetes | Diabetes | 52.97 | 8.99 | 0.0 |
| Age (Mean ± SD) | Non-diabetes | Non-diabetes | 47.72 | 8.99 | 0.0 |
| Marital status | Single | Single | 48.0 | 2.2 | 0.0 |
| Marital status | Married | Married | 1893.0 | 85.0 | 0.0 |
| Marital status | Widow | Widow | 253.0 | 11.4 | 0.0 |
| Marital status | Divorced | Divorced | 32.0 | 1.4 | 0.0 |
| Education status | Illiterate | Illiterate | 1507.0 | 67.7 | 0.0 |
| Education status | Primary school | Primary school | 327.0 | 14.7 | 0.0 |
| Education status | Secondary school | Secondary school | 113.0 | 5.1 | 0.0 |
| Education status | High school diploma | High school diploma | 152.0 | 6.8 | 0.0 |
| Education status | University | University | 127.0 | 5.7 | 0.0 |
| Smoking | User | User | 492.0 | 22.1 | 0.137 |
| Smoking | Non-user | Non-user | 1734.0 | 77.9 | 0.137 |
| Metabolic equivalent task (MET) | 1st quarter | Diabetes | 784.0 | 31.3 | 0.0 |
| Metabolic equivalent task (MET) | 1st quarter | Non-diabetes | 1719.0 | 68.7 | 0.0 |
| Metabolic equivalent task (MET) | 2nd quarter | Diabetes | 557.0 | 22.2 | 0.0 |
| Metabolic equivalent task (MET) | 2nd quarter | Non-diabetes | 1948.0 | 77.8 | 0.0 |
| Metabolic equivalent task (MET) | 3rd quarter | Diabetes | 473.0 | 18.9 | 0.0 |
| Metabolic equivalent task (MET) | 3rd quarter | Non-diabetes | 2035.0 | 81.1 | 0.0 |
| Metabolic equivalent task (MET) | 4th quarter | Diabetes | 412.0 | 16.5 | 0.0 |
| Metabolic equivalent task (MET) | 4th quarter | Non-diabetes | 2081.0 | 83.5 | 0.0 |
More than $17\%$ of people had a normal BMI (18.5–24.9) and $43.4\%$ had BMI above 30. The results of the independent t-test showed that there is a significant difference between the mean of anthropometric indices (weight, waist circumference, wrist circumference, and height) in diabetic and non-diabetic individuals. Information related to anthropometric variables is given in Table 2.
**Table 2**
| Anthropometric indicators | Anthropometric indicators.1 | Mean | SD | P-value |
| --- | --- | --- | --- | --- |
| Weight (Kg) | Diabetes | 79.68 | 15.41 | 0.0 |
| Weight (Kg) | Non-diabetes | 77.59 | 15.58 | 0.0 |
| Height (cm) | Diabetes | 164.06 | 8.94 | 0.003 |
| Height (cm) | Nondiabetes | 164.7 | 9.16 | 0.003 |
| Hip Circumference | Diabetes | 104.1 | 10.28 | 0.816 |
| Hip Circumference | Non-diabetes | 104.04 | 9.59 | 0.816 |
| Wrist Circumference | Diabetes | 17.59 | 1.35 | 0.0 |
| Wrist Circumference | Nondiabetes | 17.42 | 1.33 | 0.0 |
| Waist circumference | Diabetes | 103.05 | 11.62 | 0.0 |
| Waist circumference | Non-diabetes | 98.68 | 12.05 | 0.0 |
| Waist to hip ratio (WHR) | Diabetes | 0.99 | 0.063 | 0.0 |
| Waist to hip ratio (WHR) | Non-diabetes | 0.94 | 0.064 | 0.0 |
| Body mass index (BMI) | Underweight ( ≤ 18.5) | 0.8 | 17.0 | 0.0 |
| Body mass index (BMI) | Normal (18.5–24.9) | 17.4 | 384.0 | 0.0 |
| Body mass index (BMI) | Overweight (25–25.9) | 38.6 | 859.0 | 0.0 |
| Body mass index (BMI) | Obese (≥30) | 43.4 | 966.0 | 0.0 |
The mean duration of sleep in diabetic patients was 7.69 ± 1.60 and $31.5\%$ of people used sleeping pills. The results of the independent t-test and chi-square test showed that there was a statistically significant difference between the mean duration of sleep and the use of sleeping pills in diabetic and non-diabetic groups ($p \leq 0.05$). Other information about sleep is given in Table 3.
**Table 3**
| Variables | Variables.1 | Variables.2 | Frequency | Percent | P-value |
| --- | --- | --- | --- | --- | --- |
| Leg restlessness | Yes | Diabetes | 391.0 | 24.9 | 0.01 |
| Leg restlessness | Yes | Non-diabetes | 1178.0 | 75.1 | 0.01 |
| Leg restlessness | No | Diabetes | 1568.0 | 21.5 | 0.01 |
| Leg restlessness | No | Non-diabetes | 5717.0 | 78.5 | 0.01 |
| Leg restlessness | Don't Know | Diabetes | 267.0 | 23.1 | 0.01 |
| Leg restlessness | Don't Know | Non-diabetes | 888.0 | 76.9 | 0.01 |
| Sleeping pills use | Non-user | User | 2144.0 | 22.0 | 0.0 |
| Sleeping pills use | Non-user | Non-user | 7605.0 | 78.0 | 0.0 |
| Sleeping pills use | User | User | 82.0 | 31.5 | 0.0 |
| Sleeping pills use | User | Non-user | 178.0 | 68.5 | 0.0 |
| Sleep duration (Mean ± SD) | Diabetes | Diabetes | 7.61 | 1.53 | 0.021 |
| Sleep duration (Mean ± SD) | Non-diabetes | Non-diabetes | 7.69 | 1.6 | 0.021 |
A logistic regression test was used to determine the important predictors of diabetes. According to the regression coefficients, there exists a significant relationship between diabetes and marital status [OR = 1.69 ($95\%$ CI, 1.24, 2.30)], an education level [OR = 1.49 ($95\%$ CI, 1.22, 1.83)], MET [OR = 2.30 ($95\%$ CI, 2.01, 2.63)], height [OR = 0.99 ($95\%$ CI, 0.98, 0.99)], weight [OR = 1.007 ($95\%$ CI, 1.006, 1.012)], wrist circumference [OR = 1.10 ($95\%$ CI, 1.06, 1.14)], waist circumference [OR = 1.03 ($95\%$ CI, 1.02, 1.03)], waist-to-hip ratio [OR = 3.41 ($95\%$ CI, 2.70, 4.2)], BMI [OR = 2.55 ($95\%$ CI, 1.53, 4.25)], are good predictors for diabetes. Considering that the significant values mentioned for these variables are < 0.05, it can be said that these variables are a good predictor for the dependent variable of diabetes (Table 4).
**Table 4**
| Parameter | Crude odds ratio | SE | 95% CI for OR | P |
| --- | --- | --- | --- | --- |
| Gender | Gender | Gender | Gender | Gender |
| Male | Ref | - | - | - |
| Female | 1.030 | 0.049 | [9.36, 1.13] | 0.544 |
| Marital status | Marital status | Marital status | Marital status | Marital status |
| Single | Ref | - | - | - |
| Married | 1.694 | 0.158 | [1.24, 2.30] | 0.001 |
| Widow | 3.213 | 0.174 | [2.28, 4.51] | 0.000 |
| Divorced | 1.436 | 0.251 | [0.87, 2.34] | 0.149 |
| Educational status | Educational status | Educational status | Educational status | Educational status |
| Academic | Ref | - | - | - |
| Illiterate | 1.499 | 0.102 | [1.227, 1.831] | 0.000 |
| Primary school | 1.143 | 0.116 | [0.911, 1.434] | 0.247 |
| Secondary school | 0.944 | 0.142 | [0.714, 1.247] | 0.684 |
| High school diploma | 1.207 | 0.134 | [0.929, 1.568] | 0.159 |
| Smoking | Smoking | Smoking | Smoking | Smoking |
| Non-user | Ref | - | - | - |
| User | 1.090 | 0.058 | [0.973, 1.222] | 0.137 |
| BMI | BMI | BMI | BMI | BMI |
| Underweight ( ≤ 18.5) | Ref | - | - | - |
| Normal (18.5–24.9) | 1.604 | 0.264 | [0.957, 2.689] | 0.073 |
| Overweight (25–25.9) | 2.338 | 0.261 | [1.403, 3.896] | 0.001 |
| Obese (≥30) | 2.552 | 0.260 | [1.532, 4.251] | 0.000 |
| MET | MET | MET | MET | MET |
| 4th quarter | Ref | - | - | - |
| 1st quarter | 2.304 | 0.069 | [2.012, 2.637] | 0.000 |
| 2nd quarter | 1.444 | 0.072 | [1.254, 1.664] | 0.000 |
| 3rd quarter | 1.174 | 0.074 | [1.015, 1.358] | 0.031 |
| WHR | WHR | WHR | WHR | WHR |
| Normal | Ref | - | - | - |
| Abnormal | 3.410 | 0.118 | [2.706, 4.297] | 0.000 |
| Sleep pill use | Sleep pill use | Sleep pill use | Sleep pill use | Sleep pill use |
| Non-user | Ref | - | - | - |
| User | 1.634 | 0.136 | [1.252, 2.132] | 0.000 |
| Leg restlessness | Leg restlessness | Leg restlessness | Leg restlessness | Leg restlessness |
| Don't know | Ref | - | - | - |
| Yes | 1.104 | 0.091 | [0.924, 1.319] | 0.277 |
| No | 0.912 | 0.075 | [0.787, 1.057] | 0.223 |
| Age real | 1.062 | 0.003 | [1.05, 1.06] | 0.000 |
| Weight (kg) | 1.009 | 0.002 | [1.006, 1.012] | 0.000 |
| Waist circumference | 1.031 | 0.002 | [1.026, 1.035] | 0.000 |
| Hip circumference | 1.001 | 0.002 | [0.996, 1.005] | 0.812 |
| Wrist circumference | 1.101 | 0.018 | [1.064, 1.140] | 0.000 |
| Height (cm) | 0.992 | 0.003 | [0.987, 0.997] | 0.003 |
| Sleep duration | 1.036 | 0.016 | [1.005, 1.068] | 0.021 |
## Discussion
Based on our findings, the percentage of diabetes in the population aged 35–70 years in Hoveyzeh city, Khuzestan, Iran, was quite above $20\%$. Our results were lower than those reported in some previous studies [5, 27] and higher than in some other studies [3, 28]. Considering the smaller geographical scale and the smaller target population, our results are significant, indicating the need for proper planning, early interventions, and lifestyle change. These differences can be attributed to exposure to risk factors for diabetes, urbanization, poor diet, obesity, and genetic structure of individuals [29, 30].
Our results also showed that some demographic variables are associated with the risk of diabetes, so with increasing age, the chance of diabetes increases 1.062 times. In this regard, Akbarzadeh et al. reported that every 5 years of aging, the risk of diabetes increases by $18\%$ [28]. Kivimaki et al. reported that people aged 55–59 were almost twice as likely to develop diabetes as those aged 35–39 [31]. Based on the results of previous studies, aging is associated with chronic inflammatory processes, disturbances in lipid metabolism, increased concentration of free fatty acids in blood/plasma, and increased fat accumulation in the body, therefore the risk of metabolic syndrome and type 2 diabetes increases [32, 33] and this finding was expected.
We found that the chances of diabetes in married, widowed, and divorced people compared to single people are 1.6, 3.2, and 1.4, respectively. Consistent with these results, Oraii et al. stated widows/divorced adults were at high risk for diabetes [6]. Also, De Oliveira et al. showed marital status as a predictor of type 2 diabetes mellitus incidence [34]. On the contrary, Mirzaei et al. said there was no significant relationship between marital status with undiagnosed or control of DM in patients [3]. It seems in our context, the effective factors for this association should be further characterized. So, they may provide significant information in the better design and implementation of preventive programs.
Regarding the level of education, the present study's findings showed that illiterate people are at higher risk of diabetes, which was in line with the results of previous studies [27, 28, 35]. Also, Hariri et al. pointed out that the level of education, mainly higher education, is a protective factor against diabetes and pre-diabetes [36]. In contrast, some studies show that socioeconomic status, as measured by levels of education, occupation, or income, either they are not related [3, 6] or often are inversely related to diabetes (37–39). They argued that better-educated and wealthier people were more likely to become obese and thus more likely to develop diabetes. However, Oraii et al. reported higher levels of education were a determinant of better glycemic control among treated diabetic participants. Also, Najafipour et al. stated illiterate people had worse uncontrolled Diabetes Mellitus [5, 6]. Considering that the population of illiterate people is more in our context, it seems that we should pay special attention to this variable in the planning and implementation of future interventions to prevent the occurrence of diabetes and its early diagnosis.
According to our results, there is no significant relationship between smoking and diabetes. In this regard, some studies also showed no association between ex-smokers and type 2 diabetes [40, 41]. While, in another study, the risk of type 2 diabetes was significantly higher in active/inactive pre-diabetic smokers compared to active/inactive non-diabetic smokers [42]. However, This discrepancy in the results may be due to the heterogeneous characteristics (sample size, age range, male-to-female ratio, and ethnicity) of the groups used in these studies. Therefore, the effect of smoking and opium consumption on diabetes in the Iranian population needs to be further investigated.
In this study, with increasing weight, the chance of developing diabetes increases by 1.009 times. On the other hand, the incidence of diabetes in tall people is reduced by 0.8 times compared to short people. The risk of developing diabetes at a BMI > 30 is about 2.5 times higher. In addition, the growth of wrist and waist circumference also increases the chances of developing diabetes. Also, people with an abnormal waist-to-hip ratio were 3.410 times more likely to develop diabetes. Almost most previous studies have confirmed such relationships [13, 15]. Jayedi et al. based on a systematic review and dose-response meta-analysis of 216 cohort studies investigated anthropometric and adiposity indicators and the risk of type 2 diabetes. They reported that almost in all regions and ethnicities, BMI had a strong positive linear association with the risk of type 2 diabetes. Also, they found a larger waist circumference was strongly and linearly associated with a higher risk of type 2 diabetes. But they noted that there was an inverse association among hip circumference, waist circumference, and risk of type 2 diabetes for studies that controlled all of them. In contrast, they reported a positive association between hip circumference and the risk of type 2 diabetes for studies that did not control waist circumference [14]. Future research should evaluate the association between body fat content in specific regions and the risk of diabetes risk in more detail.
Based on our results, increased sleep duration and a history of consuming sleeping pills were also associated with diabetes. Of course, based on the baseline data, the direction of this relationship is not clear, and it will be determined after the analysis of the follow-up data. There is strong evidence that diabetes causes sleep disturbances, but on the opposite side, the results of some previous studies showed that longer sleep duration is associated with an increased prevalence of DM and IGT [17, 43, 44]. Although, based on a systematic review and meta-analysis study, it was found that short and long-term sleep duration contributed to the development of type 2 diabetes [43]. Restriction of sleep reduces insulin sensitivity the next day by increasing cortisol levels in the nighttime [45, 46]. In contrast, some infection markers such as IL-6, and C-reactive protein increase in people who report prolonged sleep, which accelerates the progression of diabetes and its complications [47, 48]. Therefore, based on the results we obtained; it can be considered sleep duration/quality as a risk marker in monitoring diabetes. So more studies are needed to examine the definitive relationship.
In this study, a statistically significant relationship was also observed between physical activity and the incidence of diabetes. In such a way increasing the amount of physical activity reduces the chance of diabetes. Consistent with this result, Akbarzadeh et al. reported low levels of physical activity, in the modified model, were associated with a $38\%$ increase in the risk of developing diabetes [28]. Also, it is supported by other previous literature [20, 49]. The results of a systematic review study show that inactivity leads to type 2 diabetes through decreased insulin sensitivity, gradual loss of beta cells, impaired glucose tolerance, and ultimately obesity [50]. Physical activity has a protective effect on both people at risk for diabetes and people with diabetes [51]. Although little is known about the type, intensity, and duration of beneficial physical activity in the region of our study, therefore, further studies should be conducted on the relationship between physical activity, its related factors, and the risk of developing diabetes.
## Research strengths and limitations
The large number, relative diversity, and heterogeneity of the study population are among the strengths of this study. As another advantage of the present work, DM has been defined based on FBS above 126 in addition to the use of anti-DM drugs. Through the PCS basic data analysis, there was an opportunity to examine the confounding effects of many potential confounders measured in highly qualified equipment and study environment.
The main limitation of the present study is its cross-sectional nature. Considering that most study participants were female and were limited to people over the age of 40, the results may be generalizable to a similar population.
## Conclusion
The results of this study showed that the prevalence of diabetes in Hoveyzeh city, Khuzestan, Iran, was almost high, and public health interventions and integrated management plans for earlier diagnosis, treatment, and better control of diabetes in this region are required. The important predictors of diabetes were increasing age, marital status, education level, obesity, increasing waist circumference, increasing BMI, and physical activity. Modifiable correlations of diabetes, including low education level, obesity, physical activity, and sleep quality emphasize the importance of public life improvement and effective preventive interventions, which should also be targeted at very young age groups. Due to the growing trend of elderly in Iran, paying attention to, caring for, and controlling diabetes since the third decade of life and health education for a healthy lifestyle are strongly recommended.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Research and Ethics Council of Ahvaz University of Medical Sciences (code: HCS-9902). The participants provided their written informed consent to participate in this study. No animal studies are presented in the manuscript.
## Author contributions
SH, SB, KZ, and KN were involved in designing this research. SH and KN collected the data. SB analyzed the data. KZ and SB were involved in the data interpretation and prepare the draft of the manuscript. KZ was responsible for writing and finalizing the manuscript. All authors read and approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Amount and composition of total fatty acids in red and yellow bone marrow are
altered with changes in bone mineral density
authors:
- Sabrina Ehnert
- Anna J. Schreiner
- Claudine Seeliger
- Josef Ecker
- Fabian Springer
- Gerhard Liebisch
- Philipp Hemmann
- Tina Histing
- Andreas K. Nussler
journal: EXCLI Journal
year: 2023
pmcid: PMC10043387
doi: 10.17179/excli2023-5843
license: CC BY 4.0
---
# Amount and composition of total fatty acids in red and yellow bone marrow are altered with changes in bone mineral density
## Abstract
There is general consent that with decreasing bone mineral density the amount of marrow adipose tissue increases. While image-based techniques, claim an increase in saturated fatty acids responsible for this effect, this study shows an increase in both saturated and unsaturated fatty acids in the bone marrow. Using fatty acid methyl ester gas chromatography-mass spectrometry, characteristic fatty acid patterns for patients with normal BMD ($$n = 9$$), osteopenia ($$n = 12$$), and osteoporosis ($$n = 9$$) have been identified, which differ between plasma, red bone marrow and yellow bone marrow. Selected fatty acids, e.g. FA10:0, FA14:1, or FA16:1 n-7 in the bone marrow or FA18:0, FA18:1 n-9, FA18:1 n-7, FA20:0, FA20:1 n-9, or FA20:3 n-6 in the plasma, correlated with osteoclast activity, suggesting a possible mechanism how these fatty acids may interfere with BMD. Although several fatty acids correlated well with the osteoclast activity and BMD, there was not a single fatty acid contained in our fatty acid profile that can be claimed for controlling BMD, a fact that may be attributed to the genetic heterogeneity of the patients.
## Introduction
There is global evidence that life span and overall health improves (Tuljapurkar et al., 2000[30]). However, the steadily aging population and increasingly sedentary lifestyles have made low bone mass, i.e. osteopenia and osteoporosis, and associated fragility-fractures a serious public health concern. Despite a minor trauma, fragility-fractures frequently result in work absence, decreased productivity, disability, deterioration of health, and reduced quality of life.
Directly after birth, the bone marrow is comprised mainly of red bone marrow (RBM), which contains its red color from large amounts of hematopoietic, osteogenic, and erythroid cells. With increasing age, the bone marrow adipose tissue (MAT) steadily expands, resulting in the development of yellow bone marrow (YBM) (Moerman et al., 2004[22]). Overall this development is comparable between mice and humans, however, in mice significant strain differences exist. C57BL/6 mice, which have an overall low BMD, have very low numbers of marrow adipocytes in their long bones. In contrast, the long bones of C3H/HeJ mice (a distinct strain), which have a very high BMD, contain a lot more marrow adipocytes (Beamer et al., 1996[1]). This challenges a causal relationship between simply the amount of MAT and the BMD. Instead, factors, e.g. fatty acid composition and genetic variability in lipid metabolism get into focus. Picking up the previous example, C3H/HeJ mice have been shown to have higher levels of triglycerides and cholesterol in their plasma than C57BL/6 mice (Jiao et al., 1990[14]), suggesting that not only the fatty acid composition could be relevant, but also that the fatty acid composition in the plasma and the MAT correlate.
However, one of the unsolved key questions about MAT and bone is whether accumulation of MAT precedes, parallels, or follows bone loss. MAT comprises a large proportion of the marrow cavity in primary and secondary osteoporosis (Cohen et al., 2012[4]; Devlin and Rosen, 2015[6]). A study using proton magnetic resonance spectroscopy proposed that saturated lipids increase preferentially to unsaturated lipids in marrow fat of patients with decreased BMD (Yeung et al., 2005[31]). Therefore, it was proposed, that nutritional modification of the fatty acid composition may improve the BMD (Martyniak et al., 2021[20]). While the protective role of omega-3 poly-unsaturated fatty acids (PUFA) in pathological calcifications, e.g. atherosclerosis, is indisputable, their effects on BMD is less clear. Although a diet rich in omega-3 PUFA could improve BMD in different rodent models, comparable studies in humans showed contradictory results - for review see (Sharma and Mandal, 2020[28]). More detailed analyses of the fatty acid composition in the bone marrow using gas chromatography (Griffith et al., 2009[10]; Miranda et al., 2016[21]), challenged the initial assumption that especially saturated fatty acids (SAT) are increased in the bone marrow of osteoporotic patients (Yeung et al., 2005[31]).
Based on the mentioned literature it is assumed that: (i) with decreasing BMD, the overall amount of fatty acids increases in the bone, (ii) the fatty acid composition in osteoporotic bone is altered, (iii) the fatty acid composition of the well-perfused red bone marrow correlates with the fatty acid composition of both the plasma and yellow bone marrow, (iv) and these alterations in the fatty acid composition in the plasma and bone marrow can be used to screen for osteopenia and osteoporosis.
To investigate these hypotheses, it is planned to characterize the fatty acid composition in the circulating blood (plasma - prior to surgery), the well-perfused red bone marrow (RBM), and the yellow bone marrow (YBM). As the literature suggests that the donor site might affect the fatty acid composition (Griffith et al., 2009[10]), only explanted femur heads will be used to obtain the RBM and YBM. The total fatty acid composition (12 SAT, 7 MUFA, and 11 PUFA) in the plasma, RBM and YBM will be quantified using gas chromatography-coupled to mass spectrometry (GC-MS) and associated to the age-adjusted BMD (T-scores), and circulating bone markers to identify disease-specific pattern.
## Ethics statement
The study includes patient material and was performed in accordance with the Declaration of Helsinki [1964] in its latest amendment. Clinical data, blood, and bone-marrow samples from a total of 30 patients receiving a planned primary total hip arthroplasty were obtained in accordance with the ethical vote $\frac{228}{2017}$BO2 (approved: 24.05.2017). All study participants have signed written informed consent. Patients below 18 years of age, not capable of consent, or with viral or bacterial infection, were excluded from the study.
## Red and yellow bone marrow
Yellow and red bone marrow were obtained from a total of 30 patients undergoing a total hip arthroplasty at a level 1 trauma center, immediately after resection of the femoral heads. Samples were stored at -80 °C until further use. The associated age-adjusted BMD (T-scores) were determined by quantitative computer tomography (qCT) scans with a reference block (Phantom EFP-06-96).
## Blood sampling
From the same patients 3.7 mL blood (EDTA plasma) was obtained during a routine blood sampling before the surgery. Blood samples were centrifuged at 1,000 g for 10 min at room temperature 30 min after sampling, to allow clotting. Serum samples were stored (aliquots) at -80 °C until further use.
## Enzyme-linked Immunosorbent Assay (ELISA)
Target proteins in plasma samples were quantified with the help of ELISA kits, performed as indicated by the manufacturers: Bone alkaline phosphatase (Ostase® BAP / AC-57DF1, IDS, Tyne & Wear, UK) and tartrate-resistant acidic phosphatase (TRAP5b / SB-TR201A, IDS) were detected as activity markers for osteoblast and osteoclast, respectively. Cross-linked C-telopeptides of Type 1 collagen (CICP / 1:12.5 sample dilution / #8003 / TecoMedical, Sissach, CHE) and C-terminal telopeptide of fibrillar collagens (CTX-I / 1:3 sample dilution / AC-57SF1, IDS) were detected as markers for collagen formation and degradation. Furthermore, the fat-soluble 25(OH) vitamin D3 (25(OH)D3 or calcidiol / AC-57DF1, IDS) was detected.
## Lipidomics analysis
Total fatty acid composition was determined based on fatty acid methyl ester gas chromatography-mass spectrometry (FAME GC-MS) in samples from plasma, red and yellow bone marrow, $$n = 30$$ respectively (Ecker et al., 2012[8]). 10 to 20 mg of bone marrow tissue and 100 µL plasma were snap-frozen on dry ice immediately after excision and stored at -80 °C until extraction. Tissue samples were weighed in tubes with 700 mg lysing matrix D (#116913050-CF, MP biomedicals, Lake Forest, CA, USA) and allowed to thaw on wet ice. The concentration was set to 0.05 mg/μL using equal parts MeOH and water (#1060181000, Merck, Darmstadt, GER). Tissues were lysed using a homogenizer (FastPrep) set to 30 s and 6 m/s. Transesterification of 10 µL serum and 1 mg bone marrow in solution was carried out as previously described and FAMEs were extracted using hexane (#1007951000, Merck) (Lepage and Roy, 1986[18]; Ecker et al., 2012[8]). GC-MS based total FA analysis was performed as previously published (Ecker 2012[8]). FA13:0 iso as well as FA21:0 iso were used as internal standard for quantification. Total fatty acid amount is presented as nmol/mg protein. Individual fatty acid compositions are presented as molar percentages of the total fatty acid profile. The analyzed fatty acid profile is summarized in Table 1(Tab. 1).
## Statistics
The numbers of patients/donors (N) and technical replicates (n) for each experiment are given in the figure legends. Multivariate analyses including correlation matrices and partitioning were done with JMP 16.0.0 (SAS Institute GmbH, Heidelberg, GER). Individual data points are displayed in scatter plots. Correlations between two factors are displayed as bivariate fitted normal ellipses ($$p \leq 0.90$$) and summarized as colored significance circles. Data comparing groups are summarized as box plots with individual measurement points - each with median and interquartile range - analyzed and visualized with GraphPad Prism Version 8 (Dotmatics, Boston - MA, USA). Due to the sample size, a Gaussian distribution could not be assumed and data were compared by non-parametric Kruskal-Wallis test with Dunn's correction for multiple comparisons. A $p \leq 0.05$ was considered significant.
## The amount of total fatty acids increased in the bone marrow with low BMD
Between July 2017 and March 2018 patients were recruited for this study. From 36 eligible patients 6 had to be excluded from the study, because the patients refused study participation ($$n = 4$$) or sampling failed ($$n = 2$$). The 30 patients included in this study were then grouped based on their age-adjusted BMD (T-score). 9 patients (4 males and 5 females) had a T-score above -1.0 representing a normal BMD. 12 patients (6 males and 6 females) had a T-score between -1.0 and -2.5, characteristic for osteopenia. The remaining 9 patients (3 males and 6 females) had a T-score below -2.5, characteristic for osteoporosis (Figure 1A(Fig. 1)). The average age and body mass index were comparable between the three groups (Figure 1B, C(Fig. 1)). The amount of total fatty acids and their composition in the plasma were comparable between the three groups. On average, the amount of saturated fatty acids (SAT) was comparable between plasma, red bone marrow (RBM), and yellow bone marrow (YBM). The amount of mono-unsaturated fatty acids (MUFA) in the plasma was approx. half of that in the RBM or YBM. In contrast, the plasma contained approx. 2.5-fold more poly-unsaturated fatty acids (PUFA) than the RBM or YBM. The overall amount of fatty acids in the bone marrow increased with decreasing T-score. The effect was most pronounced in the YBM, where patients with osteoporosis had almost double the amount of fatty acids than patients with normal BMD. The amount of SAT increased by 61.5 % in the RBM and 73.3 % in the YBM. The amount of MUFA increased by 56.2 % in the RBM and 66.2 % in the YBM. The amount of PUFA increased by 101.1 % in the RBM and 146.7 % in the YBM (Figure 1D(Fig. 1)).
## The amount of saturated fatty acids increases in the bone marrow with decreasing BMD
The composition of SAT in the plasma, RBM, and YBM had in common that the levels of FA14:0, FA15:0, FA16:0, FA17:0, FA18:0, FA20:0, FA22:0, FA23:0, and FA24:0 showed a strong positive correlation. In the RBM and the YBM this was also valid for FA10:0, and exclusively in the YBM also for FA8:0. In contrast, in the plasma, FA8:0 and FA10:0 showed a strong positive correlation independent of the other SAT measured (Figure 2A(Fig. 2)). None of the SAT measured in the plasma showed a correlation with the T-score. In contrast, almost all SAT measured in the RBM and YBM showed a negative correlation with the T-score. FA8:0 showed no correlation with the T-score (Figure 2B(Fig. 2)). Comparing the levels of the individual SAT in the plasma, RBM, and YBM revealed that there was no correlation between the individual SAT measured in the plasma and RBM, but a weak negative correlation between FA18:0 and FA22:0 measured in the plasma and YBM. In contrast, FA10:0, FA12;0, FA14:0, FA15:0, FA17:0, and FA23:0 measured in the RBM and YBM correlated positively (Figure 2C(Fig. 2)). Comparing the average levels of the SAT within the three groups investigated (normal BMD / osteopenia / osteoporosis) showed significantly increased levels of FA15:0, FA17:0, FA18:0, and FA23:0 in the RBM and YBM of patients with osteoporosis when compared to controls. Additionally, levels of FA10:0, FA16:0 and FA20:0 were significantly increased in the YBM of patients with osteoporosis when compared to controls (Figure 2D(Fig. 2)).
## The amount of the mono-unsaturated fatty acids FA18:1 n-9 and FA20:1 n-9 is elevated in the yellow bone marrow of patients with osteoporosis
The composition of MUFA in the plasma, RBM, and YBM had in common that all the MUFA measured positively correlated with each other. The strongest correlation was observed in RBM, followed by plasma and YBM (Figure 3A(Fig. 3)). Again, none of the MUFA measured in the plasma showed a correlation with the T-score. The MUFA quantified in the RBM and YBM showed a negative correlation with the T-score, which was most pronounced for FA14:1, FA16:1 N-7, FA18:1 n-9, and FA20:1 n-9 in the RBM and FA18:1 n-9 and FA20:1 n-9 in the YBM (Figure 3B(Fig. 3)). Comparing the levels of the individual MUFA in the plasma, RBM, and YBM showed only a weak positive correlation between FA18:1 n-7 measured in the plasma and RBM, and FA16:1 n-7 measured in the plasma and YBM. In contrast, most MUFA measured in the RBM and YBM correlated positively with each other (Figure 3C(Fig. 3)). Comparing the average levels of the MUFA within the three study groups identified significantly elevated levels of FA18:1 n-9 and FA20:1 n-9 only in the YBM of patients with osteoporosis when compared to controls (Figure 3D(Fig. 3)).
## The amount of poly-unsaturated fatty acids is increased especially in the yellow bone marrow of patients with osteoporosis
The composition of PUFA in the plasma, RBM, and YBM plasma, RBM, and YBM had in common that all the PUFA measured positively correlated with each other. In contrast to the RBM and YBM, which had overall a lower amount of PUFA than the plasma, FA20:4 n-3 [2] was not detectable in the plasma (Figure 4A(Fig. 4)). Similar to SAT and MUFA, none of the PUFA measured in the plasma showed a correlation with the T-score, but many of the PUFA measured in the RBM and YBM negatively correlated with the T-score (Figure 4B(Fig. 4)). Comparing the levels of the individual PUFA in the plasma, RBM, and YBM revealed positive correlations between FA20:5 n-3, FA22:5 n-3, and FA22:6 n-3 measured in the plasma and RBM, and FA20:5 n-3 measured in the plasma and YBM. In contrast, most PUFA measured in the RBM and YBM correlated positively with each other (Figure 4C(Fig. 4)). Comparing the average levels of the PUFA within the three study groups investigated showed significantly increased levels of FA18:2 n-6 and FA20:4 n-3 in both the RBM and YBM of patients with osteoporosis when compared to controls. In addition, levels of FA18:3 n-3, FA20:2 n-6, FA20:5 n-3, and FA22:6 n-3 were significantly increased in the YBM of patients with osteoporosis when compared to controls (Figure 4D(Fig. 4)).
## TRAP5b levels are negatively associated with T-scores and positively with selected fatty acids in the plasma, red-, and yellow bone marrow
In order to identify if alterations in the BMD are associated more with a decreased osteoblast activity or an increased osteoclast activity in our study population, levels of bone markers were detected in the plasma. While the osteoblast marker BAP showed no association with the T-scores, the TRAP5b levels showed a significant ($$p \leq 0.010$$) negative correlation with the T-scores (Figure 5A(Fig. 5)). Considering the associations between T-scores and the individual fatty acids measured, the next step screened for correlations between the TRAP5b levels and the individual fatty acids measured in the plasma, RBM, and YBM. In contrast to the T-score, TRAP5b levels were associated with several fatty acids measured not only in the RBM and YBM, but also in the plasma. From all SAT measured, FA10:0 showed inverse results in plasma and bone marrow; while showing the strongest positive correlation with TRAP5b in the RBM and YBM ($$p \leq 0.003$$ and $$p \leq 0.008$$, respectively), TRAP5b levels in the plasma were negatively associated with TRAP5b ($$p \leq 0.081$$). Furthermore, FA16:0 ($$p \leq 0.041$$), FA17:0 ($$p \leq 0.029$$), FA18:0 ($$p \leq 0.037$$), and FA24:0 ($$p \leq 0.044$$) in the plasma positively correlated with TRAP5b. In contrast, only FA14:0 ($$p \leq 0.033$$) in the YBM positively correlated with TRAP5b (Figure 5B(Fig. 5)). All MUFA and PUFA measured in the plasma, RBM, and YBM positively associated with TRAP5b levels. Correlations between MUFA and TRAP5b were significant for FA16:1 n-7 ($$p \leq 0.031$$), FA18:1 n-9 ($$p \leq 0.019$$), FA18:1 n-7 ($$p \leq 0.015$$), and FA20:1 n-9 ($$p \leq 0.014$$) measured in the plasma, as well as FA14:1 ($$p \leq 0.028$$ and $$p \leq 0.025$$) and FA16:1 n-7 ($$p \leq 0.028$$ and $$p \leq 0.043$$) measured in the RBM and YBM (Figure 5C(Fig. 5)). Correlations between PUFA and TRAP5b were significant only for FA20:3 n-6 ($$p \leq 0.026$$) measured in the plasma and FA18:3 n-6 ($$p \leq 0.034$$) measured the YBM (Figure 5D(Fig. 5)).
## The fatty acid composition not only in the bone marrow but also in the plasma is well associated with the age-adjusted BMD
Plasma levels of BAP and TRAP5b, the established markers for bone turnover, indicated increased bone resorption in our study group with osteoporosis. In this study population, a screening based on these two parameters identified patients with osteoporosis with the highest accuracy (sensitivity: 88.9 %; specificity: 95.2 %; accuracy 93.3 %; $p \leq 0.001$), however, failed to accurately identify (sensitivity: 57.9 %; specificity: 72.7 %; accuracy 63.3 %; $$p \leq 0.142$$) patients with osteopenia and osteoporosis (Figure 6A(Fig. 6)). Although, plasma levels of the measured fatty acids showed no association with the T-scores in our study population, a screening based on these parameters identified patients with osteoporosis with comparably high accuracy (sensitivity: 66.7 %; specificity: 95.2 %; accuracy 86.7 %; $p \leq 0.001$) than the bone markers. In contrast to the bone markers, this screening could also accurately identify (sensitivity: 75.0 %; specificity: 90.0 %; accuracy 80.0 %; $$p \leq 0.001$$) patients with osteopenia and osteoporosis (Figure 6B(Fig. 6)). As the fatty acid composition in the RBM showed most correlations with the T-scores, a screening based on these parameters identified patients with osteoporosis with the highest sensitivity (sensitivity: 100.0 %; specificity: 76.2 %; accuracy 83.3 %; $p \leq 0.001$). This screening identified patients with osteopenia and osteoporosis not only with the highest sensitivity but also with the highest accuracy (sensitivity: 100.0 %; specificity: 54.5 %; accuracy 83.3 %; $p \leq 0.001$ / Figure 6C(Fig. 6)). A screening based on the fatty acid composition in the YBM identified patients with osteoporosis with a high accuracy (sensitivity: 77.8 %; specificity: 85.7 %; accuracy 83.3 %; $$p \leq 0.002$$), but could not reliably identify (sensitivity: 81.0 %; specificity: 55,6 %; accuracy 73.3 %; $$p \leq 0.082$$) patients with both osteopenia and osteoporosis (Figure 6D(Fig. 6)).
## Discussion
Our data show significant associations between the age-adjusted BMD and the fatty acid composition in the RBM and YBM but not in the plasma of our study group. On average the plasma contained approx. 40 % SAT, 27 % MUFA, and 33 % PUFA. Interestingly, the average composition in the bone marrow was different. With 36 % the average amount of SAT was comparable to the plasma but the average amount of MUFA (52 %) and PUFA (12 %) revealed a higher proportion of MUFA than PUFA in the bone marrow. In line with the literature (Cohen et al., 2012[4]; Devlin and Rosen, 2015[6]), the amount of fatty acids increased with decreasing T-score, especially in the YBM - which in the osteoporotic group contained almost double the amount of fatty acids than in the control group. Contrary to earlier assumptions that marrow adipocytes are mainly quiescent and metabolically inert, recent studies have revealed that MAT may be highly metabolically active, responsive to physiological stimuli, and actively interacting with osteoblasts and osteoclasts to mediate bone homeostasis (Sheu and Cauley, 2011[29]; Krings et al., 2012[16]). The fatty acid composition may be the key factor in this process. For example, anti-inflammatory omega-3 PUFA may counteract the effects from pro-inflammatory SAT in developing calcified plaques in atherosclerosis, however, their effect on BMD is less clear.
Studies quantifying the general amount and composition of MAT with non-invasive imaging techniques, e.g. proton magnetic resonance spectroscopy (Schellinger et al., 2004[27]; Yeung et al., 2005[31]) or chemical shift encoding-based water-fat magnetic resonance imaging (Beekman et al., 2022[2]), uniformly suggested that an increase in SAT is mainly responsible for the increase in MAT in osteoporosis. However, quantifying the individual fatty acid composition in the bone marrow with gas chromatography could not confirm this result (Griffith et al., 2009[10]; Miranda et al., 2016[21]). In the first study no significant alterations in the fatty acid composition could be observed, which was partly explained by the different donor sites the bone marrow was obtained from (Griffith et al., 2009[10]). Such a donor site variation was excluded in our study, where all samples were obtained from the femur head. Similarly, in the second study where solely iliac crest biopsies from postmenopausal women were compared. In these samples, decreasing BMD was associated with decreased levels of SAT and increased levels of MUFA, especially in those patients which had experienced fragility fractures (Miranda et al., 2016[21]). However, the comparability of these studies is limited - besides a possible influence of the donor site (Griffith et al., 2009[10]), the fatty acid composition in these studies is described as molar percentages of the total fatty acid profile, which was different in each study. Especially, the amount of the long chain SAT, e.g. FA18:0, FA20:0 and FA22:0, seemed to be significantly affected by the donor site (Griffith et al., 2009[10]). In our samples, significantly elevated levels of the SAT FA10:0, FA15:0, FA16:0, FA17:0, FA18:0, FA20:0, and FA23:0 were detected in especially the YBM of patients with osteoporosis when compared to controls. Of these plasma levels of FA16:0, FA17:0, and FA18:0 were positively associated with the osteoclast activity in these patients, as measured by circulating TRAP5b levels. This finding is supported by a study showing enhanced osteoclastogenesis in mice on a high-fat diet enriched with FA16:0 (palmitic acid). A SAT induced inflammatory reaction with induction of TNF-α, was identified as key regulator in this process (Drosatos-Tampakaki et al., 2014[7]). In the bone marrow, however, there was a strong association of FA10:0 with the circulating TRAP5b levels, suggesting a positive effect on osteoclast formation or function. Unfortunately, FA10:0 (capric acid) was not measured in the two aforementioned studies (Griffith et al., 2009[10]; Miranda et al., 2016[21]). However, our results were supported by a mouse model investigating the effect of medium‑chain triglyceride ketogenic diet on bone mineral density. Dietary supplementation with capric acid, and even more with caprylic acid (FA8:0), resulted in decreased BMD and increased circulating TRAP5b levels (Jain et al., 2021[13]). In contrast, capric acid was reported to effectively inhibit osteoclast formation in vitro (Park et al., 2011[24]; Kim et al., 2014[15]).
In the iliac crest biopsies of postmenopausal women a significant increase in MUFA was observed especially in those samples with osteoporosis and a history of fragility fractures (Miranda et al., 2016[21]). In the present study the increase in the amount of MUFA was less pronounced, solely FA18:1 n-9 and FA20:1 n-9 were significantly increased in the YBM in the osteoporotic group when compared to the control group. In the plasma there was again a positive association of the circulating TRAP5b levels with FA18:1 n-9 (oleic acid), FA18:1 n-7 (vaccenic acid), and FA20:1 n-9 (eicosenoic acid), again suggesting a stimulatory effect on osteoclasts. Of particular interest is the highly specific trans fatty acid vaccenic acid, whose consumption in dairy products has been associated with the incidence of hip fractures in different European countries. As potential mechanism inhibition of alkaline phosphatase activity and resulting calcification has been proposed. Interestingly, dietary supplementation with oleic acid could partly counteract this effect (Hamazaki et al., 2016[11]). In another study a positive effect of oleic acid on BMD was explained with an increase in OPG/RANKL ratio, suppressing osteoclastogenesis (Martin-Bautista et al., 2010[19]), however, in this study diary supplementation was not oleic acid alone, but contained also fish oil (omega-3 PUFA) and vitamins. Likewise, dietary supplementation with oleic acid reduced saturated fatty acid-induced osteoclastogenesis in a mouse model (Drosatos-Tampakaki et al., 2014[7]). However, the triglyceride accumulation favored by oleic acid might further expand the MAT. This finding is further supported by two in vitro studies, showing enhanced triglyceride accumulation in oleic acid treated osteoprogenitor cells. While in the first study oleic acid also improved osteogenic function (alkaline phosphatase activity) in the murine bone marrow-derived stroma cell line ST2 (Deshimaru et al., 2005[5]), the opposite was observed for murine MC3T3-E1 osteoprogenitor cells in the second study (Hutchins et al., 2011[12]). In yet another study it was proposed that melatonin could counteract fatty-acid induced triglyceride accumulation in rat ROS$\frac{17}{2.8}$ osteoprogenitor cells (Sanchez-Hidalgo et al., 2007[26]), which is of potential interest for clinical application.
As mentioned before, in the current study, the overall amount of PUFA increased the most with decreasing BMD. When comparing the osteoporotic group with the control group, especially, FA18:2 n-6, FA18:3 n-3, FA20:2 n-6, FA20:4 n-3, FA20:5 n-3, and FA22:6 n-6 were significantly increased in the YBM. Increased levels of FA18:2 n-6 (linoleic acid) have also been found in the osteoporotoc iliac crest biopsies from postmenopausal women. In contrast, in these samples FA20:3 n-6 and FA20:4 n-6 levels were significantly decreased, especially when donors had a history of fragility fractures (Miranda et al., 2016[21]). Interestingly, the same PUFA (FA20:3 n-6) measured in the plasma showed a strong positive correlation with circulating TRAP5b levels in the present study. As the omega-6 (n-6) PUFA represent precursors to the most strongly pro-inflammatory eicosanoids, an induction of osteoclastogenesis via TNF-α (as reported above (Drosatos-Tampakaki et al., 2014[7])) could be considered. This assumption is supported by the study reporting increased levels of omega-6 (n-6) PUFA in MAT derived from femoral heads of patients with osteoarthritis when compared to patients with osteoporosis (Plumb and Aspden, 2004[25]). A diet rich in anti-inflammatory omega-3 (n-3) PUFA has been reported to improve BMD in different rodent models - for review see (Sharma and Mandal, 2020[28]). In the present study, certain anti-inflammatory omega-3 (n-3) PUFA have been increased in bone marrow. For example, α-linoleic acid (FA18:3 n-3) in the YBM not only negatively associated with T-scores, but also positively with circulating TRAP5b levels, suggesting active bone resorption. This is in contrast to a study in rats, which reported that α-linolenic acid reduced bone loss caused by a high-fat diet. In this study, α-linoleic acid had no effect on osteoclast function but stimulated osteoblast function (Chen et al., 2019[3]). In the present study, no significant association between osteoblast activity, measured by BAP levels, and any of the SAT, MUFA, or PUFA could be detected. In vitro, medium supplementation with PUFA FA20:5 n-3 and FA22:6 n-3 even suppressed alkaline phosphatase activity in MC3T3-E1 cells (Hutchins et al., 2011[12]). Interestingly, dietary uptake of these two PUFA were positively associated with BMD in a large female cohort investigated in Spain (Lavado-Garcia et al., 2018[17]). Overall, however, evidence is still low that dietary supplementation with omega-3 (n-3) PUFA has a real beneficial effect on BMD in human studies - for review see (Orchard et al., 2012[23]).
## Conclusion
In this study, the observed alterations in the fatty acid composition contained specific SAT, MUFA, and PUFA, which in combination gave a characteristic pattern for patients with normal BMD, osteopenia, and osteoporosis. The selective markers for their differentiation differ between plasma, RBM, and YBM and partly correlated with osteoclast activity, suggesting a possible mechanism how these fatty acids may interfere with BMD. The observation that there was no general correlation between the T-scores and the plasma levels of the measured fatty acids, however, a screening based on these parameters reliably identified patients with osteopenia and osteoporosis, suggests that there is not a single fatty acid that can be claimed for controlling BMD. The overall, positive effects of omega-3 (n3) fatty acids on BMD observed in rodents studies, could be explained by the genetic homogeneity of the animals (Jiao et al., 1990[14]). *The* genetic variability in humans should be considered, especially, when dietary supplementation with fatty acids is attempted (Garcia-Rios et al., 2012[9]).
## Notes
Anna J. Schreiner and Claudine Seeliger contributed equally as second author.
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|
---
title: '“Own doctor” presence in a web-based lifestyle intervention for adults with
obesity and hypertension: A randomized controlled trial'
authors:
- Pedro Múzquiz-Barberá
- Marta Ruiz-Cortés
- Rocío Herrero
- María Dolores Vara
- Tamara Escrivá-Martínez
- Rosa María Baños
- Enrique Rodilla
- Juan Francisco Lisón
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043391
doi: 10.3389/fpubh.2023.1115711
license: CC BY 4.0
---
# “Own doctor” presence in a web-based lifestyle intervention for adults with obesity and hypertension: A randomized controlled trial
## Abstract
### Introduction
Online interventions have long been shown to be an effective means to promote a healthy lifestyle, thereby helping to control body weight and blood pressure figures. Likewise, using video modeling is also considered an effective way to guide patients through behavioral interventions. Nonetheless, to the best of our knowledge, this study is the first to analyze how the presence of patients' “own doctor” in the audiovisual content of a web-based lifestyle program (“Living Better”) aimed at promoting regular physical exercise and healthy eating behavior, compared with an “unknown doctor,” influences the outcomes of adults with obesity and hypertension.
### Materials and methods
A total of 132 patients were randomly assigned either to the experimental ($$n = 70$$) or control ($$n = 62$$) group (“own doctor” or “unknown doctor”, respectively). The body mass index, systolic and diastolic blood pressure, number of antihypertensive drugs used, physical activity level, and quality of life was assessed and compared at baseline and post-intervention (12 weeks).
### Results
The intention-to-treat analysis showed intragroup significant improvements in both groups in terms of the body mass index (control group: mean difference −0.3, $95\%$ CI [−0.5, −0.1], $$p \leq 0.002$$; experimental group: −0.4 [−0.6, −0.2], $p \leq 0.001$) and systolic blood pressure (control group: −2.3 [−4.4, −0.2], $$p \leq 0.029$$; experimental group: −3.6 [−5.5, −1.6], $p \leq 0.001$). In addition, there were also significant improvements in the experimental group for the diastolic blood pressure (−2.5 [−3.7, −1.2], $p \leq 0.001$), physical activity (479 [9, 949], $$p \leq 0.046$$), and quality of life (5.2 [2.3, 8.2], $$p \leq 0.001$$). However, when comparing the experimental with the control group, no between-group significant differences were found in these variables.
### Conclusions
This study suggests that the presence of patients' “own doctor” in the audiovisual content of a web-based intervention, aimed at promoting a healthy lifestyle among adults with obesity and hypertension, do not show significant additional benefits over the efficacy of e–counseling.
### Trial registration
ClinicalTrials.gov NCT04426877. First Posted: $\frac{11}{06}$/2020. https://clinicaltrials.gov/ct2/show/NCT04426877.
## 1. Introduction
The international guidelines specialized in hypertension [1, 2] and obesity [3] agree that the first step to consider in clinical approaches to patients with obesity and hypertension should be the promotion and acquisition of a healthier lifestyle, based on two key pillars: the establishment of healthy eating behavior and regular engagement in physical exercise. On the other hand, with the aim of promoting proactive disease control by patients and reducing the burden of care, the World Health Organization [4] has been trying to encourage health interventions administered through the internet and technologies for many years now. Accordingly, multiple publications have shown the effectiveness of educational interventions leveraging multimedia material in different pathological populations (5–11), with most of them also being oriented toward education about healthy lifestyles (5–7, 11).
Furthermore, “using video modeling, which involves the demonstration of desired behaviors, outcomes, and attitudes through active, visual representations by an actor,” is considered an effective way to educate and guide patients through behavioral interventions, even for people with low levels of literacy [8, 12]. Moreover, it has also been shown that the simple gesture of doctors talking to patients about their own personal practices —in terms of physical activity and nutrition— helps promote general patient health. This is because patients are more likely to adopt healthy behaviors when their doctor also practices them (referred to as the “lead by example” practice) [13]. Indeed, the therapeutic alliance, understood as the quality of the relationship between the patient and the doctor, seems to be a decisive factor in patients assuming more proactive roles in their own health care [14, 15].
Considering all the above, the objective of this present study was to analyze the influence exerted by the identity of the main doctor appearing in the audiovisual content of our e-Health intervention on patients with the obesity–hypertension phenotype in terms of the following variables: body mass index, systolic and diastolic blood pressure, number of antihypertensive drugs used, physical activity, quality of life, satisfaction and adherence to the intervention. To do this, after 12 weeks of intervention with our “Living Better” web-based program (16–19), we compared the results in two groups: the control group in which an “unknown doctor” appeared in the audiovisual content, and the experimental group whose audiovisual content instructions were provided by the patients' own hypertension specialist. We hypothesized that [1] all the participants would achieve improvements in the different variables analyzed after the 12-week intervention, regardless of the identity of the doctor present in the audiovisual content delivered to them and [2], patients who saw their own specialist doctor giving them the indications would attain greater benefits than those in the control group.
## 2.1. Study design
This was a prospective, single-center, clinical trial (registered at ClinicalTrials.gov: NCT04426877) with balanced randomization (1:1). This study was reviewed and approved by the University CEU-Cardenal Herrera Ethics Committee (CEI$\frac{19}{085}$). This research was also approved by the Human Research Ethics Committee at the Hospital Universitario de Sagunto and followed the ethical guidelines established in the Declaration of Helsinki.
## 2.2. Eligibility criteria
The following inclusion criteria were applied to select the study participants: adults aged between 18 and 75 years with hypertension who were overweight (body mass index >24.9 kg/m2 and <30 kg/m2) or who had type I obesity (body mass index >29.9 kg/m2 and <35 kg/m2), and who were patients that saw the same physician specialized in hypertension. As previously described [18, 19], hypertension was defined as a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg, or patients taking antihypertensive drugs; in this study all the patients were on antihypertensive treatments. Regarding the exclusion criteria, patients who had not come for at least 1 visit with their specialist in the 5 years prior were excluded from the work. In addition, profiles with previous ischemic heart disease, cerebrovascular disease, serious psychiatric disorders, taking more than 3 antihypertensive medications, with physical impairments that could make it difficult to practice exercise, participating in other treatments for weight loss, who had previously participated in our “Living Better” intervention (17–19), and/or without internet access were also excluded from this current study.
## 2.3. Procedure
This study took place in the Hypertension and Vascular Risk Unit at the Hospital Universitario de Sagunto (Valencia, Spain) between January and June 2021. All the participants formalized their enrollment by signing their informed consent to participate in the study.
Before the start of the trial, an independent researcher unaware of the study characteristics generated a random sequence using a computerized random number generator; this was concealed from all the other study investigators throughout the entire study period. Randomization was performed with stratification for age, sex, and number of specialist visits. Upon enrollment in the study and after completing the primary and secondary outcome measures, the participants ($$n = 132$$) were randomly assigned either to the control ($$n = 62$$) or the experimental group ($$n = 70$$). It was impossible to mask the group allocation to the participants; however, the outcome evaluators and data analysts were blinded to the treatment allocations.
As shown in the participant flowchart (Figure 1), the different study variables were recorded at baseline just before the start of the program. Once this evaluation was completed, all participants started the 12-week online intervention with the “Living Better” web-based program in the group to which they had been previously assigned. The program content followed by both groups was identical, with the exception that the doctor who appeared in the audiovisual material differed between them; the control group patients saw a doctor that they did not know while those assigned to the experimental group saw their own hypertension specialist. Both the doctors involved in delivering the audiovisual content in this study were specialists in hypertension and vascular risk, regularly engaged in physical exercise, and had a healthy appearance. To understand the impact of this intervention on the health of the participants, we recorded all the variables again in the post-intervention assessments at the end of the program.
**Figure 1:** *Progression of the participants through the trial.*
## 2.4. Intervention
The “Living Better” program is a computerized intervention that is self-administered through the internet. The treatment protocol consists of 9 modules and incorporates psychological strategies that encourage a healthy lifestyle by promoting the regular practice of physical exercise and healthy eating behavior. A period of 12 weeks is allowed for completion of the entire program, during which time the modules are activated weekly or fortnightly. Some of the techniques used that have already been described in the literature (20–22) were self-monitoring, self-instruction, behavioral recording, stimulus control, self-reinforcement, problem-solving techniques, and homework. More details about the original intervention can be found in Baños et al. [ 16], Mensorio et al. [ 17], and Lison et al. [ 18]. Furthermore, considering the suggestions of the participants in these previous studies (mainly to help facilitate usability) and in order to test our current hypothesis, we converted part of the written content into audiovisual materials, as detailed in Múzquiz-Barberá et al. [ 19]. As previously mentioned, the content was identical in both groups, but the doctor who appeared in the audiovisual material differed between the groups. The audiovisual presence of the doctors (presented in video format) consisted of welcoming the patients and establishing the objectives of the module, demonstrating the exercises the participants had to practice, concluding the module and introducing the next one, and encouraging the participants to continue advancing through the intervention and put everything they had learned into practice. Specifically, “Living Better” contains 32 videos that total 52 min of the presenting doctors' audiovisual presence.
## 2.5. Outcome measures
Patient age, sex, time since the hypertension diagnosis, and the number of visits to the specialist since the first diagnosis as hypertensive, were all registered before the randomization process was implemented. Furthermore, the variables listed below were recorded before and after the intervention, through the same platform as the intervention program.
## 2.5.1.1. Body mass index
Due to the indications of the health authorities and the hospital regulations related to the COVID-19 pandemic, the participants were instructed to register this variable in a pharmacy near their home. They were also instructed to avoid smoking for 48 h, caffeine for 12 h, and strenuous exercise for 24 h prior to the registration. In addition, they were asked to go the pharmacy while fasting to avoid the possibility that any food or drink ingested could influence their data. Thus, the same person (pharmacist or pharmacy assistant) used an approved device to assess the different body composition variables for each patient. Body mass index was calculated by dividing patient weight by their height squared (kg/m2).
## 2.5.2.1. Systolic and diastolic blood pressure
The patient's body composition measurements and systolic and diastolic blood pressure were also recorded at the same pharmacy. This was done first thing in the morning and before taking their antihypertensive medication to avoid possible alterations in the measurements. Blood pressure was strictly analyzed according to the American College of Cardiology/American Society of Hypertension [1] and the European Society of Hypertension (ESH)/European Society of Cardiology guidelines [2]. Of note, the participants of this study, and in general every patient treated in the Hypertension Unit at the Hospital Universitario de Sagunto, are routinely trained to correctly measure blood pressure in this way.
## Number of antihypertensive drugs
The patients recorded the number of prescribed medications they used to control of their hypertension.
## Physical activity level
The short version of the International Physical Activity Questionnaire (IPAQ-SF) was used [23, 24] to assess the time each subject had spent being active in the 7 days prior to completion of the survey.
## Quality of life
The SF-12 Health Questionnaire (a reduced version of the SF-36) was applied to measure quality of life [25]. This self-administered instrument provides a health status profile and consists of 12 items grouped into the 8 dimensions of the SF-36, ranging from 0 (the worst state of health for that dimension) to 100 (the best state of health) [25]. In the current study, we focused on the analysis of the General Health dimension.
At the end of the intervention, we recorded the adherence of the participants to the program. To do this, we took advantage of the data regarding the degree of completion of each patient collected automatically by the online platform. In other words, we recorded how many modules they had reviewed out of a total of 9, and how much time they had spent on average per module. This also made it possible to estimate the minutes of audiovisual content (in video format) that each participant had viewed. Finally, the participants registered their degree of general satisfaction with the intervention on a scale from 0 (least satisfaction) to 10 (maximum satisfaction).
## 2.6. Statistical analysis
To detect a reduction in body mass index of 1 ± 1.7, which agrees with the data of a previous study [17], with a two-sided $5\%$ significance level and a power of $80\%$, and also accounting for an anticipated dropout rate of $30\%$, a sample size of 60 participants per group was required. The statistical analysis was performed according to intention-to-treat. We used SPSS software for Windows (version 19.0; IBM Corp., Armonk, NY) in all our analyses.
Two-way mixed analysis of covariance (ANCOVA) tests was used to compare the study effects on body mass index, systolic and diastolic blood pressure, physical activity, and quality of life, using time as the within-group factor (baseline vs. post-intervention assessments), and the group as the between-group factor (control vs. experimental group). The analysis was adjusted for number of antihypertensive drugs. On the other hand, we implemented a two-way mixed ANOVA test for the antihypertensive drugs variable, also using time as the within-group factor (baseline vs. post-intervention assessments) and the group as the between-group factor (control vs. experimental group).
Bonferroni post-hoc tests were applied following the ANCOVAs and ANOVA. Partial eta-squared (ηp2) effect sizes were calculated such that 0.01–0.06, 0.06–0.14, and 0.14 or higher, respectively corresponded to small, medium, and large effect sizes [26]. Non-parametric Mann–Whitney U tests were used to calculate the degree of satisfaction and adherence to the intervention (number of modules completed and time spent per module) by participants assigned to the control and experimental group, respectively. A per-protocol analysis was performed to compare the study effects which would occur under optimal conditions. Finally, correlation analyses were performed to examine possible associations between the changes (post-intervention minus the baseline) in body mass index, systolic and diastolic blood pressure, physical activity level, number of antihypertensive drugs, and quality of life. The magnitude of the Pearson correlation was interpreted according to the suggestions by Hopkins et al. [ 27] where 0.0–0.1 = trivial; 0.1–0.3 = small; 0.3–0.5 = moderate; 0.5–0.7 = large; 0.7–0.9 = very large, and 0.9–1 = almost perfect. In addition, forward stepwise regression was used to determine the combination of variables that most accurately predicted quality of life of patients. The statistical significance was set at $p \leq 0.05$ for all our analyses.
## 3.1. Reported changes on the body mass index, blood pressure, antihypertensive drugs, physical activity, and quality of life
Table 1 shows the values collected during the baseline assessment, prior to the intervention. Table 2 shows the results of the tests according to an intention-to-treat analysis. As shown, the two-way mixed ANCOVA tests showed intragroup significant improvements in body mass index and systolic blood pressure in the control group with a moderate and small effect size, respectively. There were intragroup significant improvements in all the variables analyzed in the patients assigned to the experimental group, with moderate effect sizes for body mass index, systolic and diastolic blood pressure, and quality of life. As shown in Supplementary Table 1, the per-protocol analysis executed showed significant improvements with a large effect size for body mass index in both groups, and also in the experimental group for systolic and diastolic blood pressure, and quality of life. The two-way mixed ANOVA test results did not show any intragroup significant changes in the number of antihypertensive drugs used by the participants in either group. However, when comparing the experimental with the control group after the intervention, no between-group differences were found in any analysis (Table 3 and Supplementary Table 2), except for antihypertensive drugs variable in which statistical significance were found in both intention-to-treat (−0.3 [−0.6, −0.1]; $$p \leq 0.011$$) and per-protocol (−0.5 [−0.9, −0.1]; $$p \leq 0.008$$) analysis; this difference had already been found before the intervention ($$p \leq 0.012$$). Associations between the changes (post-intervention minus the baseline) in body mass index, systolic and diastolic blood pressure, physical activity level, number of antihypertensive drugs, and quality of life are summarized in Supplementary Table 3. Stepwise multiple regression revealed that the changes in physical activity level was a significant and independent predictor for the improvement in quality of life (AdjR2 = 0.092, β = 0.315, $p \leq 0.001$; model 1), explaining $9.2\%$ of the variation in the quality of life (Supplementary Table 4). Model 2 included the systolic blood pressure to the physical activity level and explained 14.8 % of the variation.
## 3.2. Differences showed on adherence and satisfaction with the intervention
Regarding adherence to the intervention, no statistically significant differences were observed between the control and the experimental group in terms of the median number of modules completed (control group: 4.0 [IQR = 5]; experimental group: 4.5 [IQR = 5]; $$p \leq 0.982$$) or the median minutes dedicated to each module (control group: 60.0 [IQR = 25]; experimental group: 60.0 [IQR = 35]; $$p \leq 0.710$$). Specifically, 42 and $49\%$ of the participants in the control and experimental group reached the middle of the program (5 modules or more), respectively. Finally, both groups showed similar median levels of patient satisfaction with the intervention and there were no statistically significant differences between them (control group: 8.0 [IQR = 4]; experimental group: 8.0 [IQR = 4]; $$p \leq 0.621$$).
## 4. Discussion
To the best of our knowledge, this is the first study to analyze the influence of the audiovisual presence of the patients' own specialist doctor in an online intervention program aimed at promoting a healthy lifestyle (regular physical exercise and healthy eating behavior) in patients with an obesity–hypertension phenotype. Indeed, we are not aware of any other studies on any other disease or pathological condition that have looked at this possible effect. Contrary to expectation, the presence of a patients' “own doctor” in the audiovisual content did not bring about any significant additional benefits over the efficacy of the e–counseling in patients with obesity and hypertension.
It is known that physically active and health-conscious doctors can become influential role models for their patients, motivating them to adopt healthier lifestyles with the aim of preventing and treating possible chronic diseases [13, 28]. In fact, health promotion by physicians is more effective than outsourcing advice to a health coach, in part because patients view physicians as the most authoritative source in which to entrust their health [13]. Furthermore, several lines of investigation (29–31) have found that the therapeutic alliance can be just as effective when treatments are carried out online or making use of technological platforms. With the aim of improving the therapeutic alliance and, therefore, the results of our primary and secondary outcomes, a total of 32 videos were included in the intervention, resulting in a combined 52 min of audiovisual contact with the presenting doctor (~6 min per module). However, despite the significant benefits reported by both groups in the intragroup analysis, the possible influence exerted by the identity of the main doctor appearing in the audiovisual content did not reach the statistical significance in the between-group comparisons.
The degree of completion by the participants was lower compared to a previous “Living Better” study [18], corresponding to $24\%$ less adherence to the intervention. This may be the result of participant difficulties in the context of the ongoing COVID-19 pandemic or in following the demanding planning presented by the program in relation to accessing and reviewing the different modules. Likewise, the number of losses in the post-intervention assessments was also manifestly higher ($43\%$ in the experimental and $58\%$ in the control group) compared to those ($18\%$) in the previous study [18]. This may have been due to an increase in the difficulty in taking the post-intervention measurements which had to be carried out outside of the hospital context because of the health restrictions due to COVID-19. Despite these two differences (lower adherence and increased losses after the intervention), the results of the intention-to-treat statistical analysis showed that all participants experienced benefits. Furthermore, in order to verify if the low adherence and the high losses conditioned the possible effect of the presence of their “own doctor,” it was decided to carry out a per-protocol analysis to analyze the real impact of “Living Better” on the participants who had completed at least 5 modules and had undertaken the post-intervention assessments. Interestingly, in line with our intention-to-treat analysis, the per-protocol analysis did not show between-group differences after the intervention, confirming the observed result that the inclusion of a patients' “own doctor” did not result in significant additional benefits.
Although the results of this study did not confirm the initial hypothesis, the “Living Better” program has again shown to have benefits on the different study variables. In this sense, the results suggest that an intervention of this nature can improve body composition, blood pressure, levels of physical activity and quality of life in patients with obesity and hypertension. The results showed positive correlations between the improvements in body mass index and systolic and diastolic blood pressure. In this sense, the academic literature reflects the direct impact that weight reduction, and therefore body mass index, has on blood pressure values [32, 33]. Indeed, a meta-analysis [33] showed that a decrease in blood pressure figures of approximately 1 mmHg is achieved for each kilogram lost. In addition, systolic blood pressure reductions of 5 mmHg have been associated with significant reductions in all-cause mortality [34]. At this point, we must remember that educational interventions with multimedia materials are considered potentially more effective than other forms of support when trying to address physical inactivity and obesity [35]. Likewise, the provision of online advice through videos also facilitates the learning of new behavior related to health [7, 36]. Thus, this type of intervention has widely demonstrated its effectiveness in controlling body weight (37–44) and blood pressure figures (45–48) by promoting a healthy lifestyle. In fact, the participants in the experimental group showed greater benefits than those participants in the previous studies implementing the original “Living Better” intervention (17–19). Perhaps, these differences could be explained by the addition of more multimedia content in this current version of “Living Better.”
## 4.1. Limitations
It is important to outline the limitations of this study. Firstly, the enrolled participants had demonstrated an initial level of motivation to engage in an e-Health program, which may have introduced potential selection bias. Secondly, this single-center clinical trial only involved one doctor per arm, and therefore is potentially confounded by their personal characteristics that could have influenced the outcomes. In addition, we did not control the analysis for confounding psychological variables such as therapeutic alliance or similar constructs. A third possible limitation includes recall bias, because all the participants' responses in the questionnaires were conditioned by their ability to recall their habits, as well as desirability bias, whereby participants tended to minimize unhealthy habits and exaggerate healthy behaviors. Also, there was a low adherence to the intervention and a high attrition rate at the post-intervention assessments, so that could perhaps have limited the between-group differences. Finally, information about the results in terms of the systolic and diastolic blood pressure achieved were limited; firstly, because the data were self-reported and could not be verified (because of the COVID-19 pandemic restrictions), and secondly, because ambulatory blood pressure measurement would have been a more accurate method to assess changes in blood pressure values.
## 5. Conclusions
This study suggests, for the first time, that the presence of patients' “own doctor” in the audiovisual content of an online intervention program (aimed at promoting a healthy lifestyle through regular physical exercise and healthy eating behavior) do not show significant additional benefits over the efficacy of the e–counseling in patients with an obesity–hypertension phenotype. Future studies with multiple doctors per arm, controlling for therapeutic alliance or similar constructs, with a larger and more representative sample size, and with ambulatory blood pressure measurements, should investigate the impact of the presence of a patients' “own doctor” in audiovisual web-based interventions for adults with obesity and hypertension. On the other hand, this study opens the door to future research on online interventions focused on other pathological populations supported by multimedia material and the presence of a physician or other health professionals.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by University CEU-Cardenal Herrera Ethics Committee (CEI$\frac{19}{085}$). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
PM-B, ER, RB, and JL conceived this research methodology and wrote/prepared the original draft. MR-C and RH were responsible for the methodology. PM-B and JL conducted a formal analysis. RB, ER, and JL managed the investigation. PM-B, MR-C, and RH reviewed and edited the manuscript. MV and TE-M were responsible for visualization. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1115711/full#supplementary-material
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|
---
title: Systematic identification of potential key microRNAs and circRNAs in the dorsal
root ganglia of mice with sciatic nerve injury
authors:
- Youfen Yu
- Xueru Xu
- Chun Lin
- Rongguo Liu
journal: Frontiers in Molecular Neuroscience
year: 2023
pmcid: PMC10043392
doi: 10.3389/fnmol.2023.1119164
license: CC BY 4.0
---
# Systematic identification of potential key microRNAs and circRNAs in the dorsal root ganglia of mice with sciatic nerve injury
## Abstract
### Background
Neuropathic pain (NeP) is a pathological condition arising from a lesion or disease affecting the somatosensory system. Accumulating evidence has shown that circular RNAs (circRNAs) exert critical functions in neurodegenerative diseases by sponging microRNAs (miRNAs). However, the functions and regulatory mechanisms of circRNAs as competitive endogenous RNAs (ceRNAs) in NeP remain to be determined.
### Methods
The sequencing dataset GSE96051 was obtained from the public Gene Expression Omnibus (GEO) database. First, we conducted a comparison of gene expression profiles in the L3/L4 dorsal root ganglion (DRG) of sciatic nerve transection (SNT) mice ($$n = 5$$) and uninjured mice (Control) ($$n = 4$$) to define the differentially expressed genes (DEGs). Then, critical hub genes were screened by exploring protein–protein interaction (PPI) networks with Cytoscape software, and the miRNAs bound to them were predicted and selected and then validated by qRT-PCR. Furthermore, key circRNAs were predicted and filtered, and the network of circRNA-miRNA-mRNA in NeP was constructed.
### Results
A total of 421 DEGs were identified, including 332 upregulated genes and 89 downregulated genes. Ten hub genes, including IL6, Jun, Cd44, Timp1, and Csf1, were identified. Two miRNAs, mmu-miR-181a-5p and mmu-miR-223-3p, were preliminarily verified as key regulators of NeP development. In addition, circARHGAP5 and circLPHN3 were identified as key circRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that these differentially expressed mRNAs and targeting miRNAs were involved in signal transduction, positive regulation of receptor-mediated endocytosis and regulation of neuronal synaptic plasticity. These findings have useful implications for the exploration of new mechanisms and therapeutic targets for NeP.
### Conclusion
These newly identified miRNAs and circRNAs in networks reveal potential diagnostic or therapeutic targets for NeP.
## Introduction
Neuropathic pain (NeP) is defined as chronic pain that arises from a lesion or disease that affects the somatosensory system by the International Association for the Study of Pain (IASP). NeP manifests as hyperalgesia, allodynia, and spontaneous pain, which have serious impacts on people’s daily lives. The most common clinical NeP diseases include trigeminal neuralgia, postherpetic neuralgia, and painful radiculopathy (Scholz et al., 2019). The common critical mechanism of different NeP diseases involves peripheral and central sensitization. Specific pathophysiological changes are involved in the pathogenesis of NeP, such as alterations in ion channel activity, activation of microglia and epigenetic modulation of nerve cells (Penas and Navarro, 2018).
It has been well accepted that the dorsal root ganglion (DRG) plays an important role in nociceptive transmission and modulation. In particular, the DRG receives peripheral nociceptive information and transmits signaling to the central nervous system (CNS) (Ma et al., 2022). Structural and functional disorders of the DRG, such as synaptic reorganization and alteration of voltage-gated sodium channels, are important for the progression of NeP. Considering the anatomical accessibility and functional specificity of DRG in NeP (Berger et al., 2021), further research on the pathogenesis of NeP at the level of the DRG would be of considerable significance to the development of novel treatments.
In recent years, there has been an increase in research on the regulation of NeP-related transcriptional genes. A transcription analysis of microRNAs (miRNAs), circular RNAs (circRNAs), and mRNAs in the DRG suggested that the circRNA-miRNA regulatory network is involved in paclitaxel-induced NeP (Mao et al., 2022). Other clinical and basic research has shown that circHIPK3 is highly abundant in serum from diabetes patients who suffered from NeP and in DRG from streptozocin-induced diabetic NeP rats (Wang et al., 2018). Advanced research has further applied single-cell transcriptomic analysis of somatosensory neurons in the DRG of spared nerve injury (SNI) rats, uncovering the temporal development of the NeP (Wang H. et al., 2021). These studies revealed that related miRNAs and circRNAs underlie the molecular mechanism of NeP genesis and development. However, the involvement of ceRNAs in the pathogenesis of NeP has not yet been studied in depth.
The purpose of this study was to explore the regulatory circRNA-miRNA-mRNA network in the DRG of mice induced by sciatic nerve transection (SNT). Finally, 2 circRNAs, 2 miRNAs and 10 mRNAs were used to construct a circRNA-miRNA-mRNA regulatory network, which may reveal new etiopathogenesis of NeP.
## Data source and DEGs definition
The public GSE96051 dataset was obtained from the Gene Expression Omnibus (GEO) database. 1 The dataset contains 9 samples, including DRGs of sciatic nerve transection (SNT) mice ($$n = 5$$) and uninjured mice (Control) ($$n = 4$$). First, gene expression data quality was analyzed and visualized for each set of samples using the ggplot2 package of R software, which is an open-source software package for statistical computing and graphics. 2 Then, differential expression genes (DEGs) analysis was performed for the “SNT” group versus the “Control” group with the use of the limma R package. |log2FC| > 1 and P-adjusted <0.05 were set as the cutoff criteria. Finally, the R language was utilized for result visualization. Heatmaps are graphical representations of data that represent each value as a color. Here, we used a heatmap to show the DEGs in NeP.
## KEGG and GO enrichment analysis
The KEGG and GO functional enrichment analysis were performed using the online tool Metascape (Zhou et al., 2019). The GO analysis included analyses of biological processes, cellular components, and molecular functions. The top 20 enriched entries were displayed, and the results were visualized with the R language. The p value indicates the significance of the enriched entries under the corresponding conditions. The pathway is more significant if the p value is lower.
## Protein–protein interaction network analysis and screening of hub genes
To evaluate the interrelationships among DEGs, we mapped the DEGs to String v11.5 (Szklarczyk et al., 2021). PPI networks were constructed using proteins encoded by the 421 differentially expressed mRNAs (DEmRNAs), which represent genes as nodes and interactions as lines. The disconnected nodes in the network were hidden. The key PPI network was screened using a plug-in, cytoHubba (Chin et al., 2014), in Cytoscape 3.9.0 software 3 (Shannon et al., 2003). The top 10 nodes ranked by the MCC method were considered key PPI network nodes.
## Construction of miRNA-mRNA network and functional annotation
The miRNAs potentially binding to the top 5 hub genes in the key PPI network were predicted by three databases (TargetScan, miRWalk, and miRDB) (Agarwal et al., 2015; Sticht et al., 2018; Chen and Wang, 2020). The total miRNAs were then enriched for function to screen for key miRNAs, and experimental validation was performed. The network connecting target genes and differentially expressed miRNAs was visualized using the Cytoscape 3.9.0 tool.
## Construction of the circRNA-miRNA-mRNA ceRNA network
Considering the potential regulatory function of circRNAs in achieving the regulation of target gene expression by recruiting miRNAs, we predicted the potential targeting circRNAs of differentially expressed miRNAs using the online database starBase 3.0 (Li et al., 2014). The intersection of the prediction results of two key miRNAs was selected as key circRNA. Eventually, the circRNA-miRNA-mRNA network in NeP was established.
By intersecting the predicted results of two miRNAs, mmu-miR-181a-5p and mmu-miR-223-3p, both circLPHN3 and circARHGAP5 were found to be able to target the two miRNAs and they were identified as essential circRNAs accordingly. Among the 5 DEmRNAs, only IL6 was predicted by the databases to target both mmu-miR-181a-5p and mmu-miR-223-3p. As a result, 2 miRNAs target a key DEmRNA (IL6), and 2 circRNAs further target 2 DEmiRNAs, forming a circRNA-miRNA-mRNA network (Figure 10A). The binding sequence between target circRNAs and miRNAs was obtained by downloading from the starBase3.0 database (Figure 10B).
**Figure 10:** *The analysis of circRNA-miRNA-mRNA network. (A) The circRNA–miRNA-hub gene network. (B) The interaction of novel circRNAs/DEmiRNAs.*
## Animals
A total of 9 Adult male C57BL/6 mice (7–9 weeks old) were utilized in this study. Mice were caged at a temperature of 25 ± 1°C and followed a standard light/dark cycle of $\frac{12}{12}$ h, with water and food available ad libitum. All procedures involving animals were approved by the Experimental Animal Welfare Ethics Committee of Fujian Medical University. All efforts were made to reduce suffering as well as the number of experimental animals. All investigators conducting the experiments were blinded to the grouping of the experimental animals.
## Animal model of SNT
The SNT procedure was carried out as previously described (Hu et al., 2016). Briefly, mice were anesthetized using $1\%$ pentobarbital sodium (50 mg/kg), and the right sciatic nerve was exposed at the mid-thigh level and sectioned distally. Once the modeling was complete, each layer opened was carefully closed with sutures. After three days, the ipsilateral L3 and L4 DRGs, as well as the contralateral uninjured DRGs as controls, were harvested for subsequent processing (Larhammar et al., 2017).
## Behavioral tests
The paw mechanical withdrawal threshold (PMWT) was measured preoperatively and 3 days postoperatively. Mice were put in separate Plexiglas cells on a wire mesh floor and acclimated for 30 min prior to testing. Following the previous instructions (Dixon, 1980), Von Frey (Stoelting, Wood Dale, IL, United States) was applied to the ipsilateral and contralateral hind paw with the up-and-down method. The measurements of PMWT were undertaken according to a previously described approach (Chaplan et al., 1994). All behavioral studies were performed by an experimenter who was blinded to the group assignment.
## Validation of the hub genes and miRNAs by quantitative real-time PCR
After the behavioral test, the mice were euthanized by decapitation under deep anesthesia on the third day after the operation. The L3/L4 DRGs were separated and immediately frozen in liquid nitrogen. A 10 mg tissue sample was mixed in TRIzol Reagent (Takara, Kusatsu, Japan) to isolate total RNA from tissues. A Primer Script RT reagent kit (TaKaRa, Code No. RR047A, No. 638313) was used to reverse transcribe the RNA into cDNA using a PCR instrument (2,720 Thermal Cycler, ThermoFisher), which was then subjected to qPCR using the TB Green® Premix Ex Taq™ II (Tli RNase H Plus) and Mir-X TM miRNA First-Strand Synthesis Kit (TaKaRa, Code No. RR420A, No. RR820A) on a Roche LightCycler480 real-time PCR system. Five hub genes and two key miRNAs were selected for expression validation. Normalization of mRNA was achieved using GAPDH (Sangon, Shanghai, China) as an endogenous control gene, and U6 (Takara, No. 638313) was used as the internal reference gene of miRNA. The primer sequences were designed by Sangon Biotech (Sangon) and are listed in Table 1. The relative expression was calculated by the 2−ΔΔCT method.
**Table 1**
| Target | Sequence (5′ → 3′) |
| --- | --- |
| IL6 | F: GGAGTCACAGAAGGAGTGGC |
| | R: AACGCACTAGGTTTGCCGAG |
| Jun | F: CCAAGAACGTGACCGACGAG |
| | R: GCGTGTTCTGGCTATGCAGT |
| Cd44 | F: GGGTTTTGAAACATGCAGGTAT |
| | R: GTTGGACGTGACGAGGATATAT |
| Timp1 | F: GCAAAGAGCTTTCTCAAAGACC |
| | R: CTCCAGTTTGCAAGGGATAGAT |
| Csf1 | F: AACAGCTTTGCTAAGTGCTCTA |
| | R: ACTTCCACTTGTAGAACAGGAG |
| GAPDH | F: GTTACCAGGGCTGCCTTCTC |
| | R: GATGGTGATGGGTTTCCCGT |
| miR-223-3p | GCTGTCAGTTTGTCAAATACCCCA |
| miR-181a-5p | AACATTCAACGCTGTCGGTGAGT |
## Western blotting
On the 3rd day after SNT, the L3/L4 DRGs were collected and immediately frozen after the mice were sacrificed. The tissue was lysed in RIPA buffer containing phosphatase and protease inhibitors (Beyotime, Shanghai, China). A BCA protein assay kit (Beyotime) was used to determine the concentration of protein. The proteins were transferred to polyvinylidene fluoride membranes after gel electrophoresis and then incubated with primary antibodies at 4°C overnight after blocking with $5\%$ nonfat dry milk in TBS + Tween. The primary antibodies were as follows: rabbit anti-IL-6 (1:1000, ab6672, Abcam), rabbit anti-c-Jun (1:1000, Cat. No. D151652, Sangon), rabbit anti-Cd44 (1:500, ab51037, Abcam), rabbit anti-Timp1 (1:1000, ab109125, Abcam), rabbit anti-Csf1 (1:1000, ab233387, Abcam), and β-actin rabbit mAb (1:1000, AC026, Abclonal, Wuhan, Hubei, China), and were processed with goat anti-rabbit IgG (1:4000, AS014, Abclonal) at room temperature for 1 h. The signals of target proteins and β-actin were detected by a chemiluminescence detection system (Tanon4600, Shanghai, China). Relative protein expression levels were normalized using β-actin as an internal reference, and the band intensity was analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, United States).
## Transmission electron microscopy analysis
On the 3rd day after SNT, the right injured and contralateral undamaged L3/L4 DRGs of mice were extracted. The samples were fixed in $2\%$ glutaraldehyde, washed 3 times (15 min each time) in phosphate buffer (pH 7.4), postfixed in $1\%$ osmium tetroxide in phosphate buffer (pH 7.4) for 2 h, and dehydrated under increasing alcohol concentrations. After embedding in epoxy resin medium, tissue was sliced into semithin sections (approximately 2 μm), stained with azure methylene blue, cut into ultrathin slices (approximately 60 nm), and stained with osmic acid. The morphology of the DRG neuron cell body and myelin were observed by transmission electron microscopy (HT7700, Hitachi, Japan). The ultrastructural assessment of nerve fiber myelin is consistent with the previously reported grading system (Table 2; Kaptanoglu et al., 2002). Ten myelinated axons in three samples per group were evaluated by this grading system. Besides, three views were randomly selected in each case, and the number of autophagosomes in the DRG was recorded for statistical analysis.
**Table 2**
| Score | Category |
| --- | --- |
| Grade 0 | Normal |
| Grade 1 | Separation in myelin configuration |
| Grade 2 | Interruption in myelin configuration |
| Grade 3 | Honeycomb appearance |
| Grade 4 | Collapsed myelin-forming ovoids |
## Statistical analysis
Statistical analyses were performed using SPSS 21.0 (SPSS, Chicago, IL, United States) and GraphPad Prism 9 (GraphPad Software Inc., San Diego, CA, United States). Quantitative data are expressed as the mean ± SD. Student’s t test was used to analyze the comparison between two groups. The grade of myelin sheath damage was statistically analyzed using a chi-square test. A two-sided p value less than 0.05 was considered statistically significant.
## Identification of DEGs in NeP
The boxplot automatically generated by GEO shows the normalized data, and the distribution of values in the GSE96051 dataset is relatively consistent across all samples, indicating that the location and dispersion of the data meet the quality requirements (Figure 1A). Samples from the SNT group displayed a green color, and samples from the control group displayed a purple color. The expression density plots complement the boxplot in examining data normalization by visually depicting the distribution of gene expression in each sample before performing DEGs analysis. The intensity of genes was mainly between-2 and 2 with little variation between samples, indicating their consistency in respective datasets and ideal sample data quality (Figure 1B). Uniform manifold approximation and projection (UMAP) is a dimensionality reduction technique that can be used to visualize how samples are related to each other. The distance between points (sample to sample) can reflect the obvious difference between groups and the consistency within the group (Figure 1C). The volcano map shows DEGs in the two groups, with upregulated genes and downregulated genes marked in red and blue, respectively (Figure 1D). These DEGs are displayed in Supplementary Table S1. Heatmap shows hierarchical cluster analysis of DEmRNAs, including 332 upregulated genes and 89 downregulated genes. The color of genes from blue to red indicates a low or high level of gene expression (Figure 1E).
**Figure 1:** *Differentially expressed profile of mRNAs and characterization between the SNT group and the control. (A) Boxplot shows the distribution of the values of the DRG samples, abscissa represents sample name and ordinate represents the normalized intensity values, the upper and lower sides of the rectangular box mean minimum and maximum values, the upper and lower lines of the error bars mean interquartile range, the line inside the rectangular box means median value. (B) The density map of GSE96051 shows the expression of each sample. (C) The UMAP shows the distribution relationships between the samples. (D) Volcano plot of DEmRNAs. The vertical lines correspond to twice the fold changes in upregulation or downregulation; the horizontal line represents p-adjusted = 0.05; red points indicate mRNAs with upregulation and blue points indicate mRNAs with downregulation. (E) Hierarchical cluster analysis of DEmRNAs. Blue and red color indicate down-regulation and up-regulation of gene expression, respectively.*
## KEGG and GO enrichment analyses
KEGG functional analysis of DEmRNAs showed the top 17 representative enrichment pathways, including the MAPK signaling pathway, calcium signaling pathway, p53 signaling pathway, HIF-1 signaling pathway, and cytokine-cytokine receptor interaction (Figure 2A; Supplementary Table S2).
**Figure 2:** *Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses for DEmRNAs. (A) KEGG pathway analysis shows the significantly enriched pathways and their gene counts. (B–D) The GO enrichment results of DEmRNAs, including GO-BP (B), GO-CC (C), and GO-MF (D).*
Furthermore, we performed GO analysis of DEGs, and the enriched results were highly significant, including 131 entries enriched in biological process (BP), 36 entries in cellular component (CC) and 34 entries in molecular function (MF) (Figures 2B–D; Supplementary Table S2). For BP, DEmRNAs were mainly enriched in inflammatory and immune processes as well as endocrine metabolism processes. The inflammatory and immune processes included inflammatory response (GO:0006954), neutrophil chemotaxis (GO:0030593), cellular response to interleukin-1 (GO:0071347), macrophage chemotaxis (GO:0048246), cytokine-mediated signaling pathway (GO:0019221), positive regulation of tumor necrosis factor production (GO:0032760), immune system process (GO:0002376), etc. The metabolic processes included positive regulation of apoptotic process (GO:0043065), negative regulation of neuron death (GO:1901215), regulation of angiogenesis (GO:0045765), positive regulation of calcium ion import (GO:0090280), positive regulation of receptor-mediated endocytosis (GO:0048260), positive regulation of serotonin secretion (GO:0014064), positive regulation of chemokine production (GO:0032722), etc. For CC, almost all of the enriched terms were related to cellular metabolic processes, intracellular organelles and signal transmission, such as neuronal cell body (GO:0043025), glial cell projection (GO:0097386), neuron projection (GO:0043005), glutamatergic synapse (GO:0098978), *Golgi apparatus* (GO:0005794), axon cytoplasm (GO:1904115), and postsynaptic density (GO:0014069). Regarding MF, the enriched terms were almost all associated with protein binding, sequence-specific binding and ion channel activity, including chemokine receptor binding (GO:0048020), neuropeptide hormone activity (GO:0005184), calmodulin binding (GO:0005516), transcription factor activity (GO:0003700), sequence-specific DNA binding (GO:0043565), transmembrane transporter activity (GO:0022857), extracellular-glutamate-gated ion channel activity (GO:0005234), and ligand-gated ion channel activity (GO:0015276).
In summary, the involvement of intracellular and extracellular signaling pathways in immune, inflammatory and oxidative stress processes as well as endocrine metabolic processes may be closely related to the onset and development of NeP.
## Establishment of the protein–protein interaction network and identification of hub genes
The 421 DEGs (log2FC > 1) in NeP form a PPI network (Figure 3A) based on the STRING database, including 332 upregulated genes and 89 downregulated genes. The critical PPI network was established using Cytoscape, and the top 10 nodes ranked by the MCC algorithm were screened as hub genes: IL6, Jun, Cd44, Timp1, Csf1, Serpine1, Ccl7, Atf3, Lgals3, and Fcgr1 (Figure 3B). Figure 3C presents the interaction relationships of the 10 hub genes in the STRING database and the structures of the proteins they encode. Given that highly connected hub nodes have important functions in biological networks, the 10 hub genes were likely to play important roles in NeP, and the top 5 hub genes were selected for subsequent analysis and validation.
**Figure 3:** *The construction of protein–protein interaction network and identification of hub genes. (A) PPI network is established by 421 DEmRNA-encoded proteins using the STRING website. (B) Relationship network diagram of hub genes from PPI network using cytoscape. (C) Relationship network of 10 hub genes that extracted from the PPI network and the structure of proteins they encoded.*
## Western blot analyses of hub genes in the PPI network
The top five DEGs in the PPI network were selected to check their expression levels by Western blot, including IL-6, Jun, Cd44, Timp1, and Csf1. The results showed that the protein expression levels of IL-6, Jun, Csf1, Timp1, and Cd44 in the SNT group were significantly upregulated compared with those in the control group (all $p \leq 0.05$) (Figure 4). The result of Western blot is broadly in line with that of sequencing.
**Figure 4:** *Verification of the top 5 hub genes. (A) The expression of the IL6, Jun, Cd44, Timp1 and Csf1 in DRG tissues of mice induced by SNT and the control by Western blot. (B–F) The expression analysis of the IL6, Jun, Cd44, Timp1 and Csf1. Data are presented as means ± SD (n = 3; *p < 0.05).*
## Construction of the miRNA-mRNA-binding protein network and biological functional analysis
The miRNAs binding to the top 5 hub genes were predicted by three public databases (TargetScan, miRWalk and miRDB), and the intersections were used to determine the key miRNAs (Figure 5). In total, 7, 18, 43, 3, and 71 target miRNAs for IL6, Jun, Cd44, Timp1, and Csf1, respectively, were predicted (Figure 5; Supplementary Table S3). The miRNA Enrichment Analysis and Annotation Tool (miEAA) contributes to the functional analysis of target miRNAs (Kern et al., 2020). The results of KEGG analysis showed that these miRNAs were primarily enriched in pathways related to immune inflammation, oxidative stress, endocrine metabolism and neural signaling, including the chemokine signaling pathway, Toll-like receptor signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, neurotrophin signaling pathway, long-term potentiation and long-term depression. Notably, numerous autophagy-related entries were enriched, such as autophagy, endocytosis, lysosome, phagosome, and protein processing in endoplasmic reticulum (Figure 6A; Supplementary Table S4). Additionally, the results of GO functional enrichment indicate that these miRNAs are involved in the cyclic nucleotide biosynthetic process (GO0009190), mRNA binding (GO0003729), protein complex assembly (GO0006461), regulation of dopamine secretion (GO0014059), regulation of myelination (GO0031641), and regulation of axonogenesis (GO0050770), especially in the autophagic vacuole (GO0005776), phagocytosis (GO0006909), cytoplasmic vesicle membrane (GO0030659), and endoplasmic reticulum Golgi intermediate compartment (GO0005793) (Figure 6B; Supplementary Table S5).
**Figure 5:** *Construction of miRNA-mRNA network. (A–E) Overlap analysis for the miRNAs binding to the IL6, Jun, Cd44, Timp1, and Csf1 from the TargetScan, miRWalk, and miRDB database. (F) The miRNAs-hub genes network.* **Figure 6:** *Functional enrichment analysis of miRNAs. (A) KEGG pathways enriched by the shared miRNAs between 3 different databases, TargetScan, miRWalk, and miRDB. (B) The GO enrichment analysis shows the top 15 significant items and their gene counts.*
In summary, the strong correlation between the KEGG pathway and GO enrichment analysis suggests that potential DEmRNA-miRNA interactions may play a role in the development of NeP. Based on the remarkable prominence of mmu-miR-181a-5p and mmu-miR-223-3p in the functional enrichment results, we selected them for subsequent analysis and verification.
## Changes in pain threshold and DRG ultrastructure after SNT
As shown in the illustration (Figure 7A), the right sciatic nerve of the mice was exposed and cut off, resulting in hyperalgesia (Zhang et al., 2021). The baseline PMWT was measured preoperatively and at 3 days after the surgery (Figure 7B). Compared with the contralateral, ipsilateral hind paw showed a significantly lower pain threshold at 3 days after surgery ($p \leq 0.05$) (Figure 7C). The results suggested that mice undergoing SNT surgery exhibited NeP behaviors.
**Figure 7:** *Changes of pain threshold and DRG ultrastructure after SNT. (A) The right sciatic nerve of mice was exposed and transected. (B) Behavioral test of mice using Von Frey. (C) SNT induced reduction of PMWT in mice. (D) Electron microscope analysis of the ultrastructural changes in L3/L4 DRGs on the 3rd day after SNT in different groups. The morphology of neuronal somata and myelin sheaths of DRG neurons in two groups. The neuron nucleus is indicated by the black arrow, Scale bar = 10 μm; magnification ×1,000; The myelin sheath is indicated by the white arrow, Scale bar = 5 μm; magnification ×2,000. (E) Representative electron microscope views of autophagosomes (yellow arrow) in DRG of the two groups. Scale bar = 1 μm; magnification ×12,000. (F) The number of autophagosomes of the two groups in per vision was determined on the 3rd day after SNT. Data are presented as means ±SD (n = 3; *p < 0.05).*
Further observation of the ultrastructure of the myelin sheath and cell body by electron microscopy revealed that SNT induced degeneration of a proportion of nerve fibers in the DRG. Compared to those of the control, myelin damage grading scores were significantly higher in the SNT group (Table 3) ($p \leq 0.05$), accompanied by structural abnormalities of the cell body (Figure 7D). In particular, DRG neuronal soma was regular with homogeneous cytoplasm and uniform and loose chromatin in the control group. The myelin sheath of nerve fibers was arranged in concentric circles with distinct layers. In the SNT group, the soma and myelin sheath of DRG were severely damaged with nerve demyelination, and partial myelin lamellar structures were completely destroyed on the 3rd day after SNT. Specifically, the neuronal soma displayed cytoplasmic shrinkage or swelling, nuclear pyknosis and displacement, and heterochromatin aggregation, and the membrane of cells was obscure with slightly enlarged space. The nerve medullary sheath had an unclear layer structure characterized by axis cylinder destruction even with a honeycomb-like appearance and fractured loose layers. The above results suggested that SNT does not cause axonal degeneration in the contralateral DRG, whereas it induces axonal injury in the ipsilateral DRG.
**Table 3**
| Score | Control | SNT |
| --- | --- | --- |
| Grade 0 | 21 | 3 |
| Grade 1 | 9 | 5 |
| Grade 2 | 0 | 8 |
| Grade 3 | 0 | 10 |
| Grade 4 | 0 | 4 |
Considering that in addition to axon-related entries, autophagy-related entries were also abundantly enriched in GO and KEGG, we further observed alterations in DRG autophagy after SNT by electron microscopy and discovered that the number of autophagosomes in DRG was increased significantly in the SNT group compared to the control group (Figures 7E,F) ($p \leq 0.05$).
These results not only suggest that the biological process of autophagy and DRG ultrastructural damage are involved in NeP but also illustrate the accuracy of the analysis at the ultrastructural level.
## Confirmation of differentially expressed mRNAs and miRNAs by qRT-PCR
The top 5 hub genes and 2 key miRNAs were selected to conduct qRT-PCR on independent samples to check the reliability of the RNA-sequencing data ($$n = 3$$ per group, Figure 8). The results showed that the expression of both targeting miRNAs was significantly lower in the SNT group than in the control group (Figure 8) (all $p \leq 0.05$). The quantitative PCR findings matched the results of RNA sequencing (Table 4).
**Figure 8:** *Amplification and identification of differentially expressed mRNAs and miRNAs. (A–I) The melt curves of the identified mRNAs and miRNAs. (J–P) The results of qRT-PCR show the expression levels of DEGs between the two groups. Data are presented as means ± SD ($$n = 3$$; *$p \leq 0.05$).* TABLE_PLACEHOLDER:Table 4
## Prediction of differentially expressed miRNAs
Potential target circRNAs were identified based on predictive analysis of circRNA-miRNA interactions, and relevant ceRNA regulatory networks were constructed (Figure 9A; Supplementary Table S6). Noticeably, the prediction results showed that circLPHN3 and circARHGAP5 could target key DEmiRNAs mmu-miR-181a-5p and mmu-miR-223-3p. Accordingly, circLPHN3 and circARHGAP5 were identified as important circRNAs. The structural pattern graphs of the 2 significant circRNAs in the ceRNA network were drawn by the CSCD database (Xia et al., 2018), and they can be used to predict miRNA response elements, RNA binding proteins, and open reading frames to further investigate the potential functions of the circRNAs (Figure 9B).
**Figure 9:** *The analysis of circRNA-miRNA networks. (A) The competing endogenous RNA networks of DEmiRNAs. (B) Structural patterns of circLPHN3 and circARHGAP5. The colored circle represents the circRNAs that consist of exons. The numbers on the circRNAs mean the exon number. The red,blue, and green regions inside the circRNA molecule, respectively represent MRE (microRNA response element), RBP (RNA binding protein), and ORF (open reading frame).*
## Discussion
As a widespread chronic disease, NeP has a complex clinical presentation and a long course and is often combined with sleep disorders, anxiety and depression. NeP severely reduces the quality life of patients and imposes a significant economic burden on society (Wu et al., 2019). The exact mechanism underlying the role of ceRNAs in NeP is not yet clear. Analysis of the circRNA-miRNA-mRNA expression profile may provide new insights into the pathophysiology of NeP. In previous studies, several research groups have identified dysregulated miRNAs or long noncoding RNAs (lncRNAs) in the DRGs of mice with different NeP models using deep RNA-seq analysis (Mao et al., 2018; Jia et al., 2020). However, little is known about the systematic study of circRNAs, especially the role of the circRNA-miRNA-mRNA regulatory network in NeP.
At present, the DRG is considered to be a key structure in sensory transmission and modulation, including pain transmission and maintenance of a persistent neuropathic pain state. The unique properties of the DRG, including selective somatic cell organization, specific membrane properties and an easily accessible and consistent location, make it an ideal target for neuromodulation (Esposito et al., 2019). In this study, we used bioinformatics to identify potential DRG biomarkers for NeP by constructing related ceRNA networks to explore their possible molecular regulatory mechanisms. We identified 421 DEmRNAs (log2FC > 1) and 2 miRNAs (mmu-miR-181a-5p and mmu-miR-223-3p) in response to SNT model building. A few studies have reported the involvement of miR-181a-5p in several neurological disorders. Mechanistically, miR-181a-5p inhibition regulates cell survival in neurons and astrocytes after forebrain ischemia and stroke (Arvola et al., 2019), and lncRNA SNHG1 promotes neuronal injury in a Parkinson’s disease cell model via the miR-181a-5p/CXCL12 axis (Wang K. et al., 2021). However, the involvement of miR-181a-5p in the pathogenesis of NeP has not yet been reported. Encouragingly, limited research has preliminarily revealed the involvement of miR-223-3p in NeP. For example, trigeminal NeP can be alleviated by miR-223-3p targeting MKNK2 and MAPK/ERK signals in male mice (Huang et al., 2022). Another study also reported that electroacupuncture inhibits autophagy in neuronal cells by increasing the expression of miR-223-3p in postherpetic neuralgia (Zou et al., 2021). In addition, one clinical study identified that miR-223-3p in cerebrospinal fluid was significantly lower in fibromyalgia patients than in healthy controls (Bjersing et al., 2013). Notably, the analysis of this study revealed that the target miRNAs and IL6 and their strong correlations may play a role in NeP, which is consistent with the results of a previous study (Hori et al., 2016). For example, intrathecal injection of miR-214-3p can reverse enhanced CSF1 expression and astrocyte overactivity and alleviate the IL-6 upregulation and pain behavior in in rats with spinal nerve ligation (Liu et al., 2020). Besides, another key DEG, Jun, is an oncogene that can activate the cAMP pathway, has been demonstrated that Jun complex promoted the progression of NeP via JNK pathway (Xu C. et al., 2022). These results suggest that the miRNA-mRNA network may play an important regulatory role in NeP.
CircRNAs are a type of noncoding, regulatory RNAs that exhibit tissue-specific and disease-specific expression. An increasing number of studies have reported that circRNAs may play pivotal roles in the development of NeP (Xu D. et al., 2021). Therefore, we further constructed circRNA-miRNA ceRNA networks to demonstrate their interactions, and circRNAs (circLPHN3 and circARHGAP5) predicted by both mmu-miR-181a-5p and mmu-miR-223-3p may play an essential role in the regulatory networks. Here, we suggest that the role and function of circRNAs as ceRNAs in the DRG of the NeP model are worth further investigation.
KEGG and GO analysis based on the above DEmRNAs and corresponding potential binding miRNAs showed similar results. The enriched KEGG pathways were related to immune inflammation, oxidative stress, endocrine metabolism and neural signaling. Immune inflammation reactions (Hu et al., 2020), oxidative stress (Xu J. et al., 2021), genomic metabolic analysis (Xu Z. et al., 2022) and neural signaling (Chen et al., 2021) in the DRG have previously been associated with pathogenesis in NeP. Similar to KEGG, the results of the GO analysis focused on cellular processes and bioregulation, including regulation of axonogenesis (GO0050770), autophagic vacuole (GO0005776), nucleotide biosynthetic process (GO0009190), mRNA binding (GO0003729), ligand-gated ion channel activity (GO:0015276), etc. These results further confirm the regulatory role of miRNA-mRNA networks in NeP.
Considering that the above DEmRNAs and corresponding potential binding miRNAs for KEGG and GO functional analysis were abundantly and significantly enriched in the entries related to autophagy and axons. We further observed the changes in DRG ultrastructure and autophagosomes after SNT by electron microscopy. The results imply that the SNT could induce a certain degree of nerve fiber demyelination (Dun and Parkinson, 2018), which not only demonstrates the accuracy of functional enrichment analysis but also provides credibility to the modeling. In addition, behavioral observation is consistent with a previous study showing that the SNT induces mechanical hyperalgesia (Manassero et al., 2012). Autophagy has been proved to participate in various biological processes of diseases, including NeP. In recent years, increasing evidence has shown that ceRNAs influence the course of a disease by regulating many genes involved in autophagy, suggesting that autophagy is involved in the onset and progression of various diseases and can affect drug resistance (Cai W. et al., 2020; Cai L. et al., 2020; Zou et al., 2021; Wang et al., 2022). In this study, a significant increase in the number of autophagosomes was observed in the SNT-induced NeP mice, which may indicate that the mechanism of ceRNA involvement in NeP may be associated with the autophagic pathway.
Most of the previous studies on the mechanism of NeP were based on animal models, however, these studies did not systematically describe the changes in DRG that occur in NeP, which may be an important obstacle for DRG-related treatment and research. To the best of our knowledge, this is one of the few studies to reveal the potential mechanism of NeP by integrating the analysis of mRNA, miRNA and circRNA in DRG. The circRNA-miRNA-mRNA regulatory network constructed in this study will contribute to further understanding of the involvement of DRG in the pathogenesis of NeP. This study still has some limitations. First, we only used the sequencing results of 1 dataset due to limited data, and it may be possible to reduce the variation in sequencing results and make the analysis more convincing if multiple sequencing results were applied and intersected. Second, we only observed the SNT as a NeP model, and whether there are inconsistencies in the ceRNA regulatory network for different model conditions needs further investigation. Finally, the expression level of circRNA was not determined, and the regulatory analysis of the ceRNA network can be further verified and comprehensively explored by experimental methods such as gene overexpression, gene knock-out and dual-luciferase reporter assays in the future.
In conclusion, we constructed a ceRNA network associated with miRNAs and circRNAs to identify potential mechanisms of NeP. Our findings suggest that specific miRNAs and circRNAs may help explore candidate targets and new molecular biomarkers for NeP therapy. The results of this study provide preliminary confirmation that the novel circLPHN3/circARHGAP5_mmu-miR-223-3p/mmu-miR-181a-5p_IL6 networks may regulate the pathophysiology of NeP by affecting multiple signaling pathways. These newly identified networks and genes in the signaling pathway reveal potential diagnostic and therapeutic targets for NeP. However, whether these associations contribute to the development of NeP remains to be further studied.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://www.ncbi.nlm.nih.gov/gds, and accession number is GSE96051.
## Ethics statement
The animal study was reviewed and approved by the Animal Ethics Committee of Fujian Medical University.
## Author contributions
YY and XX completed the experiments, analyzed the data, and wrote the manuscript. RL conceived the study, obtained funding, and critically revised the manuscript. CL offered lab instruments, participated in data analysis, and the revised version. All authors read and approved the final manuscript.
## Funding
This work was supported by the Project of Medical Innovation of Fujian Province, China (2018-CX-6) and the Startup Fund for Scientific Research of Fujian Medical University, China (2019QH1151).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2023.1119164/full#supplementary-material
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|
---
title: Acute effects of fresh versus dried Hayward green kiwifruit on sleep quality,
mood, and sleep-related urinary metabolites in healthy young men with good and poor
sleep quality
authors:
- Alexander P. Kanon
- Caroline Giezenaar
- Nicole C. Roy
- Warren C. McNabb
- Sharon J. Henare
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043399
doi: 10.3389/fnut.2023.1079609
license: CC BY 4.0
---
# Acute effects of fresh versus dried Hayward green kiwifruit on sleep quality, mood, and sleep-related urinary metabolites in healthy young men with good and poor sleep quality
## Abstract
### Background and aims
Daily kiwifruit (KF) consumption has been associated with improved sleep quality, but underlying physiological mechanisms are unknown. This study examined acute effects of fresh and dried green KF, compared with a water control, on sleep quality, mood, and urinary serotonin and melatonin metabolite concentrations.
### Methods
24 men (age: 29 ± 1 years, body mass index: 24 ± 1 kg/m2) with poor ($$n = 12$$) or good ($$n = 12$$) sleep quality participated in a randomized, single-blind crossover study. One of three treatments was consumed with a standardized evening meal; [1] the flesh of two fresh green KF, [2] dried green KF powder (including skin; equivalent to dry matter of two fresh KF) mixed with water, or [3] a water control, in their own home. Subjective and objective sleep quality, mood, waking urinary 5-hydroxyindoleacetic acid (5-HIAA), 6-sulfatoxymelatonin (aMT6s), vitamin C and B-vitamin concentrations were determined.
### Results
Regardless of sleep quality group, compared to control, morning sleepiness, alertness upon awakening, and vigor were improved ($p \leq 0.05$) after dried KF consumption. Compared to control, both fresh and dried KF treatments tended ($p \leq 0.1$) toward improved esteem and total mood disturbance. Both KF treatments increased (fresh +1.56 ± 0.4 ng/g, $$p \leq 0.001$$; dried: +1.30 ± 0.4 ng/g, $$p \leq 0.004$$) urinary concentration of the serotonin metabolite 5-HIAA compared to the control (4.32 ± 0.4 ng/g). In poor sleepers, ease of awakening improved by $24\%$ after dried KF consumption ($$p \leq 0.005$$) and tended to improve by $13\%$ after fresh KF intake ($$p \leq 0.052$$) compared to the control. Good sleepers tended toward $9\%$ improved ratings of getting to sleep with fresh KF ($$p \leq 0.053$$) compared to the control. Poor sleepers had lower amounts of some B-vitamins compared to good sleepers ($p \leq 0.05$).
### Conclusion
Consumption of dried or fresh KF with a standard evening meal, was associated with improved aspects of sleep quality and mood, possibly mediated through changes in serotonin metabolism.
### Clinical trial registration
[www.anzctr.org.au], identifier [ACTRN12621000046808].
Graphical Abstract
## Introduction
Sleep is essential for the cellular repair of the body. Inadequate sleep is associated with increased health risks such as inflammation, diabetes, hypertension, and obesity. ( 1–5). Insufficient sleep can also lower cognitive performance and cause mood changes [6]. One night of sleep disturbance can affect a person’s ability to concentrate; increasing technical errors and their overall mood the following day (7–9). Sleep quality is an assessment of a person’s contentment with their sleep period. This includes measures of one’s sleep duration, latency, efficiency, and waking after sleep. Good sleep quality has positive effects including improved daily function and feeling rested. Poor sleep quality outcomes include increased irritability and fatigue [10].
The circadian clock in the hypothalamus, which is affected by the light and dark cycles, controls sleep–wake cycles in humans. The neurohormone melatonin is secreted from pinealocytes in the pineal gland, and its precursor serotonin plays a vital role in regulating this. The urinary melatonin metabolite 6-sulfatoxymelatonin (aMT6s) is related to subjective and objective sleep quality measures [11] and the urinary concentration of the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) is related to mood [12]. Moreover, first-morning urine concentration of aMT6s accurately reflected peak and total nocturnal plasma melatonin secretion [13].
The consumption of fruit and vegetables is known to affect plasma and urinary melatonin and serotonin metabolite concentrations. Consumption of vegetables (sweet corn, bitter gourd, Japanese radish sprout, shumeji and shiitake mushrooms) [14], tomatoes [15], and cherries [16], which are rich in melatonin, increased morning urinary aMT6s concentration (14–16) and improved sleep quality (15–17). Consumption of serotonin-rich fruits, such as kiwifruit (KF) [18] and a Jerte Valley cherry-based product [19] increased urinary levels of 5-HIAA [19, 20] and mood [19]. Additionally, vitamin C is a co-factor involved in catecholamine biosynthesis and reducing oxidative stress. Studies have also shown increased urinary levels of vitamin C when consuming vitamin C-rich foods. B-vitamins are also important co-factors involved in catecholamine biosynthesis. Furthermore, daily consumption of two green KF 1 h before bed for 4 weeks was associated with improved sleep quality [21]. KF is high in serotonin, vitamin C and B-vitamins [22]. However, the relationships between biochemical measures and sleep quality with KF consumption are unknown.
The aim of this study was to determine the effect of consuming the flesh of two fresh green KF (without the skins), whole freeze-dried green KF (with the skins included; equivalent to two green KF), or a water control with a standardized evening meal on urinary concentrations of metabolites of melatonin, serotonin, vitamin C and B-vitamins as well as objective (actigraphy) and subjective sleep quality and mood measures in healthy young men. We hypothesized that sleep quality and mood would improve and urinary metabolite concentrations would increase after the KF interventions compared to the control intervention.
## Participant recruitment and screening
Twenty-four healthy men (eligibility: age 18–35 years, Body Mass Index (BMI) 18.5–30 kg/m2) were recruited by advertisement from the Massey *University campus* and community in Palmerston North, Manawatu, New Zealand, between January 2021 and May 2021 amid the COVID-19 pandemic. The Pittsburgh Sleep Quality Index (PSQI) was used to determine participants subjective sleep quality. Participants were classified as a ‘good sleeper’ when their global PSQI score was ≤ 5 and as a ‘poor sleeper’ when their global PSQI score was > 5 [23]. Exclusion criteria included smoking, excessive alcohol intake of > 21 standard drinks per week, use of prescribed or non-prescribed medications and antibiotics, physically active for more than 2 hours a day, food intolerances and allergies, consuming a vegan/vegetarian diet, gastrointestinal disorders; chronic conditions, such as cardiorespiratory, diabetes mellitus, high cholesterol/blood pressure; psychiatric conditions; diagnosed sleep conditions and working night shift or irregular work hours. In addition, participants who experienced significant weight loss (>$5\%$) 3 months prior to the start of the study or consumed strict diets were excluded.
The study protocol was approved by the Massey University Human Ethics Committee (Massey University HEC: Southern A application-$\frac{20}{52}$), and the study was conducted according to the guidelines in the Declaration of Helsinki. The study was registered as a clinical trial with the Australian New Zealand Clinical Trial Registry.1 All participants provided written informed consent before the clinical trial and could withdraw at any time for any reason.
## Study design, intervention, and protocol
The intervention was a randomized, single-blind, cross-over study. The study involved two green KF interventions of different forms and a control. The two KF interventions were: [1] flesh of two fresh green KF (*Actinidia deliciosa* cv. Hayward) (flesh only; approximate 200 g), [2] freeze-dried whole (flesh and skin), green KF (32 g, equivalent to the dry matter of two fresh green KF) with 200 ml of water, or [3] a control of 200 ml of water. The nutritional composition of each treatment is shown in Table 1. The researcher administering and analyzing the data (AK) was blinded to treatment allocation until completion of analyzes, and a separate researcher (CG) was unblinded and responsible for preparing the interventions. The researcher preparing the interventions was not involved in analyzing the data.
**Table 1**
| Nutrient | Quantity per serve | Quantity per serve.1 | Quantity per serve.2 |
| --- | --- | --- | --- |
| Nutrient | ≈200 g fresh kiwifruita | 32 g freeze-dried whole green kiwifruit powder with 200 ml waterb | 200 ml water |
| Energy (kcal) | 95.00 | 96.00 | 0 |
| Carbohydrates | Carbohydrates | Carbohydrates | Carbohydrates |
| Carbohydrate, total (g) | 18.20 | 21.30 | 0 |
| Sugars | Sugars | Sugars | Sugars |
| Sugar, total (g) | 17.60 | 20.77 | 0 |
| Fructose (g) | 9.40 | 10.30 | 0 |
| Galactose (g) | 0.00 | 0.03 | 0 |
| Glucose (g) | 8.20 | 8.93 | 0 |
| Lactose Anhydrous (g) | 0.00 | 0.03 | 0 |
| Lactose monohydrate (g) | 0.00 | 0.03 | 0 |
| Maltose (g) | 0.00 | 0.03 | 0 |
| Sucrose (g) | 0.00 | 1.41 | 0 |
| Dietary fiber, total (g) | 6.00 | 4.16 | 0 |
| Fats | Fats | Fats | Fats |
| Fat, total (g) | 1.40 | 0.61 | 0 |
| Minerals | Minerals | Minerals | Minerals |
| Calcium (mg) | 54.00 | 41.92 | 0 |
| Iron (mg) | 0.44 | 0.26 | 0 |
| Potassium (mg) | 602.00 | 550.40 | 0 |
| Magnesium (mg) | 28.00 | 18.24 | 0 |
| Sodium (mg) | 4.00 | 6.05 | 0 |
| Zinc (mg) | 0.20 | 0.16 | 0 |
| Protein and amino acid | Protein and amino acid | Protein and amino acid | Protein and amino acid |
| Protein (g) | 2.40 | 1.31 | 0 |
| Tryptophan (mg) | 100.00 | 43.10 | 0 |
| Vitamins Water Soluble | Vitamins Water Soluble | Vitamins Water Soluble | Vitamins Water Soluble |
| Vitamin B12 | 0.00 | 0.02 | 0 |
| Vitamin B3 (mg) | 1.66 | 0.40 | 0 |
| Vitamin B6 (mg) | 0.14 | 0.36 | 0 |
| Vitamin B9 (mg) | 76.00 | 48.00 | 0 |
| Vitamin C (mg) | 170.20 | 98.56 | 0 |
| Vitamins Fat Soluble | Vitamins Fat Soluble | Vitamins Fat Soluble | Vitamins Fat Soluble |
| Vitamin E (mg) | 1.72 | 2.69 | 0 |
| Other | Other | Other | Other |
| Ash (g) | 1.40 | 1.18 | 0 |
| Water (g) | 167.00 | 200.00 | 200.00 |
| Total phenolic content (mg GAE)b | 168.30** | 217.60** | 0 |
Randomization was conducted using a 6 × 3 Williams Design balanced for the order of presentation and carryover effects. The trial CONSORT flow diagram is shown in Figure 1. The study was conducted in the participant’s home environment. Before starting the study evenings, a familiarization evening occurred one to three days before the first evening. The same procedure described below was followed without providing a standardized evening meal and intervention.
**Figure 1:** *Consolidated Standards of Reporting Trials (CONSORT) flow diagram of the recruitment, enrolment, and random assignment process. DKF (Dried KF—32 g freeze-dried green KF powder), FKF (fresh KF—approx. 200 g fresh green KF).*
Participants were studied on three separate occasions, separated by six to eight days. Each participant consumed an intervention with a standardized evening meal [Pub Size Spaghetti and Meatballs (McCain Foods), ∼720 kcal, P 26.5 g, C 75.0 g, F 33.5 g] Enrolled participants were asked to refrain from eating KF from enrolment until the completion of the study period. On the day of the study, participants were asked to collect a data collection pack (Figure 2). The pack contained the evening meal, an intervention they were randomized to receive, an actigraphy watch, a urine sample collection container, and a survey booklet. It was confirmed that the participant refrained from consuming any restricted foods during the two days prior to their study day. The restricted foods included oranges, pineapples, bananas, mangos, papayas, plums, grape, cherries, strawberries, tomatoes, capsicum, pistachios, plantains, mushrooms, chocolate, teas, coffee, and caffeinated beverages as they are known to contain and alter levels of serotonin and melatonin in urine [20, 25, 26].
**Figure 2:** *Study protocol for each intervention. At approximately 6 pm, the participant consumed a standardized evening dinner followed by one of three interventions. Before bedtime, at approximately 10–11 pm, the participant was asked to rate their sleepiness. Upon waking the next morning, a urine sample was collected, and the survey booklet was completed. The sample was delivered to the laboratory within 2 h of collection.*
The participant consumed the standardized evening meal 4 h before their usual sleep time, followed immediately by the allocated intervention taking note of the time. Participants were asked not to eat any food or drinks, except water, until the following morning. Immediately before going to bed, participants were asked to rate their sleepiness level using the Stanford Sleepiness Scale (SSS). The participant was asked to activate the phase marker on the actigraphy watch when they were in bed and ready to sleep. Upon waking the following day, participants were asked to activate the phase marker, collect the whole first-morning urine sample noting the time, and complete a set of surveys including another SSS, Leeds Sleep Evaluation Questionnaire (LSEQ), and the abbreviated Profile of Mood States (POMS). The urine sample and surveys were delivered to the laboratory within 2 h of waking (Figure 2). Arrangements were made if the participant could not deliver samples within this time.
## Subjective measures for sleep quality and mood
Two questionnaires to measure subjective sleep quality were used. The SSS measures sleepiness, consisting of a one-item scale; the participants select one of seven statements that best represent their current sleepiness. For example, a score of one indicates feeling active, vital, and alert, while a score of seven indicates no longer fighting sleep, sleep onset soon [27]. The LSEQ is ten Visual Analog Scales (VAS) as a subjective self-measure to assess sleep quality changes throughout psychopharmacological treatment interventions [28]. The scale evaluates four domains of sleep: (i) ease of getting to sleep (mean of questions 1, 2 and 3), (ii) quality of sleep (mean of questions 4 and 5), (iii) ease of awakening from sleep (mean of questions 6 and 7), and (iv) alertness upon awakening (mean of questions 8, 9, and 10). A 100 mm VAS scale was used for scoring, and scores were averaged per domain to determine the domain score. Higher scores indicate a better sleep quality domain.
Mood was assessed using the abbreviated POMS questionnaire [29]. This form contains 40 mood-related adjectives rated on a 5-point Likert-type scale, ranging from 0 (not at all) to 4 (extremely). The data is then categorized into seven mood scales (maximum scores indicated): (i) tension, (ii) depression, (iii) anger, (iv) fatigue, (v) confusion, (vi) vigor, and (vii) esteem. Finally, a Total Mood Score (TMD) was calculated by adding tension, depression, anger, fatigue, and confusion scores, then subtracting the vigor and esteem scores.
## Objective sleep measures
Participants were provided with an Actiwatch Spectrum Plus (Philips Respironics, Murrysville, Pennsylvania, United States) to wear on the wrist of their non-dominant arm during the study evenings. The medium threshold setting was used due to its use in studies using actigraphy to assess sleep [30], and 30 s was selected as the epoch length. The data was downloaded and subject to a mathematical algorithm in Actiware 6.0.9 (Philips Respironics, Murrysville, Pennsylvania, USA) to quantify sleep onset latency, sleep efficiency, total sleep time, wake after sleep onset, number of awakenings and the average length of awakenings.
## Biochemical measures
Urine samples were collected in 500 ml containers with 1.0 g of ethylenediaminetetraacetic acid (EDTA). Participants were provided with two containers. Upon arriving at the laboratory, urine samples were weighed, and their volume was recorded. The urine was aliquoted into 1.5 ml Eppendorf tubes and stored at -80°C until analysis of aMT6s and vitamin C. Ten milliliters of urine were acidified with 6 M HCL and aliquoted and stored at -80°C until analysis of 5-HIAA. Urinary aMT6s and 5-HIAA analyzes were analyzed according to the manufacturer’s instructions using an enzyme-linked immunosorbent assay (ELISA) kit (IBL International, Hamburg, Germany). Assays were performed in duplicate and averaged. The average intra-assay coefficient of variance for the aMT6s assay was $5.2\%$, and the inter-assay was $2.9\%$. The average intra-assay coefficient of variance for the 5-HIAA assay was $7.5\%$, and the inter-assay was $3.5\%$. The ascorbate content of the samples was determined by reverse-phase high-performance liquid chromatography (HPLC) with electrochemical detection [31]. B-vitamins and vitamers were measured using high-performance liquid chromatography coupled with mass spectrometry (UHPLC–MS/MS) technique [32]. The B-vitamins measured included pantothenic acid, 4-pyridoxic acid, nicotinic acid, nicotinamide, nicotinuric acid, pyridoxal, biotin, riboflavin, folic acid, pyridoxamine and thiamine. The concentrations of measured metabolites and vitamins were corrected to creatinine to adjust for variation in urine dilution. Creatinine was measured using the colorimetric Jaffe method [33] (Nutrition Laboratory, Massey University).
## Data and statistical analysis
A power analysis was performed using ease of getting to sleep VAS from LSEQ as the dependent variable to estimate the required sample size. Estimates of variance components were conducted based on data from a study examining caffeine ingestion’s effect on VAS getting to sleep in healthy subjects [34]. When there are three treatments and a sample size of two in each of the six sequences, a 6 × 3 Williams Crossover inequality test of paired differences will have $94.2\%$ power to detect a minimum difference of 20 mm or greater, assuming that the standard deviation of the paired differences is 13 mm [34] at the $5\%$ significance level. The Bonferroni adjustment was used to keep the family-wise error at the specified error level. Thus, with the total number of pairwise comparisons equal to 3, each pairwise test was at the two-sided $1.67\%$ significance level.
Statistical analyzes were performed using SPSS software (version 25; IBM, Armonk, NY, United States). No outliers for any outcome measures were noted by examination of studentized residuals for values greater than ±3. A paired t-test was used to compare nutritional compositional and demographic data. Effects of sleeper type and treatment and their interaction effect were determined using a repeated measures mixed-effects model. An unstructured covariance structure was used to account for the repeated treatments by the subject. Post hoc comparisons, adjusted for multiple comparisons using Bonferroni’s correction, were performed when there were significant main or interaction effects. Residual plots were inspected to confirm that the normality and constant variance model assumptions were met. Statistical significance of the mixed effects models was accepted at a probability inferior to 0.05 ($p \leq 0.05$). A trend was noted at a probability lower than 0.10 but higher than 0.05. To assess the within-subject correlations between urinary metabolites and sleep quality and mood measures, a univariate model with sleep and mood measures as the dependent variable, urinary metabolites as the covariate, and subject as the fixed factor were performed [35]. Correlations with $p \leq 0.05$ and R-value > ± 0.4 were considered significant [36]. All data are presented as means ± standard errors of the mean (SEM).
## Participants
Twenty-four young men (18–35 years) completed the study: 12 with good sleep quality (age: mean ± SEM: 29 ± 0.9 years; body weight: 79.3 ± 2.8 kg; BMI: 24.4 ± 0.6 kg/m2) and 12 with poor sleep quality (29 ± 1.2 years; 76.4 ± 2.2 kg; 24.3 ± 0.7 kg/m2). The demographics between the groups were similar except for PSQI which was significantly lower in good sleepers (3 ± 1) compared to poor sleepers (8 ± 2, $$p \leq 0.005$$) (Table 2). Main effects of sleeper type were identified for evening sleepiness (SSS) [F[2, 22] = 5.71 $$p \leq 0.026$$], getting to sleep (LSEQ) [F[2, 22] = 4.57 $$p \leq 0.044$$] and quality of sleep (LSEQ) [F[2, 22] = 7.91 $$p \leq 0.010$$]. Regardless of treatment, good sleepers rated themselves sleepier in evening (mean ± SEM across treatments; 4.4 ± 0.3) compared to poor sleepers (3.4 ± 0.3; post hoc $$p \leq 0.026$$). Good sleepers rated themselves as harder getting to sleep and had worse sleep quality (mean ± SEM across treatments; 47.8 ± 2.6, 46.6 ± 2.7 respectively) compared to poor sleepers (55.6 ± 2.6; post hoc $$p \leq 0.044$$, 57.2 ± 2.7; post hoc $$p \leq 0.010$$). All subjects completed and tolerated the study protocol. All participants had not been diagnosed with COVID-19.
**Table 2**
| Characteristics | Whole group (n = 24) | Poor sleeper (n = 12) | Good sleeper (n = 12) | Unnamed: 4 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| Age, y | 29 ± 4, 19–34, 28.5 (2) | 29 ± 3, 19–34, 28 (5.3) | 29 ± 3, 24–34, 28.5 (2) | | |
| Body weight, kg | 29 ± 4, 19–34, 28.5 (2) | 29 ± 3, 19–34, 28 (5.3) | 77.8 ± 8.7, 77.2 (12.2) | 76.4 ± 7.7, 76.6 (6.3) | 79.3 ± 9.8, 79.8 (14.8) |
| BMI, kg/m2 | 24.3 ± 2.3, 24.1 (2.9) | 24.3 ± 2.5, 24.2 (2.3) | 24.4 ± 2.2, 24.1 (2.9) | | |
| PSQI Score | 5 ± 3, 1–12, 3 (1.3) | 8 ± 2, 6–12, 7 (2.5)*** | 3 ± 1, 1–5, 3 (1.3) | | |
| Daily screen time, hours | 8.0 ± 3.6, 2–14, 6 (5.1) | 9.1 ± 3.9, 2.5–14, 10 (5.75) | 8.0 ± 3.6, 3–12.5, 6 (5.13) | | |
| Daily caffeine intake, cups | 1.7 ± 1.5, 0–5, 1.0 (1.75) | 2.0 ± 1.6, 0–5, 1.5 (2.13) | 1.4 ± 1.3, 0–4, 1.0 (1.63) | | |
| Ethnicity (n) | Ethnicity (n) | Ethnicity (n) | Ethnicity (n) | | |
| European | 15 | 8 | 7 | | |
| Asian | 3 | 0 | 3 | | |
| Indian | 3 | 1 | 2 | | |
| African | 1 | 1 | 0 | | |
| Latin American | 2 | 2 | 0 | | |
## Sleep measures
Table 3 shows a summary of subjective sleep measures. Analyzes determined interaction effects of treatment by sleeper type for ease of getting to sleep (LSEQ) [F[2, 22] = 4.81, $$p \leq 0.018$$] and ease of awakening following sleep (LSEQ) [F[2, 22] = 3.85, $$p \leq 0.037$$]. Within good sleepers, ratings of ease of getting to sleep tended to increase after the fresh KF treatment (54.1 ± 4.1) compared to the control (45.1 ± 3.4; post hoc $$p \leq 0.053$$; Figure 3A). Within poor sleepers, ratings of ease of waking were higher after the freeze-dried KF (62.8 ± 5.1; post hoc $$p \leq 0.005$$) and tended to be higher after the fresh KF treatment (51.9 ± 5.0, post hoc $$p \leq 0.052$$), compared to the control (38.6 ± 5.1, Figure 3B).
Main effects of treatment were identified for morning sleepiness (SSS) [F [2, 22] = 5.47, $$p \leq 0.012$$] and alertness upon awakening (LSEQ) [F[2, 22] = 5.11, $$p \leq 0.015$$]. Irrespective of sleeper type, morning sleepiness ratings were higher after the control (3.00 ± 0.26; post hoc $$p \leq 0.013$$) and fresh KF (2.6 ± 0.2; post hoc $$p \leq 0.034$$) interventions compared to the freeze-dried KF treatment (2.1 ± 0.2; Figure 3C). Ratings of alertness upon awakening were higher after the freeze-dried KF (57.1 ± 2.4; post hoc $$p \leq 0.018$$) and tended to be higher following the fresh KF treatment (53.1 ± 3.4; post hoc $$p \leq 0.082$$) compared to the control (44.2 ± 2.8, Figure 3D).
## Mood measures
A summary of subjective mood measures is presented in Table 4. Main significant effects of treatment for esteem-related affect were identified [F[2, 22] = 4.56, $$p \leq 0.022$$], vigor [F[2, 22] = 4.34, $$p \leq 0.026$$], and total mood disturbance [F[2, 22] = 3.82, $$p \leq 0.038$$]. Furthermore, main trend effects of treatment for fatigue [F[2, 22] = 3.17, $$p \leq 0.062$$] and confusion [F[2, 22] = 3.37, $$p \leq 0.053$$] were identified. Irrespective of sleeper type, ratings for esteem-related affect tended to be higher following freeze-dried KF (12.6 ± 0.8; $$p \leq 0.060$$) and fresh KF (12.3 ± 0.6; post hoc $$p \leq 0.054$$) compared to the control (10.8 ± 0.5; Figure 4A). Ratings for vigor were higher following freeze-dried KF (6.58 ± 0.91; post hoc $$p \leq 0.030$$), but not fresh KF (4.9 ± 0.7; post hoc $$p \leq 0.487$$) treatments compared to the control (3.9 ± 0.6; Figure 4A). Ratings for total mood disturbance tended to be lower following the freeze-dried KF (86.9 ± 2.5; post hoc $$p \leq 0.054$$) and fresh KF (90.3 ± 1.8; post hoc $$p \leq 0.063$$) treatments compared to the control (96.4 ± 2.1, Figure 4B). No main effects for sleeper type or interactions were identified for any mood measures.
## Objective measures of sleep quality
A summary of objective sleep measures is presented in Table 5. There was an interaction effect between treatment and sleeper type for the number of awakenings [F[2, 22] = 4.06, $$p \leq 0.032$$]. Within poor sleepers, the number of awakenings tended to be lower after the fresh KF treatment (35.9 ± 2.76; $$p \leq 0.099$$) than the control (43.4 ± 3.5, Figure 5B). Conversely, among good sleepers, the number of awakenings tended to increase after the freeze-dried KF treatment (42.8 ± 3.81; post hoc $$p \leq 0.080$$) compared to the control (33.3 ± 3.54, Figure 5B).
There were main effects of treatment for wake after sleep onset [F[2, 22] = 3.74, $$p \leq 0.040$$]. Irrespective of sleeper type, the number of awakenings was higher after the freeze-dried KF treatment (41.4 ± 3.2; $$p \leq 0.045$$) but not the fresh KF treatment (34.4 ± 2.7; $$p \leq 0.888$$) compared to the control (37.0 ± 4.3) (Figure 5A). No main effects of treatment for sleeper type were identified for any objective sleep measures.
## Urinary metabolites
A summary of urinary measures is in Table 6. A significant main effect of treatment for urinary 5-HIAA concentration was identified [F[2, 22] = 19.15, $p \leq 0.001$] with post hoc comparisons identifying freeze-dried KF (6.2 ± 0.4 mg/g creatinine; $p \leq 0.001$) and fresh KF (6.6 ± 0.4 mg/g creatinine; $$p \leq 0.001$$) treatments as showing significantly higher 5-HIAA concentration, as compared to the control (4.9 ± 0.4 mg/g creatinine) (Figure 6A). A significant main effect of sleeper type for urinary B-vitamin concentration was identified for nicotinamide [F[2, 22] = 6.11, $$p \leq 0.022$$], biotin [F[2, 22] = 5.77, $$p \leq 0.025$$], riboflavin [F[2, 22] = 6.85, $$p \leq 0.016$$], pyridoxamine [F[2, 22] = 7.71, $$p \leq 0.011$$], thiamine [F[2, 22] = 7.58, $$p \leq 0.012$$] and was near significant for pyridoxal [F[2, 22] = 4.28, $$p \leq 0.051$$]. The post hoc comparison revealed that good sleepers had significantly higher concentrations of all measured B-vitamins than poor sleepers (Figure 6B). No interaction effects were identified for any urinary metabolite measures. Additionally, the urinary concentration of 5-HIAA and vitamin C in good sleepers was negatively related to total mood disturbance (5-HIAA; r = −0.41, $$p \leq 0.04$$) and latency (vitamin C; r = −0.72, $p \leq 0.005$). A full within correlation table is presented in Supplementary Table S1.
## Discussion
The current study shows that irrespective of the sleep quality participant experience, acutely supplemented freeze-dried KF resulted in feeling less sleepy and more alert. Both fresh and freeze-dried KF increased urinary excretion of 5-HIAA. Furthermore, poor sleepers felt it was easier to wake up in the morning after consuming freeze-dried KF in the evening and this was also moderately felt when consuming the fresh KF. Good sleepers rated themselves as getting to sleep easier when consuming the fresh KF. Interestingly, poor sleepers tended to wake more often in the night after consuming freeze-dried KF with the evening meal, while good sleepers woke less often during the night after consuming fresh KF with the evening meal.
The beneficial impacts observed when consuming KF on aspects of sleep quality are consistent with other KF sleep intervention studies that showed improvement in subjective daytime function ratings [37] and sleep quality measures [21]. In addition, in a mouse model fed KF skin extracts sleep onset latency was reduced [38], thus providing further evidence supporting the beneficial impact of KF on sleep. Sleep is a complex process controlled by internal and external factors. There are potentially at least three explanations that could explain the observed outcomes. Firstly, it is known that KF is rich in phenylalanine, tyrosine, tryptophan, glutamic acid [22] and serotonin [39] that are further metabolized to produce dopaminergic, serotoninergic, and GABAergic neurochemicals. These neurotransmitters are vital in the regulation of sleep/wake cycles. Secondly, green KF contains the enzyme actinidin, which has been shown to cause an earlier peak increase in plasma circulating amino acids in adults when compared to kiwifruit without actinidin [40]. Therefore, potentially increasing the concentration of bioavailable amino acids to be further metabolized into neurochemicals. Lastly, KF may act on improving sleep via potentiating the bioaminergic responses. This has been demonstrated in in vitro studies [41]. Given this, KF may impact sleep through the supply and modulation of peripheral neurochemical responses, which may impact systemic concentration modifications throughout the rest of the body. Further studies are needed to determine which postprandial biomarkers are increased in sleep and upon waking the following morning to elucidate how KF may impact sleep and subsequent mood.
The current study also shows that irrespective of sleep quality participant experience, acutely supplemented freeze-dried KF resulted in improved vigor compared to the control. Likewise, fresh, and freeze-dried KF treatments tended to improve esteem-related affects, and freeze-dried KF tended to decrease total mood disturbance score compared to the control. The impacts of consuming KF on aspects of waking mood in this study are consistent with earlier KF mood studies that showed improvement in mood [42, 43].
Similarly, the various underlying regulatory mechanisms of mood are complex. Firstly, vitamin C is a potent water-soluble antioxidant with distinct roles in the body, including reducing systemic inflammation and as co-factors in production of neurotransmitters. Consumption of KF reduces oxidative stress and inflammatory markers [44]. Additionally, higher vitamin C status has been correlated with decreased total mood disturbances [45], and regular consumption of KF increases vitamin C in the body [46]. Secondly, neurochemicals, such as serotonin, provided by KF, may also enhance mood [47]. Lastly, KF is rich in other vitamins and polyphenols, which may have facilitated these mood improvements by improving oxidative stress and metabolism. Given this, KF may have impacted mood via the supply of vitamin C and other nutrients, which altered the metabolism of neurochemicals and may also have affected oxidative stress. One cannot rule out that improved sleep might have led to improved mood outcomes and vice versa [48].
No differences between KF treatments were observed for urinary aMT6s and vitamin C. The correlation analysis of urinary metabolites with sleep and mood measures indicated that urinary aMT6s and vitamin C morning excretion were negatively related to sleep latency. Waking urinary aMT6s can be used to predict nocturnal plasma melatonin [13] and lower urinary aMT6s excretion has been associated with lowered alertness [11]. The results of aMT6s can be interpreted in two ways; first, the lack of treatment effects on excreted aMT6s may be due to insufficient samples collected or only one sample collected upon waking. Studies have shown that a 24–48-h collection period may better reflect these changes in urinary aMT6s [49]. Dim light melatonin onset, a circadian phase marker measured over a 5–7 h window in plasma or saliva before bed, could be measured to assess whether KF may alter the circadian phase. Second, the finding may suggest that another mechanism not related to melatonin may facilitate the acute improvements in sleep quality and mood by KF. However, regardless of sleep quality, consumption of fresh and dried KF treatments increased urinary excretion of 5-HIAA compared to the control, which is consistent with data on the acute effects of KF consumption on urinary 5-HIAA [20]. Comparably, in a different study, participants consuming tart cherries increased morning urinary 5-HIAA excretion and improved mood [19]. 5-HIAA is the primary metabolite of serotonin and is involved with mood regulation [47]. The results here suggested that consumption of fresh or freeze-dried KF may have increased circulating and brain levels of serotonin, which impacted mood.
Concentrations of excreted B-vitamins were greater for good sleepers than for poor sleepers, regardless of the treatment received. Excretion of B-Vitamins can be used as an indicator of baseline B-vitamin status, with higher excretion suggesting saturation and lowered amounts indicating deficiency [50]. The lower amount of B-vitamins may be explained by a lower background dietary intake of B-vitamins, however this is speculation and should be interpreted with caution as no dietary records were collected. It may be suggested that poor sleepers utilize more B-vitamins for cellular metabolic processes. For example, nicotinamide (a form of B3), pyridoxine and pyridoxamine (forms of B6) are required as co-factors in metabolizing neurotransmitters [51]. Additionally, low levels of B-vitamins have also been associated with low-grade inflammation [52] and poor sleep caused increased circulating inflammatory markers [53]. However, this study did not measure any inflammatory markers, suggesting that inflammatory status is worth exploring in future studies. Other factors that were not assessed that may also impact B-vitamins include exercise status, which is known to increase the requirements for B-vitamins [54].
Irrespective of sleep quality participant experience, acutely supplemented freeze-dried KF improved morning sleepiness compared to both fresh KF treatment and control. This finding could be explained by the structural and compositional differences between KF interventions; a KF drink that contained the skin (liquid) or KF flesh eaten fresh without the skin (solid). Including skin in the freeze-dried powder altered the polyphenolic content, and as mentioned previously, KF skin extracts potentiate sleep induction in a pentobarbital-induced sleep mouse model [38]. Thus, the freeze-dried powder may be improving sleep onset, thus causing better morning alertness. The results on sleep onset suggested that this may occur in poor sleepers. Furthermore, the added polyphenols may be impacting the body by affecting the expression of clock genes [55], improving cerebral blood flow (thus, improving mood [56]), altering the permeability of the blood–brain barrier and/or altering neurotransmissions [57].
Nevertheless, it is worth noting that the food structure may influence stomach emptying rate and satiety and cause differential interactions with other food matrices, consequently impacting gut comfort. For example, consumption of a carbohydrate-rich meal with KF lowered postprandial hunger compared to consumption of a carbohydrate meal only [58]. Also, consuming fresh green actinidin-containing KF has improved gastric comfort compared to KF without actinidin [59]. These findings suggest that improvements in sleep quality may be due to reduced feelings of bloating and hunger; however, these parameters were not measured in this study.
Contrary to the previous KF and sleep quality studies, this study was the first to assess the acute and separate effects of a fresh KF or dried (containing skin) KF treatment on sleep quality and mood in a healthy male cohort with good or poor-quality sleep. The urinary 5-HIAA and aMT6s concentrations suggest a novel potential mechanism underpinning the relationship between KF and sleep. Additionally, the results provide evidence for the repurposing of KF skin and lower-quality fruit, which would otherwise be wasted, into products that can easily be stored with extended shelf-life.
This study is not without limitations. One of the limitations was the low subject numbers, the study was powered using a different intervention to this [34], as this was the first study to assess acute impact of KF on LSEQ. Secondly, due to the nature of the intervention, it was impossible to blind participants to their treatment. This is evident in the ratings for getting to sleep and quality of sleep in good sleepers. Fresh KF was on average higher than both the control and freeze-dried KF, suggesting that some participants may have preconceived notions about one treatment over another. Thirdly, because the study was conducted only on men, it is difficult to interpret the results for women. Fourthly, the control used was not iso-calorically matched, suggesting participants may have rated themselves poorly on the control intervention due to other factors such as appetite and hunger, which were not measured. Furthermore, urine samples were limited to morning samples, with no other timepoints collected. Collecting samples other than urine at different time points may have allowed a better understanding of the postprandial changes that have occurred. Collection of plasma and saliva pre and post intervention and up to 5 h before bedtime may have provided a better understanding of melatonin circulation, which may have influenced sleep [49]. In addition, saliva could have been collected upon waking and used to quantify cortisol awakening response (CAR), which could provide a simple measure of the reactive capacity of the hypothalamic–pituitary–adrenal (HPA) axis.
Although actigraphy was used here and in other acute settings [60], results should be interpreted with caution. For instance, a person lying still while awake may be recorded as asleep due to immobility. Actigraphy does not account for these events. Using polysomnography was not feasible in this study due to the in-home setting, but would have provided a better understanding of the actual acute impacts of KF and sleep quality and architecture [61]. Future studies could incorporate a larger cohort with participants of mixed cohort of males and females consuming differing doses of the freeze-dried KF. Furthermore, future studies could use an energy matched placebo with the same amounts of vitamin C and B-vitamins. Additionally, other cohorts worth exploring include older adults due to age-related sleep disturbances [62], University students under high stress [63], or inpatients in planned care services [64].
## Conclusion
Overall, this study is the first to demonstrate that a single evening meal with KF, whether fresh or freeze-dried, improved sleep quality and mood in males with good or poor-quality sleep. This effect may have been mediated through increased serotonin metabolism. Further studies should be conducted to elucidate the impact of freeze-dried KF on those who experience poor sleep and the potential mechanisms by measuring other biomarkers in urine, plasma, and saliva. Nonetheless, as beneficial effects of sleep were identified following supplementation with KF in young men, these data help provide additional evidence for the role of KF in facilitating healthy sleep regulation.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The study protocol was approved by the Massey University Human Ethics Committee study (Massey University HEC: Southern A application-$\frac{20}{52}$), and the trial has been registered at the Australian New Zealand Clinical Trials Registry (ANZCTR) (ANZCTR12620000411943). Participants provided written, informed consent for participation.
## Author contributions
AK, CG, NR, WM, and SH conceptualized the study and contributed to editing the manuscript. AK designed and conducted the study and prepared the manuscript with input from the other authors. All authors contributed to the article and approved the submitted version.
## Funding
This study was funded by the Riddet Institute, a Centre of Research Excellence (CoRE) funded by the NZ Tertiary Education Commission, and the High-Value Nutrition National Science Challenge (UOAX1902) with in-kind support from the partner organization: Zespri International and Alpha Group Holdings. The funders had no role in the design of this study and did not have any role during its execution, analyses, interpretation of the data, or decision to submit results. AK was supported by a PhD stipend from the Riddet Institute.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1079609/full#supplementary-material
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|
---
title: A high-throughput drug screening identifies luteolin as a therapeutic candidate
for pathological cardiac hypertrophy and heart failure
authors:
- Zhenya Wang
- Wei Shi
- Taibo Wu
- Tian Peng
- Xiaoming Wang
- Shuaiyang Liu
- Zifeng Yang
- Jia Wang
- Peng-Long Li
- Ruifeng Tian
- Ying Hong
- Hailong Yang
- Lan Bai
- Yufeng Hu
- Xu Cheng
- Hongliang Li
- Xiao-Jing Zhang
- Zhi-Gang She
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10043402
doi: 10.3389/fcvm.2023.1130635
license: CC BY 4.0
---
# A high-throughput drug screening identifies luteolin as a therapeutic candidate for pathological cardiac hypertrophy and heart failure
## Abstract
### Background
Pathological cardiac hypertrophy is commonly resulted from sustained pressure overload and/or metabolic disorder and eventually leads to heart failure, lacking specific drugs in clinic. Here, we aimed to identify promising anti-hypertrophic drug(s) for heart failure and related metabolic disorders by using a luciferase reporter-based high-throughput screening.
### Methods
A screen of the FDA-approved compounds based on luciferase reporter was performed, with identified luteolin as a promising anti-hypertrophic drug. We systematically examined the therapeutic efficacy of luteolin on cardiac hypertrophy and heart failure in vitro and in vivo models. Transcriptome examination was performed to probe the molecular mechanisms of luteolin.
### Results
Among 2,570 compounds in the library, luteolin emerged as the most robust candidate against cardiomyocyte hypertrophy. Luteolin dose-dependently blocked phenylephrine-induced cardiomyocyte hypertrophy and showed extensive cardioprotective roles in cardiomyocytes as evidenced by transcriptomics. More importantly, gastric administration of luteolin effectively ameliorated pathological cardiac hypertrophy, fibrosis, metabolic disorder, and heart failure in mice. Cross analysis of large-scale transcriptomics and drug-target interacting investigations indicated that peroxisome proliferator activated receptor γ (PPARγ) was the direct target of luteolin in the setting of pathological cardiac hypertrophy and metabolic disorders. Luteolin can directly interact with PPARγ to inhibit its ubiquitination and subsequent proteasomal degradation. Furthermore, PPARγ inhibitor and PPARγ knockdown both prevented the protective effect of luteolin against phenylephrine-induced cardiomyocyte hypertrophy in vitro.
### Conclusion
Our data clearly supported that luteolin is a promising therapeutic compound for pathological cardiac hypertrophy and heart failure by directly targeting ubiquitin-proteasomal degradation of PPARγ and the related metabolic homeostasis.
## Introduction
Heart failure (HF) causes a serious social and economic burden, with a prevalence of approximately $1\%$–$2\%$ [1]. Cardiac hypertrophy caused by hemodynamic overload is a critical irritation in heart failure [2]. The pathogenesis of cardiac hypertrophy involves alterations in cardiac myocyte metabolism, oxidative stress, endoplasmic reticulum stress and autophagy [3], as well as alterations in fibroblasts, inflammatory cells, and endothelial cells [4]. A group of drugs, including angiotensin converting enzyme inhibitors, β-adrenergic receptor blockers, and angiotensin receptor blockers, exhibit clinical benefit in inhibiting the progression of cardiac hypertrophy and HF [5]. However, decades of clinical application of these drugs failed to reduce the absolute number of HF patients mainly due to the sustained exacerbation of risk factors including obesity, diabetes, and other causal factors, as well as an ageing population [6]. Therefore, it is urgently necessary to gain insight into the pathogenesis of cardiac hypertrophy and HF and identify new therapeutic approaches.
De novo drug development generally requires a significant investment of time, manpower and costs and the success rate still needs to be improved. An alternative strategy to reduce the duration and costs of drug development is to explore new indications for existing drugs, which can take advantage of the pre-existing pharmacodynamic/pharmacokinetic and toxicology properties of many drugs approved for human use. For example, raloxifene, which is approved for the treatment of osteoporosis, has been found to be beneficial in the treatment of breast cancer in recent studies [7].
Here, we applied luciferase reporter-based high-throughput screening on FDA-approved chemical compounds library (include 2,570 compounds) to identify potential anti-hypertrophic drugs for heart failure and the related metabolic disorders. Among several positive hits, luteolin emerged as the most robust candidate against cardiomyocyte hypertrophy. Luteolin, one of the most prevalent flavones, possesses anti-oxidative, anti-tumor, anti-apoptotic, and anti-inflammatory properties (8–10). Although its potential protective effects on cardiomyocyte hypertrophy and fibrosis have also been proposed (11–14), it is remains to be verified regarding whether luteolin has a sustained protective effect throughout the progression of pressure overload-induced pathological cardiac hypertrophy and HF. Furthermore, the panoramic effects of luteolin in the heart and the specific regulatory mechanism underlying its effects are also unclear. All these information is fundamentally essential for repurposing luteolin as a potential candidate for pathological cardiac hypertrophy and HF.
Here, we successively demonstrated that luteolin blocked phenylephrine-induced cardiomyocyte hypertrophy and ameliorated pressure overload-induced cardiac hypertrophy, fibrosis, metabolic disorder, and HF in mice. Mechanistically, we have demonstrated that peroxisome proliferator activated receptor γ (PPARγ) is the direct target of luteolin in the setting of pathological cardiac hypertrophy and the related metabolic disorders. Luteolin can directly interact with PPARγ to inhibit its ubiquitination and subsequent proteasomal degradation. In summary, we here found out from FDA-approved drug library that luteolin emerged as a therapeutic candidate for pathological cardiac hypertrophy and heart failure by directly suppressing ubiquitin-proteasomal degradation of PPARγ and metabolic homeostasis.
## Animals
All mice were placed in an environment with controlled light cycles, temperature, and humidity. The cardiac hypertrophy model was constructed by transverse aortic constriction (TAC) surgery as previously described [15]. Briefly, male mice with C57BL/6 background (10-week-old; weight: 25–27 g) were anesthetized via i.p. injection of pentobarbital sodium (90 mg/kg, P3761, Sigma-Aldrich). After exposing the transverse aorta, the transverse aorta was ligated transversely with 7–0 silk and a 26-gauge needle. Identical operation without ligation the transverse aorta was performed in the sham operation group.
Seven days after TAC or sham surgery, mice were randomly given vehicle or luteolin treatment. Mice were administered orally with luteolin (40 mg/kg) or vehicle [$1\%$ carboxymethyl cellulose sodium (CMC-Na)] daily for 11 consecutive weeks. After 4, 8 and 12 weeks of TAC or sham surgery, heart function was respectively measured by echocardiography as previously described [16]. At the end of the experiment, the mice were anesthetized using the above method and then subjected into euthanasia via cervical dislocation, and the heart, lung, and tibia were collected for further examinations.
## Echocardiography
Echocardiography was used to evaluate mice cardiac function as described previously [16]. A small animal ultrasound imaging system (Mylab30CV, ESAOTE, S. P. A) was used to perform echocardiography. The left ventricle was evaluated on both long- and short-axis views of the parasternal sternum as described previously. The echocardiography operator is not informed about the grouping of mice.
## Histological analysis
Twelve weeks after TAC or sham surgery, mice hearts were collected. Hearts were macerated in $10\%$ formalin and subsequently encapsulated in paraffin. Paraffin-embedded mice hearts were sectioned transversely (5 μm). Subsequently, hematoxylin-eosin and picrosirius red staining (for collagen volume fraction analysis) were executed. These analyses were performed using Image-Pro Plus 6.0. At least one hundred cardiomyocytes were examined in each section, and the collagen volume fraction was computed as picrosirius red staining area divided by total area.
## Primary cardiomyocytes isolation, cell treatment, and immunofluorescence staining
Primary cardiomyocytes were obtained from Sprague-Dawley rat (1–2 days) hearts in accordance with previously described [17]. Primary neonatal rat cardiomyocytes (NRCMs) were incubated in the DMEM/F12 medium with $10\%$ fetal bovine serum, $1\%$ penicillin/streptomycin, and 0.2 mM BrdU for 48 h. After culturing NRCMs in serum-free DMEM/F12 for 12 h, hypertrophy was induced by adding 50 μM phenylephrine (PE, P6126, Sigma) for 24 h. DMEM medium with $1\%$ penicillin/streptomycin and $10\%$ fetal bovine serum was used for the incubation of H9C2 cells and HEK 293 T cells.
The size of the cardiomyocyte surface was assessed by immunofluorescence staining for α-actinin after 24 h of incubation with PBS or PE as previously described in the established protocol [15, 16]. Briefly, cardiomyocytes were successively soaked in $4\%$ formaldehyde and $0.2\%$ Triton-X 100 (T8787, Sigma-Aldrich). Then the cells were stained with α-actinin (1:100 dilutions, A7811, Sigma) and appropriate secondary antibody (1:200 dilutions, A11061, Invitrogen). The size of the cardiomyocyte surface was examined with Image-Pro Plus 6.0.
## Plasmid and lentivirus construction
Rat Bnp (b-type natriuretic peptide) promoter (−2147, +132 bp) and Myh7 (myosin heavy chain 7) promoter (−2500, +89 bp) were expanded and cloned into pGL3-promoter luciferase reporter vector to obtain promoter reporter plasmids. The lentiviral plasmids encoding shRNA for Pparγ was constructed in the pLKO.1 vector. HEK 293 T cells were transfected with pLKO-shRNA or pLKO-sh Pparγ along with the packaging vectors pSPAX2 and pMD2G. After incubation of cells at 37°C for 40 h, lentiviral suspensions were harvested for infection with H9C2 cells followed by puromycin selection for one week for various analyses. Primers for plasmid and lentivirus construction are provided in Supplementary Table S1.
## FDA-approved library screening
HEK 293 T cells were co-transfected with a plasmid expressing Firefly luciferase and a plasmid expressing Renilla luciferase. After 24 h, FDA-approved compounds (20 μM, L1300, Selleckchem), containing 2,570 compounds, were added separately to the culture medium. The cells were lysed using lysis solution, and the luciferase activity was examined by adding luciferase assay substrate (E1980, Promega). The results were shown as log2-fold change normalized to control.
## Cell viability assay
The Cell Counting Kit (CCK-8, Beyotime) was applied to assay the cell viability of NRCMs. NRCMs were incubated in 96-well plates with 2 × 104 cells per well and subsequently treated with different concentrations of luteolin. After the addition of CCK-8 solution, the NRCMs were further incubated. After 24 h of incubation, the absorbance of each well at 450 nm was detected.
## Immunoprecipitation assays
After 24 h transfection with the appropriate plasmids, HEK293T cells were treated with biotin-linked luteolin (20 μM) or biotin for another 4 h and then lysed in pre-cooled immunoprecipitation (IP) buffer. Add the specified antibodies and protein G Bestarose 4FF beads (AA304307, Bestchrom) to the lysate supernatant and incubate for 4 h. The immunocomplexes were washed with pre-cooled IP buffer and subsequently collected for western blot analysis.
## Western blot
Proteins were obtained from cardiac tissue and cardiomyocytes. The BCA Protein Assay Kit (23225, Thermo) was applied to detect protein levels. Proteins were isolated by SDS-PAGE and electrotransferred to PVDF membranes, which were successively incubated with primary and secondary antibodies. Protein signals were detected using the ChemiDoc MP Imaging System (Bio-Rad). GAPDH served as the loading control. All antibodies are shown in Supplementary Table S2.
## Ubiquitination assays
Cultured HEK 293 T cells were collected and lysed in SDS lysis buffer including protease inhibitor cocktail (04693132001, Roche). The lysates were centrifuged (12,000 rpm for 15 min), and the supernatants were analyzed by immunoprecipitation assays with indicated antibodies, followed by western blot analyses.
## Quantitative real-time PCR
RNA was derived from mice heart tissue and NRCMs with Trizol regent (T9424, Sigma-Aldrich), and reverse transcription was conducted by the HiScript III RT SuperMix for qPCR (R323-01, Vazyme). Quantitative real-time PCR was conducted to detect gene expression level using SYBR Green (Q311-03, Vazyme), and Gapdh served as internal reference gene [18]. The real-time PCR primers are shown in Supplementary Table S3.
## RNA-sequencing and data processing
RNA extracted from mice hearts and cultured NRCMs was used for library preparation. Single-end RNA-seq was carried out with MGISEQ-2000 (MGI, China). Based on the Ensembl mouse (mm10/GRCm38) genome, gene sequence alignment on clean reads was conducted by HISAT2 software (version 2.1.0). SAMtools software (version 1.4.1) was used to transform the mapped fragments into the Binary Alignment Map files. Then, the reads count value of the genes were computed using StringTie (version v1.3.3b). DESeq2 (v1.2.10) software was used for differentially expressed genes identification by processing read count information. Gene with adjusted P-values less than 0.05 and a fold change larger than 1.5 was recognized as differentially expressed gene. Gene Ontology analysis, Gene Set Enrichment Analysis (GSEA), and Kyoto Encyclopedia of Genes and *Genomes analysis* were conducted by R package clusterProfiler (version 4.2.2). Gene sets with adjusted P value of less than 0.05 were recognized as statistically significant. The R package gmodels (version 2.18.1) was used for principal component analysis.
## Data analysis
SPSS 22.0 was applied to analyze all results and the results were presented as the mean ± SD. When the data were normally distributed, 2 group comparisons were conducted by 2-tailed Student t test, and multiple comparisons were conducted by one-way ANOVA. When data were skewed distributed, multiple comparisons were conducted by Kruskal-Wallis test. $P \leq 0.05$ was regarded as statistically significant.
## Luteolin emerged as a therapeutic candidate for pathological cardiac hypertrophy in the FDA-approved chemical compounds screen
Upregulation of Myh7 and Bnp transcriptional activity is an indication of the risk for developing cardiac hypertrophy [19, 20]. To explore potential therapeutic drugs for cardiac hypertrophy and HF, our study screened the effects of FDA-approved chemical compounds library on the transcriptional activity of Myh7 and Bnp based on luciferase reporter assays (Figure 1A). The compounds were added to HEK 293 T cells transfected with Myh7 and Bnp promoter reporter plasmids, respectively, and luciferase activity was assayed after 24 h of incubation. Among 2,570 compounds, a total of five were screened as the candidates according to the criteria of downregulating Myh7 and Bnp promoter activity for more than $50\%$ (Figure 1B). After excluding two candidate compounds with significant cardiotoxicity, three candidate compounds were further examined for their effects on PE-induced cardiomyocyte hypertrophy (Figure 1C). Notably, luteolin, a flavonoid from a variety of plants [10], produced the most pronounced protective effect compared to dimethyl sulfoxide (DMSO) as indicated by immunofluorescence staining (Figures 1C,D).
**Figure 1:** *Luteolin emerged as a therapeutic candidate for pathological cardiac hypertrophy in the FDA-approved chemical compounds screen. (A) Schematic illustration of the experimental workflow of the luciferase-based FDA-approved chemical compounds screen. (B) The scatter plot demonstrating Myh7 and Bnp luciferase activity in HEK 293 T cells treated with FDA-approved compounds. Red dots represent each FDA drug that inhibited both Myh7 and Bnp luciferase reporter activities for more than 50%. (C) The advancement criterion for screening out the most effective compound on Myh7 and Bnp luciferase activity downregulation and cardiomyocyte hypertrophy inhibition. (D) Representative immunofluorescence images (left) of α-actinin staining and quantitative results of the cell surface area (right) of NRCMs treated with PBS, PE (50 μM), PE + entinostat, PE + belinostat, or PE + luteolin for 24 h (n ≥ 50 cells per group). Scale bar, 50 μm. The data shown are representative of three independent experiments. Values are presented as mean ± SD. *P < 0.05, **P < 0.01, n.s., no significant difference. FDA, the United States Food and Drug Administration; NRCMs, Primary neonatal rat cardiomyocytes; HEK 293 T cells, human embryonic kidney 293 T cells; luc, luciferase; Bnp, b-type natriuretic peptide; Myh7, myosin heavy chain 7; PE, phenylephrine.*
## Luteolin ameliorates PE-induced cardiomyocyte hypertrophy in primary cardiomyocytes
The cellular safety of luteolin was confirmed by its non-significant impact on cell viability of NRCMs (Figure 2A). We continued to treat PE-stimulated NRCMs with different dosages of luteolin to further evaluate its effect on the hypertrophy of NRCMs. Immunostaining results demonstrated that luteolin inhibited the increase in surface area of cardiomyocytes in a dose-dependent way (Figure 2B). Similarly, the mRNA levels of Anp (atrial natriuretic peptide), Bnp, and Myh7 were also significantly inhibited by luteolin (10 μM) (Figure 2C). To explore the effect of luteolin at the panoramic molecular level, RNA-sequencing analysis was conducted on PE-treated NRCMs in the presence or absence of luteolin. By principal component analysis, the transcriptome profiles were clearly divided into two clusters (Figure 2D). The volcano plot results indicated a huge number of differentially expressed genes between the two groups (Figure 2E). GSEA analysis based on gene ontology database showed that the genes modulated by luteolin were mainly concentrated in cardiac hypertrophy, fibrosis, and protein synthesis (Figure 2F). The heatmap showed that luteolin markedly repressed the expression levels of genes associated with the aforesaid pathways (Figure 2G). These results show that luteolin inhibits cardiomyocyte enlargement in vitro.
**Figure 2:** *Luteolin ameliorates PE-induced cardiomyocyte hypertrophy in primary cardiomyocytes. (A) Relative cell viability of NRCMs after treatment with different concentrations of luteolin. The data shown are representative of three independent experiments. n.s., no significant difference compared to the 0 μM group. (B) Representative immunofluorescence images (left) of α-actinin staining and quantitative results of the cell surface area (right) of NRCMs treated with PBS, PE (50 μM), or PE + luteolin (5 or 10 μM) for 24 h (n ≥ 50 cells per group). Scale bar, 50 μm. The data shown are representative of three independent experiments. (C) Relative mRNA levels of cardiac hypertrophy marker genes (Anp, Bnp, and Myh7) in NRCMs treated with PBS, PE (50 μM), or PE + luteolin (5 or 10 μM) for 24 h (n = 5 independent experiments). (D) Principal component analysis showing the global sample distribution profiles between groups based on the RNA-sequencing data. (E) Volcano plot analysis showing a huge number of differentially expressed genes between the two groups. Genes with adjusted P-values less than 0.05 and a fold change larger than 1.5 was recognized as differentially expressed genes. (F) Gene set enrichment analysis of molecular events involved in cardiac hypertrophy, fibrosis, and protein synthesis in RNA-sequencing data. (G) Heatmap showing the significantly altered genes related to cardiac hypertrophy. Values are presented as mean ± SD. *P < 0.05, **P < 0.01, n.s., no significant difference. PE, phenylephrine; Anp, atrial natriuretic peptide; Bnp, b-type natriuretic peptide; Myh7, myosin heavy chain 7.*
## Luteolin inhibits cardiac dysfunction induced by pressure overload in mice
To evaluate whether luteolin ameliorated heart failure in mice, we randomly divided wild type mice (C57BL/6) into two groups for TAC or sham surgery. Each group were further randomly divided into two groups and orally administered with luteolin (40 mg/kg) or vehicle ($1\%$ CMC-Na) once daily at one week after surgery (Figure 3A). Cardiac function at 4, 8, and 12 weeks after TAC or sham surgery was detected by echocardiography, respectively (Supplementary Table S4). After 4 weeks of TAC, echocardiographic evaluation showed signs of cardiac hypertrophy and reduced cardiac function in TAC-treated mice compared to sham control, based on increased left ventricular end-diastolic diameter (LVEDd) and left ventricular end-systolic diameter (LVESd), reduced ejection fractions (EF) and fractional shortening (Figure 3C). Compared to the vehicle group, luteolin inhibited cardiac hypertrophy and deterioration of cardiac function in TAC mice (Figure 3C). After 12 weeks of TAC, mice were sacrificed for phenotypic and histological examination. Luteolin significantly blocked pressure overload-induced cardiac enlargement in TAC-treated mice (Figure 3D). Furthermore, echocardiographic evaluation, including EF, fractional shortening, stroke volume, cardiac output, LVEDd, LVESd, left ventricular end-diastolic volume, and left ventricular end-systolic volume, further verified the protective function of luteolin administration against cardiac remodeling and decreased cardiac function in TAC-treated mice after 12 weeks of TAC (Figures 3E,F). Notably, luteolin administration showed negligible influences in the sham settings (Figures 3B–F).
**Figure 3:** *Luteolin inhibits cardiac dysfunction induced by pressure overload in mice. (A) Schematic diagram of the experimental procedure. (B) Representative echocardiography images of mice measured at 12 weeks after TAC. (C) Assessments of echocardiographic parameters of left ventricular end-diastolic diameter (LVEDd), left ventricular end-systolic diameter (LVESd), ejection fractions (EF), and fraction shortening (FS) of mice at 4 weeks after sham or TAC surgery (n = 8). (D) Heart weight (HW), HW/body weight (BW), lung weight (LW)/BW, and HW/tibia length (TL) ratios of mice at 12 weeks after sham or TAC surgery (n = 8). (E,F) Assessments of echocardiographic parameters of EF, FS, stroke volume, cardiac output, LVEDd, LVESd, left ventricular end-diastolic volume (LVEDV), and left ventricular end-systolic volume (LVESV) of mice at 12 weeks after sham or TAC surgery (n = 8). Values are presented as mean ± SD. *P < 0.05, **P < 0.01.*
## Luteolin prevents cardiac hypertrophy and fibrosis in mice
Sustained pathological overload induces maladaptation and cardiac remodeling, including cardiomyocyte hypertrophy, fibrosis, capillary rarefaction, cellular dysfunction, and other complex responses, ultimately leading to heart failure [21, 22]. After 12 weeks of TAC, luteolin-treated TAC mice had significantly attenuated cross-sectional area of cardiomyocytes compared with TAC mice, as indicated by histological analysis with hematoxylin-eosin staining (Figures 4A,B). Furthermore, PSR staining of heart sections showed that luteolin treatment markedly inhibited cardiac fibrosis in TAC mice (Figures 4C,D). Consistent with these data, the expression levels of Anp, Bnp, Myh7, Col3a1 (collagen type III alpha 1), Col1a1 (collagen type I alpha 1), and Ctgf (connective tissue growth factor) were also notably downregulated in luteolin-treated TAC mice compared to the vehicle group (Figures 4E–G). These data clearly support that luteolin treatment attenuated the pathological cardiac remodeling induced by pressure overload.
**Figure 4:** *Luteolin prevents cardiac hypertrophy and fibrosis in mice. (A) Representative images of hematoxylin-eosin (H&E) staining of left ventricular cross-sections in the mice hearts at 12 weeks after sham or TAC surgery (n = 6). Scale bar, 1 mm for the top set and 25 μm for the bottom parts. (B) Quantitative results of average cross-sectional areas from the indicated groups. (C) Representative images of picrosirius red (PSR) staining of left ventricular cross-sections in the mice hearts at 12 weeks after sham or TAC surgery (n = 6). Scale bar, 50 μm. (D) Quantitative results of left ventricular interstitial collagen volume from the indicated groups. (E,F) Relative mRNA levels of hypertrophy and fibrosis marker genes in heart tissues from the indicated mice (n = 5). (G) Immunoblotting (left) and quantitation (right) of ANP, BNP, and MYH7 protein levels in the mice hearts at 12 weeks after sham or TAC surgery (n = 3). Values are presented as mean ± SD. *P < 0.05, **P < 0.01. ANP, atrial natriuretic peptide; BNP, b-type natriuretic peptide; PE, phenylephrine; MYH7, myosin heavy chain 7.*
## Luteolin enhances fatty acid metabolism and decreases glucose metabolism in the mouse failing hearts
It is known that in the case of pathological hypertrophy, the heart undergoes metabolism reprogramming characterized by an increased reliance on glucose metabolism and a reduced reliance on fatty acid oxidation. This metabolic profile reduces the capacity for ATP synthesis and ultimately promotes the progression of heart failure [23, 24]. We detected the mRNA levels of genes associated with fatty acid and glucose metabolism in the mouse failing hearts. The mRNA levels of PPARγ coactivator-1α/1β, the critical regulators of fatty acid uptake and oxidation, were markedly reduced in TAC hearts, while luteolin attenuated this change (Figure 5A). Consistently, luteolin treatment reversed the reduction of mRNA levels of genes associated with fatty acid uptake (Figure 5B) and fatty acid oxidation (Figure 5C) in the heart from TAC-treated mice. The administration of luteolin treatment also reversed increased levels of genes associated with glucose metabolism (Figure 5D).
**Figure 5:** *Luteolin enhances fatty acid metabolism and decreases glucose metabolism in the mouse failing hearts. (A) Relative mRNA levels of PPARγ coactivator-1α and PPARγ coactivator-1β in the mice hearts at 12 weeks after sham or TAC surgery (n = 5). (B,C) Relative mRNA levels of genes associated with fatty acid uptake (B) and fatty acid oxidation (C) in the mice hearts at 12 weeks after sham or TAC surgery (n = 5). (D) Relative mRNA levels of genes associated with glucose metabolism in the mice hearts at 12 weeks after sham or TAC surgery (n = 5). Values are presented as mean ± SD. *P < 0.05, **P < 0.01. Pgc-1α/β, peroxisome proliferative activated receptor-gamma coactivator-1α/β; Fabp3/4, fatty acid binding protein 3/4; Cpt1b/2, carnitine palmitoyltransferase 1b/2; Mcad, medium-chain acyl-CoA dehydrogenase; Lcad, long-chain acyl-CoA dehydrogenase; Atgl, adipose triglyceride lipase; Glut1, glucose transporter 1; Hif-1α, hypoxia-inducible factor 1α; PPARγ, peroxisome proliferator activated receptor γ; Ldha, lactate dehydrogenase A; Pkm2, pyruvate kinase M2.*
## Luteolin directly binds to and activates PPARγ during cardiac hypertrophy and HF
Considering the protective effects of luteolin on cardiac hypertrophy and HF, we tried to reveal the underlying molecular mechanisms of luteolin to ameliorates myocardial hypertrophy and heart failure. We analyzed RNA-sequencing data obtained from in vivo models. Principal component analysis revealed that the transcriptome profiles were clearly divided into two clusters (Figure 6A). GSEA analysis showed that the genes modulated by luteolin were mainly concentrated in cardiac hypertrophy, fibrosis, and protein synthesis (Figure 6B). The heatmap showed that luteolin markedly repressed the expression levels of genes associated with the aforesaid pathways (Figure 6C). To identify potential targets of luteolin, we performed a combined analysis of drug-target interacting investigations and RNA-sequencing (Figure 6D). First, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that PPAR signaling pathway was the most notably enriched molecular event regulated by luteolin (Figure 6E). Moreover, we performed gene set enrichment analysis, which further confirmed the upregulated PPAR signaling in the luteolin-treated group (Figure 6F). Then, we queried the protein data bank (PDB) and obtain 10 candidates interacting with luteolin (Figure 6G, Supplementary Table S5).
**Figure 6:** *Luteolin directly binds to and activates PPARγ during cardiac hypertrophy and HF. (A) Principal component analysis showing the global sample distribution profiles between groups based on the RNA-sequencing data. (B) Gene set enrichment analysis of molecular events involved in cardiac hypertrophy, fibrosis, and protein synthesis in RNA-sequencing data. (C) Heatmap showing the significantly altered genes related to cardiac hypertrophy. (D) Schematic diagram of the conjoint analysis. (E) Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the identified differentially expressed genes. (F) Individual GSEA (gene set enrichment analysis) plots of PPAR signaling pathway. (G) Schematic diagram of luteolin-protein binding from protein data bank (PDB). (H) Biotinylated protein interaction pull-down assays showing the binding of luteolin and HA-tagged PPARγ protein in HEK 293 T cells. The data shown are representative of three independent experiments. (I) PPARγ-induced PPRE luciferase activity in the treatment of luteolin at three doses (5 μM, 10 μM, and 20 μM) in HEK 293 T cells. The data shown are representative of three independent experiments. Values are presented as mean ± SD. **P < 0.01. PPARγ, peroxisome proliferator activated receptor γ; PPRE, PPAR response element.*
Combining the results of KEGG analysis with PDB data, we hypothesized that PPARγ may be a crucial target for luteolin in the inhibition of cardiac hypertrophy and HF. In order to determine whether luteolin could bind to PPARγ protein, we synthesized the chemical probe biotin-labeled luteolin (biotin-luteolin) and performed the biotinylated protein interaction pull-down assay. The data confirmed the interaction between luteolin and PPARγ (Figure 6H). Furthermore, we validated that luteolin dose-dependently activated the transcriptional activity of PPARγ in cultured HEK 293 T cells transfected with the PPAR response element (PPRE) reporters and PPARγ (Figure 6I). Thus, we speculated that PPARγ might be the direct target required for luteolin to exert a protective effect in cardiac hypertrophy and HF.
## Luteolin inhibits cardiac hypertrophy in a PPARγ-dependent manner
PPARγ is a critical modulator against cardiac hypertrophy (25–27), and its agonists have been found to inhibit cardiomyocytes hypertrophy by improving metabolic homeostasis and inflammatory response [28]. To further evaluate whether PPARγ activation is required for the protective effect of luteolin, we cotreated NRCMs by luteolin in combined with a PPARγ activation inhibitor, GW9662. Remarkably, GW9662 treatment largely eliminated the effect of luteolin in ameliorating PE-induced cardiomyocyte hypertrophy, as shown by immunofluorescence staining and immunoblotting analysis (Figures 7A–D). Furthermore, we constructed Pparγ knockdown H9C2 cells (Figure 7E). Consistently, Pparγ knockdown abrogated the protective effect of luteolin on PE-induced expression of the relevant cardiac hypertrophy markers (Figures 7F,G).
**Figure 7:** *Luteolin inhibits cardiac hypertrophy in a PPARγ-dependent manner. (A) Representative immunofluorescence images of α-actinin staining of NRCMs treated with PBS, PE (50 μM), PE + luteolin (10 μM), or PE + luteolin + GW9662 (20 μM) for 24 h (n ≥ 50 cells per group). Scale bar, 50 μm. (B) Quantitative results of the cell surface area of NRCMs from the indicated groups. The data shown are representative of three independent experiments. (C,D) Immunoblotting analysis (C) and quantitative results (D) of ANP and BNP in cultured NRCMs treated with vehicle (PBS), PE (50 μM), PE + luteolin (10 μM), or PE + luteolin + GW9662 (20 μM) for 24 h. The data shown are representative of three independent experiments. (E) Immunoblotting analysis (top) and quantitative results (bottom) of PPARγ in cultured WT and PPARγ knockdown H9C2. (F,G) Immunoblotting analysis (F) and quantitative results (G) of ANP and BNP in cultured cultured WT and PPARγ knockdown H9C2 treated with vehicle (PBS), PE (50 μM), and PE + luteolin (10 μM) for 24 h. The data shown are representative of three independent experiments. Values are presented as mean ± SD. *P < 0.05, **P < 0.01, n.s., no significant difference. PPARγ, peroxisome proliferator activated receptor γ; NRCM, primary neonatal rat cardiomyocyte; ANP, atrial natriuretic peptide; BNP, b-type natriuretic peptide; PE, phenylephrine; WT, wild-type.*
## Luteolin elevates the stability of PPARγ via inhibiting PPARγ ubiquitination
To explore the mechanism by which luteolin modulates PPARγ, we detected the mRNA and protein levels of PPARγ in heart samples from mice. Luteolin had no significant effect on PPARγ mRNA expression, whereas luteolin administration largely blocked TAC-induced decrease in PPARγ protein expression levels in mouse heart samples at 12 weeks after TAC surgery (Figures 8A,B). These results indicate that luteolin may activate PPARγ by regulating its protein stability. By treating NRCMs with cycloheximide (CHX), the half-life of PPARγ protein was remarkably extended in luteolin-treated NRCMs (Figure 8C). Furthermore, we found that the proteasome inhibitor MG132, rather than the lysosomal inhibitor chloroquine (Chlq), reversed CHX-induced destabilization of PPARγ, indicating that PPARγ was degraded mainly in a proteasome-dependent way (Figure 8D).
**Figure 8:** *Luteolin elevates the stability of PPARγ via inhibiting PPARγ ubiquitination. (A) Relative mRNA levels of PPARγ in the mice hearts at 12 weeks after sham or TAC surgery (n = 5). Values are presented as mean ± SD. n.s., no significant difference. (B) Immunoblotting analysis (left) and quantitative results (right) of PPARγ in the mice hearts at 12 weeks after sham or TAC surgery (n = 5). Values are presented as mean ± SD. **P < 0.01, n.s., no significant difference. (C) Immunoblotting analysis (left) and quantitative results (right) of PPARγ protein in NRCMs exposed to CHX (100 μM) for the indicated time with or without the luteolin treatment. The data shown are representative of three independent experiments. Values are presented as mean ± SD. *P < 0.05 compared to the control group. #P < 0.05 compared to the PE group. (D) PPARγ protein levels in NRCMs exposed to MG132 (10 μM) or Chlq (25 μM) in the presence of CHX (100 μM) for 4 h. The data shown are representative of three independent experiments. (E) The ubiquitination levels of PPARγ in HEK 293 T cells transfected with HA-tagged PPARγ and Myc-tagged Ub exposed to DMSO or luteolin (10 μM) in the presence of MG132 (10 μM). The data shown are representative of three independent experiments. (F) IP analyses of the interaction between TRIM55 and PPARγ in HEK 293 T cells transfected indicated plasmids exposed to DMSO or luteolin (10 μM). The data shown are representative of three independent experiments. (G) PPARγ protein levels in HEK 293 T cells transfected with HA-tagged PPARγ and Flag-tagged TRIM55 exposed to DMSO or luteolin (10 μM) in the presence of CHX (100 μM) for 12 h. The data shown are representative of three independent experiments. (H) The ubiquitination levels of PPARγ in HEK 293 T cells transfected with HA-tagged PPARγ, Flag-tagged TRIM55, and Myc-tagged Ub exposed to DMSO or luteolin (10 μM) in the presence of MG132 (10 μM). The data shown are representative of three independent experiments. (I) Schematic illustrating the model that luteolin is a promising therapeutic compound for pathological cardiac hypertrophy and heart failure by directly targeting PPARγ ubiquitin-proteasomal degradation and metabolic homeostasis. PPARγ, peroxisome proliferator activated receptor γ; DMSO, dimethyl sulfoxide; CHX, cycloheximide; Ub, ubiquitin; PE, phenylephrine; TRIM55, tripartite motif containing 55; NRCM, primary neonatal rat cardiomyocyte; PPRE, PPAR response element.*
Subsequently, we investigated the impact of luteolin on PPARγ ubiquitination levels and found a dramatically reduced ubiquitination level of PPARγ by luteolin treatment (Figure 8E). A previous study reported that tripartite motif containing 55 (TRIM55), a muscle-specific ubiquitin ligase, mediates the ubiquitination-mediated degradation of PPARγ in myocardial tissue [29]. We first confirmed the interaction with and enhancing ubiquitination function of TRIM55 on PPARγ (Figures 8F–H), and further examined the impact of luteolin on PPARγ and TRIM55 complex. The data revealed that luteolin weakened the interaction between TRIM55 and PPARγ and largely reversed TRIM55-induced enhancement of PPARγ ubiquitination level (Figures 8F–H). In summary, PPARγ is the direct pharmacological target of luteolin, and luteolin stabilizes PPARγ protein expression by inhibiting TRIM55-mediated ubiquitinational degradation (Figure 8I).
## Discussion
In this study, based on a high-throughput FDA drug screening, we identified luteolin as a candidate for the management of cardiac hypertrophy. Further in vitro and in vivo experiments verified that luteolin can significantly attenuate pathological cardiac hypertrophy and HF mainly by activating PPARγ pathway. Mechanistically, luteolin treatment stabilizes PPARγ by inhibiting its ubiquitination, thereby indirectly regulating fatty acid and glucose metabolism to exert a protective effect on the heart.
Luteolin is one of the most prevalent flavones flavonoids and is abundant in a wide range of vegetables, fruits, and herbs [10]. Although the therapeutic effects of luteolin on cardiac hypertrophy and HF have been sporadic suggested [11, 13, 14, 30], systemic studies in this regard are still missing. Meanwhile the detailed molecular events and gene expression profiles associated with the histological phenotypes induced by luteolin treatment are also not clear. All these information is imperative for developing luteolin for the treatment against cardiac hypertrophy. To date, previous studies have repeatedly attributed the therapeutic effects of luteolin to suppressing oxidative stress, inflammatory responses, autophagy, and apoptosis via remaining to be verified molecular target [10]. In this study, beyond phenotypic verification, we systematically illustrated that luteolin treatment universally downregulated genes associated with cardiac hypertrophy, fibrosis, and protein synthesis by RNA-sequencing. Notably, we first verified that luteolin can directly bind to PPARγ, a crucial regulator of metabolic homeostasis, prevents its ubiquitination mediated degradation, thereby exerts the protective effect against cardiac hypertrophy and heart failure. And this mechanism is novel relative to what we know regarding the protective effect of luteolin.
PPARγ is a nuclear receptor that regulates glucose and fatty acid metabolism [31]. PPARγ promotes lipid droplet formation and triglyceride lipolysis in myocardial tissue, thus inhibiting the accumulation of cardiac cytotoxic lipids [24]. Cardiomyocyte-specific PPARγ deficiency induces cardiac hypertrophy in mice [25], whereas overexpression of PPARγ in cardiomyocyte enhances cardiac uptake of lipids and glucose [32]. Although the therapeutic outcomes of transgenic mice and rosiglitazone are ambiguous [25, 32], there is increasing evidence that PPARγ is a protective modulator in cardiac hypertrophy (26–28, 33) and heart failure [31, 34]. PPARγ and the downstream signaling can be activated by promoting its expression, stimulating its activity, and preventing its degradation. While various PPARγ agonists have been developed, their application has been limited by hepatotoxicity, cancer risk and cardiac side effects [35]. The expression booster and protein level stabilizer have not been sufficiently explored. In this study, following finding luteolin prevents cardiac hypertrophy and heart failure via activating PPARγ pathway, and we also found that by binding to PPARγ luteolin interrupted the interaction between TRIM55 and PPARγ and prevents the TRIM55-mediated proteasome-dependent degradation of PPARγ. Notably, the role of PPARγ in cancer progression is still in debate. Although there is substantial evidence that PPARγ acts as a tumor suppressor and inhibits tumor cell growth in a variety of cancers, its pro-tumor potential should not be overlooked [36, 37]. Considering the complex role of PPARγ in metabolic regulation and cancer progression, selective PPARγ modulators for cell- or organ-specific modulation are a promising area for future studies. While the PPARγ agonists haven't been able to be applied in clinic, our study demonstrated that the strategies of stabilizing PPARγ can be a key alternative way for pathological cardiac hypertrophy and HF treatment.
It is well known that cardiac metabolism disturbances including the relative lack of energy production and the altered source of energy substrates are thought to be associated with impaired cardiac function in failing failure [38]. During heart failure induced by the prolonged presence of pressure load, there are two significant adaptive changes in myocardial energy metabolism [24, 39]. In the first place, an enlargement of heart weight is accompanied by increased myocardial energy consumption. Secondly, pressure load and low cardiac output chronically activate the renin-angiotensin-aldosterone system and the sympathetic nervous system, altering the nutrient supply to the heart by activating gluconeogenesis, ketogenesis, and lipolysis [40]. Overall, the failing heart is more inclined to glucose [41], ketone bodies, and lactate [42] as energy suppliers, while the proportion of energy supply from fatty acid uptake and oxidation is reduced [43]. In addition, the low oxidative phosphorylation capacity leads to low cardiac metabolic reserve, decrease cardiomyocyte high-energy phosphate content, and ultimately leads to poorer cardiac contractility [44]. Considering PPARγ as a crucial regulator of glucose and fatty acid metabolism, we detected the mRNA levels of genes associated with glucose and fatty acid metabolism in the mice hearts. Interestingly, luteolin effectively reversed cardiac hypertrophy and subsequent HF by augmenting fatty acid metabolism and inhibiting glucose metabolism in failing heart, which may endow luteolin more potential for pathological cardiac hypertrophy and heart failure treatment relative to known therapeutics.
## Conclusion
In summary, our results first revealed that luteolin prevents cardiac hypertrophy and HF by regulating myocardial fatty acid and glucose metabolism, which relies on PPARγ activation. luteolin binds to PPARγ and then interferes with the interaction between PPARγ and TRIM55, reduces the ubiquitination of PPARγ induced by TRIM55, stabilising the PPAR γ protein, and ultimately improves myocardial fatty acid and glucose metabolism. The above results offer new insights into the pathogenesis of pathological cardiac hypertrophy and HF, and add novel evidence for the benefits of PPARγ activation in cardiac hypertrophy and HF.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA939393; https://www.ncbi.nlm.nih.gov/, PRJNA939371.
## Ethics statement
The animal study was reviewed and approved by Renmin Hospital of Wuhan University's Animal Care and Use Committee.
## Author contributions
ZW and WS: carried out the experimental design, conducted the experiments, analyzed the data, and wrote the manuscript. TW and XW: analyzed the transcriptomic dataset. TP and SL: performed the histological experiments. JW, PL, and ZW: performed molecular biology experiments. ZY, RT, and YH: performed animal experiments. HY: synthesed biotin-linked luteolin. YH, LB, and XC: provide valuable suggestions. HL, XZ and ZS: supervised the project and provided suggestion for experiment design. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1130635/full#supplementary-material.
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|
---
title: Identification of hsa_circ_0001445 of a novel circRNA-miRNA-mRNA regulatory
network as potential biomarker for coronary heart disease
authors:
- PhongSon Dinh
- JunHua Peng
- ThanhLoan Tran
- DongFeng Wu
- ChauMyThanh Tran
- ThiPhuongHoai Dinh
- ShangLing Pan
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10043405
doi: 10.3389/fcvm.2023.1104223
license: CC BY 4.0
---
# Identification of hsa_circ_0001445 of a novel circRNA-miRNA-mRNA regulatory network as potential biomarker for coronary heart disease
## Abstract
### Objects
To evaluate the hsa_circ_0001445 level in peripheral blood leukocytes of patients with coronary heart disease (CHD) and its related clinical factors, and predict its circRNA-miRNA-mRNA regulatory network in CHD pathogenesis via bioinformatics analysis.
### Methods
Peripheral blood leukocytes were isolated from the whole blood samples of 94 CHD patients (aged 65.96 ± 9.78 years old) and 126 healthy controls (aged 60.75 ± 8.81 years old). qRT-PCR was used to quantify the expression level of circRNA and subsequently analyze its association with CHD clinical parameters. Via bioinformatics algorithm and GEO datasets, differential miRNA expression was evaluated using the Limma package. A miRNA-mRNA regulatory network was predicted by cyTargetLinker. ClusterProfiler was employed to perform functional enrichment analysis of the circRNA network to investigate its role in CHD pathogenesis.
### Results
The expression of hsa_circ_0001445 in peripheral blood leukocytes of CHD patients was downregulated compared with that of healthy controls. Positive correlations were evident between hsa_circ_0001445 expression level and the levels of hemoglobin, triglycerides, high- and low-density lipoprotein cholesterol. A significant negative correlation was also found between hsa_circ_0001445 expression level and age and the neutrophil level. Low expression of hsa_circ_0001445 exhibited a discriminatory ability between CHD patients and healthy controls with a sensitivity of $67.5\%$ and a specificity of $76.6\%$ ($p \leq 0.05$). By bioinformatics analysis, 405 gene ontology terms were identified. The Kyoto Encyclopedia of Genes and Genomes terms focused principally on the PI3K-Akt signaling pathway. hsa_circ_0001445 was associated with the expression of three miRNAs that may regulate 18 genes involved in KEGG processes: hsa-miR-507, hsa-miR-375–3p, and hsa-miR-942–5p.
### Conclusion
The hsa_circ_0001445 level in peripheral blood leukocytes may serve as a biomarker for CHD diagnosis. Our work on circRNA-miRNA-mRNA networks suggests a potential role for hsa_circ_0001445 in CHD development.
## Introduction
Coronary heart disease (CHD) is the most common heart condition worldwide and the leading cause of death of elderly men and women [1]. Despite recent declines in developed countries, both CHD morbidity and mortality continue to increase rapidly in developing countries. Various factors are involved in CHD pathogenesis, including older age, dyslipidemia, obesity, psychological issues, hypercholesterolemia, diabetes, and family history (2–4). Currently, most efforts focus on biochemical tests, protein and gene based biomarkers to predict the incidence of CHD [5, 6]. However, the ability of these factors in terms of early detection of CHD are still controversial [5]. Recently, circular RNAs (circRNAs) have emerged as a novel potential non-invasive biomarker for diagnosis and prognostic of CHD patients.
CircRNAs are a kind of non-coding RNA that consists of continuous covalently closed loops without the 3′- and 5′ end like linear RNA, which enables it to resist degradation, and thus has relative conservation and stability [7]. Recently, their functions and biological features have been extensively studied. They modify gene expression by serving as microRNA (miRNA) sponges that bind and inactivate certain miRNAs (8–10). Research on their roles in cardiovascular diseases has progressed rapidly. In particular, in-depth studies of the relationship between circRNA and CHD have provided effective tools to early diagnose CHD and by that reduced CHD mortality [5, 7, 11].
Recently, Vilades et al. [ 6] showed that the plasma levels of the circRNA hsa_circ_0001445 (hsa_circSMARCA5_013) were proportional to coronary atherosclerotic burden. hsa_circ_0001445 has been consistently detected in clinically relevant samples, including heart tissue [12], plasma [13], serum [14], and whole blood [15]. In this study, we first investigated the levels of hsa_circ_0001445 in peripheral blood leukocytes (PBLs) of CHD patients from Guangxi, China [6], and analyzed its correlation with clinical characteristics. Next, we performed bioinformatics analyses to define a novel circRNA-miRNA-mRNA network involved in CHD. Finally, we conducted functional and pathway enrichment analyses of potentially relevant genes. Our findings may provide potential candidate for further studies on the pathogenesis of CHD.
## Study population
The experimental group included 94 CHD patients aged 65.96 ± 9.78 years (58 men and 36 women) admitted to the Department of Cardiology of the People's Autonomous Hospital of Guangxi Zhuang from January 1 2019 to December 31 2020. All patients underwent coronary angiography (CAG), and those with stenoses ≥$50\%$ in at least one of the three main coronary arteries or their major branches (diameter ≥2 mm) were diagnosed with CHD. The exclusion criteria were diabetes mellitus, any other clinically acute or chronic inflammatory systemic disease, uncontrolled hypertension, liver or kidney dysfunction, endocrine disease, autoimmune disease, a malignancy, prior percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), and a history of CHD. The control group included 126 healthy subjects aged 60.75 ± 8.82 years (61 men and 65 women) recruited in the same period from the Second Affiliated Hospital of Guangxi Medical University. They were confirmed healthy after physical check-ups; none had a history of coronary atherosclerosis or microvascular disease. This study was conducted in accordance with the Declaration of Helsinki [1975] and was approved by the ethics committee of Guangxi Medical University (approval no. 2019-SB-060). All patients and controls gave written informed consent.
## Total RNA extraction
Total RNAs were extracted from PBLs of CHD patients and healthy controls using the SanPrep column microRNA extraction kits (Sangon Biotech, China); all samples were stored at −80°C.
## Reverse transcription polymerase chain reaction (RT-PCR)
RNA reverse transcription into cDNA was performed using 5× HiScript III qRT SuperMix kits (Vazyme Biotech, China) according to the manufacturer's instructions. One microgram of RNA and 4x gDNA wiper mix were incubated at 42°C for 2 min, then 5× HiScript III qRT SuperMix was added, followed by incubation at 37°C for 15 min and 85°C for 5 s. The products served as qRT-PCR templates.
## qRT-PCR analysis
The expression level of hsa_circ_0001445 was detected by qRT-PCR with Light Cycler 96 platform (Roche, USA), and Glyceraldehyde-3-phosphate dehydrogenase (hGAPDH) served as the internal standard for normalization. The specific primers were listed as follows: (hsa_circ_0001445) forward primer: 5′-TGGGCGAAAGTTCACTTAGAA-3′, reverse primer: 5′- CACATGTGTTGCTCCATGTCT-3′; (hGAPDH) forward primer: 5′- TGTTGCCATCAATGACCCCTT-3′, reverse primer: 5′-CTCCACGACGTACTCAGCG-3′ [6, 16]. Each sample was performed in triplicate. The reaction conditions included of 40 cycles at 95°C for 3 min, 95°C for 10 s, and 60°C for 60s; the dissolution curves were obtained via one cycle at 95°C for 5 s, 60°C for 1 min, and 97°C for 1 s. The expression level of hsa_circ_0001445 was calculated using the 2−ΔCt method relative to hGAPDH.
## Statistical analysis
Data were statistically analyzed using SPSS 22.0 (SPSS Inc. Chicago, IL, USA) and GraphPad Prism 8 (GraphPad Software, San Diego, California, USA). Continuous data were presented as mean ± standard deviation (means ± SD) if normally distributed, and otherwise as median (interquartile range). The circRNA expression levels between CHD and controls were compared using the Student's t-test (calculated by SPSS) to determine statistically significant difference between the means of two groups. Spearman's rho coefficient was used to assess the correlation between continuous variables. Logistic regression was used to assess relationships between various factors and the PBL levels of hsa_circ_0001445. A p-value < 0.05 was considered significant. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the optimal area under the ROC curve (AUC) for evaluating the CHD diagnostic ability of hsa_circ_0001445.
## Differential miRNA expression and construction of the miRNA-mRNA regulatory network
miRNA expression levels of CHD patients were collected from the public Gene Expression Omnibus database (GEO) (https://www.ncbi.nlm.nih.gov/geo/) using the following criteria: peripheral blood cells from humans, ≥3 samples of patients and normal controls, and miRNAs expression in CHD patients. The relevant datasets were GSE105449 (GPL22949) [17] and GSE61741 (GPL9040) [18]. There were 236 samples in total (136 control and 100 CHD) that met the above criteria. GSE IDs, the high-throughput data, and the annotated subject characteristics of the control and CHD groups were collected in a series matrix file and were analyzed using R software ver. 3.6.2.
R software (Version 3.6.2; R Foundation for Statistical Computing, Vienna, Austria) (https://www.r-project.org/), the Bioconductor software package (https://bioconductor.org/packages) [19], and the limma package [20] were used to analyze differential miRNA expression based on miRNA expression data. All p-values were corrected using the false discovery rate (FDR) correction toolkit. A p-value <0.05 and with an FDR < 0.05 for all GSE files (fold change >1) were considered significant. Then, Venn diagrams were framed to identify overlapping miRNAs among the predicted datas in order to determine which potential miRNAs were associated with hsa_circ_0001445 in CHD. miRNAs identified as significant were entered into Cytoscape ver. 3.6.1 (http://cytoscape.org/) and were used to establish a network. A regulatory network was predicted by cyTargetLinker (https://cytargetlinker.github.io/) using the targetscan-hsa-7 and miRTarBase-hsa-7 databases [21]. Finally, a miRNA-mRNA network was constructed.
## Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses
ClusterProfiler ver. 3.14.3 package in R software was used to analyze the gene ontology and signaling pathways. Genome-wide annotation for humans was based on mapping employing the *Entrez* gene identifiers; we used several methods to visualize and interpret the functional enrichment results. A p-value <0.05 served as a cut-off when determining significant enrichments of GO terms and KEGG pathways. That is, the more likely that the gene associated with the listed entry/pathway influences cellular life activities and warrants further research [22]. Venny 2.1 diagram was used to improve predictive accuracies (via intersection) (https://bioinfogp.cnb.csic.es/tools/venny/). Finally, the interaction network of miRNAs with genes of the KEGG pathway was established.
## Expression of hsa_circ_0001445 in PBLs of CHD patients
The expression level of hsa_circ_0001445 in PBLs of CHD patients was significantly lower than that of healthy controls (0.640 ± 0.254 and 1.079 ± 0.453, respectively) ($p \leq 0.001$) (Figure 1).
**Figure 1:** *Expression levels of hsa_circ_0001445 in the healthy and CHD groups. p < 0.001.*
## Clinical characteristics comparison between CHD patients and healthy controls groups
We compared the age, sex, blood parameters, fasting blood glucose levels (GLU), three renal function items (UREA, CREA, and UA), three liver enzymes (AST, ALT, GGT) and cholesterol levels of the two groups. As shown in Table 1, age, white blood cell count (WBC), and levels of hematocrit (HCT), neutrophils (NEU), red blood cells (RBCs), hemoglobin (HGB), platelets (PLT), alanine aminotransferase (ALT), aspartate transaminase (AST), glutamyl transpeptidase (GGT), glucose (GLU), total cholesterol (T-Cho), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) significantly differed between the two groups (all $p \leq 0.05$).
**Table 1**
| Clinical parameters | Healthy | CHD | P value |
| --- | --- | --- | --- |
| Age | 60.746 ± 8.817 | 65.957 ± 9.780 | <0.001 |
| Sex (Male/Female) | 61/65 | 58/36 | 0.051 |
| WBC (109/L) | 6.2387 ± 1.475 | 6.877 ± 2.289 | 0.013 |
| NEUTR (109/L) | 3.485 ± 1.115 | 4.234 ± 1.779 | <0.001 |
| RBC (1012/L) | 4.815 ± 0.606 | 4.497 ± 0.620 | <0.001 |
| HGB (g/L) | 138.218 ± 11.815 | 131.489 ± 15.641 | <0.001 |
| HCT | 41.610 ± 3.199 | 39.485 ± 4.373 | <0.001 |
| PLT (109/L) | 251.108 ± 59.333 | 228.391 ± 64.189 | 0.008 |
| AST (U/L) | 20.246 ± 5.353 | 31.607 ± 35.559 | 0.001 |
| ALT (U/L) | 17.802 ± 6.516 | 29.122 ± 46.475 | 0.008 |
| GGT (U/L) | 26.776 ± 17.127 | 40.417 ± 40.246 | 0.001 |
| Urea (mmol/L) | 4.981 ± 1.130 | 5.351 ± 2.176 | 0.109 |
| Crea (µmol/L) | 79.429 ± 15.032 | 91.765 ± 115.907 | 0.239 |
| UA (µmol/L) | 329.659 ± 74.302 | 341.455 ± 129.966 | 0.464 |
| GLU (mmol/L) | 4.986 ± 0.502 | 4.677 ± 0.712 | <0.001 |
| T-Cho (mmol/L) | 4.963 ± 1.038 | 4.457 ± 1.098 | 0.001 |
| TG (mmol/L) | 1.356 ± 0.70031 | 1.454 ± 0.981 | 0.416 |
| HDL-C (mmol/L) | 1.454 ± 0.398 | 1.110 ± 0.275 | <0.001 |
| LDL-C (mmol/L) | 3.245 ± 0.852 | 2.642 ± 0.861 | <0.001 |
## Relationships between PBL hsa_circ_0001445 expression level and clinical characteristics
Clinical parameters associated with hsa_circ_0001445 expression level in CHD patients were shown in Table 2. Its expression was negatively correlated with age and NEU but positively correlated with HGB, T-Cho, HDL-C, and LDL-C ($p \leq 0.05$).
**Table 2**
| Factors | hsa_circ_0001445 | hsa_circ_0001445.1 |
| --- | --- | --- |
| Factors | r | P value |
| Age | −0.290 | <0.001 |
| WBC (109/L) | −0.088 | 0.195 |
| NEUTR (109/L) | −0.137 | 0.043 |
| RBC (1012/L) | 0.142 | 0.036 |
| HGB (g/L) | 0.181 | 0.007 |
| HCT | 0.172 | 0.011 |
| PLT (109/L) | 0.039 | 0.566 |
| AST (U/L) | −0.103 | 0.136 |
| ALT (U/L) | −0.021 | 0.781 |
| GGT (U/L) | −0.102 | 0.143 |
| GLU (mmol/L) | 0.063 | 0.356 |
| T-Cho (mmol/L) | 0.249 | <0.001 |
| HDL-C (mmol/L) | 0.284 | <0.001 |
| LDL-C (mmol/L) | 0.293 | <0.001 |
## Logistic regression analysis of associations between hsa_circ_0001445 expression level and clinical characteristics of CHD patients
We used multivariate logistic regression to explore whether hsa_circ_0001445 independently predicted CHD. Model 1 included NEU and HGB; model 2 included T-Cho, HDL-C, and LDL-C; and model 3 included NEU, HGB, T-Cho, HDL-C, and LDL-C. Based on the median 2−ΔCt values for hsa_circ_0001445, the CHD group was subdivided into those with low and high circRNA expression. In addition, based on the normal ranges and median values of NEU, HGB, T-Cho, and LDL-C, patients were subdivided into high and normal subgroups. The HDL-C values were used to define normal- and low-expression subgroups.
Low hsa_circ_0001445 expression level was an independent risk factor for CHD in all three models (Table 3).
**Table 3**
| Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value |
| hsa_circ_0001445 | 0.175 | <0.001 | 0.158 | <0.001 | 0.163 | <0.001 |
| hsa_circ_0001445 | (0.096–0.319) | <0.001 | (0.081–0.306) | <0.001 | (0.084–0.318) | <0.001 |
## ROC curve analysis and the AUC of hsa_circ_0001445 for diagnosing CHD
ROC curve analyses of the healthy and CHD groups showed that the AUC for hsa_circ_0001445 was 0.816 ± 0.028 ($95\%$ CI 0.761–0.871; $p \leq 0.001$; Figure 2). It had the highest J index [(Se + Sp − 1) = 0.441]; a 2−ΔCt value of 0.814 was thus chosen as the cut-off. The sensitivity was $67.5\%$, the specificity was $76.6\%$, and the likelihood ratio [Se/(1-Sp)] was 2.88.
**Figure 2:** *ROC curve analysis of hsa_circ_0001445. The AUC shows the diagnostic ability of hsa_circ_0001445 in terms of CHD (p < 0.001).*
## Differentially expressed miRNAs of CHD and the predicted miRNA-mRNA regulatory networks
We found 132 differentially expressed miRNAs in CHD, including 79 that were upregulated and 53 that were downregulated in GSE105449 and GSE61741 dataset; all met the cutoff criteria of abs (log2FC) >1 and $p \leq 0.05$ (Supplementary Table S1). The top 10 up- and downregulated miRNAs are listed in Tables 4, 5.
The Circbank and Circinteractome databases were used to predict interactions between hsa_circ_0001445 and miRNAs. We identified 26 interacting pairs.
We used Venny ver. 2.1 to visualize the Circbank and Circinteractome data, the upregulated miRNAs of GSE105449 and GSE61741 dataset; and their intersections (Figure 3). The predicted target miRNAs of hsa_circ_0001445 were hsa-miR-507, hsa-miR-375–3p, hsa-miR-576–5p, and hsa-miR-942–5p. These four miRNAs were entered into Cytoscape ver. 3.6.1. The CyTargetLinker app simply links Cytoscape networks to miRNA-mRNA interactions. A total of 1,175 mRNAs were obtained and the Gene IDs were converted into Entrez IDs for GO and KEGG analyses using R software and the Perl tool.
**Figure 3:** *The Venn diagram for the overlap of numbers of the predicted miRNAs.*
## GO and KEGG analysis
GO terms were analyzed using R ver. 3.6.2 and clusterProfiler ver. 3.14.3 followed by pathway enrichment. A total of 405 GO terms were found (Supplementary Table S2). These covered molecular function pathways, as shown in the table. The GO terms were ranked by their adjusted p-values (p-value <0.05; p-adjusted <0.05) (Figure 4).
**Figure 4:** *Go enrichment analysis. BP, biological process; CC, cellular component; MF, molecular function.*
The GO terms indicated biological process pathways such as dephosphorylation, negative regulation of phosphorylation, and venous blood vessel development (Figure 5).
**Figure 5:** *Top 10 GO terms with the lowest p-values. Biological pathways and associated genes were identified. Each node indicates a biological process in the path. The lines are the interactions between the genes and the terms.*
KEGG enrichment analysis identified 70 pathways (p-values <0.05) (Supplementary Table S3). The top 30 are summarized in Figure 6.
**Figure 6:** *KEGG pathway analysis and the numbers of associated genes. The pathways and the associated genes were ranked based on the p-values. The sizes of the dots represent the gene counts of each row (KEGG categories).*
To enhance accuracy, the results afforded by the mirtarbase and targetscan databases were intersected with those described above (Figure 7). This yielded 63 genes that were compared to KEGG pathway genes. We found 18 potential KEGG genes. Finally, we built a subnetwork of the interactions between miRNAs and these genes. This included the signaling pathways mentioned.
**Figure 7:** *Mechanism of action of hsa_circ_0001445. (A) Venn diagram of genes predicted by mirtarbase and targetscan. (B) The subnetwork of the interactions between miRNAs and these genes. (C) Functional analysis of potential genes of the KEGG pathway using Cytoscape ver. 3.6.1.*
## Discussion
Identifying factors involved in CHD pathogenesis can not only improves our understanding of its development but also suggests new approaches to the diagnosis, prognosis, and management of CHD. Numerous biomarkers associated with CHD have been applied, however, to identify its primary stage by regular examinations, such as cardiac ultrasound and electrocardiography, remains challenging. CHD diagnosis in its early stage with a sensitive biomarker are crucial for treatment and prognosis. Recent large-scale studies have suggested that circRNAs play an essential role in the pathogenesis and progression of CHD [5, 6, 12, 23]. In this study, we identified for the first time the circRNA hsa_circ_0001445 as a potential diagnostic biomarker for CHD. By evaluating the expression level of hsa_circ_0001445 in CHD patients and control group, our results showed that the expression of hsa_circ_0001445 in PBLs of CHD patients was significantly lower than that of healthy controls. ROC curve analysis indicated that its expression level well-distinguished CHD patients from healthy subjects with AUC valued 0.816 ± 0.028 ($95\%$ CI 0.761–0.871; $p \leq 0.001$). In addition, logistic regression revealed that hsa_circ_0001445 was an independent predictor of CHD. Further studies on hsa_circ_0001445 as well as other circRNAs involved in CHD is required to investigate their potential in CHD diagnosis.
The RNA binding protein Quaking (QKI) promotes circRNA formation [24]. QKI was proven to be involved in cell differentiation, apoptosis, proliferation, and migration [25]. Interestingly, recent works have shown that QKI affects cardiovascular development and function (26–29), suggesting that QKI may affect the expression of hsa_circ_0001445 in CHD patients and consequently act on the disease progression. Several studies have also indicated that hsa_circ_0001445 may be involved in CHD development. Vilades et al. [ 6] found that plasma levels of hsa_circ_0001445 are lower in patients with higher coronary atherosclerotic burdens. Moreover, hsa_circ_0001445 is expressed by human coronary smooth muscle cells in vitro; its secretion is reduced under atherosclerotic conditions [6]. Cai et al. [ 30] found that hsa_circ_0001445 is downregulated during low lipoprotein-induced oxidation of human umbilical vein endothelial cells. Overexpression of hsa_circ_0001445 promotes cell proliferation and inhibits the inflammatory response and apoptosis. These findings and our work suggest that hsa_circ_0001445 may involve in CHD development. Since the molecular mechanism of CHD is complicated, as is the effect of hsa_circ_0001445 on CHD, further research is required to elucidate its role in CHD pathogenesis.
The circRNA-miRNA-mRNA axis has been recently researched in terms of how circRNAs regulate CHD development. Lin et al. [ 31] constructed a network graph of the correlations between hsa-miR-101–5 and each of hsa_circ_0030769, hsa_circ_15486–161, hsa_circ_0122274, and hsa_circ_0079828. Miao et al. [ 5] hypothesized that the miRNAs regulated by hsa_circ_0016274 are miRNA-361–5p, miR-21–3p, miRNA-296–3p, and miRNA-375. The target genes of the circ-YOD1-miR-21–3p/miR-296–3p axis are BCL6, FBXL18, MMP9, and FCGR3B, as confirmed in other studies describing associations of MMP9, BCL6 [32, 33], hsa-miR-21–3p [32], and hsa-miR-296–3p [34] with CHD. In another study, the network of circ_ZNF609-related miRNAs included hsa-miR-615–5p, hsa-miR-145–5p, hsa-miR-138–5p, hsa-miR-150–5p [35], and AKT1 (a downstream target of miR-138–5p) [36]. In this study, we predicted that certain genes affect CHD through bioinformatics analysis. This finding is in agreement with previous reports as JAK2 [37, 38], FZD4 [39], PDGFC [40], YWHAZ [41], SP1 [42], and LRP5 (43–45) which are all targets of hsa-miR-375–3p [46, 47]. *These* genes participate in many signaling pathways, of which the PI3K-Akt pathway may play a major role in CHD (48–52). Phosphoinositide 3-kinase (PI3K) lies downstream of many receptor tyrosine kinases. PI3Ks play crucial roles in many aspects of biological response, such as membrane trafficking, cytoskeletal organization, cell growth and apoptosis. A serine/threonine kinase Akt, also known as protein kinase B, is the most well characterized target of PI3K. Akt is known to mediate cell survival signal by regulating several effectors, increases the rate of initiation of translation of mRNA by ribosomes such as Bad or procaspase-9, p70S6K. Stimulating and activating the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway can regulate the expression of vascular endothelial cytokines, the polarization and survival of macrophages, the expression of inflammatory factors and platelet function, thus affecting the occurrence and development of CHD. This reinforces the authenticity of our interactive network prediction. The integrated biological information predicts the potential molecular mechanism of hsa_circ_0001445.
This study had certain limitations. First, it was a cross-sectional study with a modest sample size. Our findings require confirmation in larger studies to obtain higher reliability. Second, we did not perform luciferase assay, WB assay; but only database-derived links. Those binding assays would reinforce our suggestion that hsa_circ_0001445 is a good candidate biomarker of CHD. The mechanism by which the circRNA-miRNA-mRNA axis regulates CHD pathogenesis requires further in vivo and in vitro research.
## Conclusion
We identified hsa_circ_0001445 as a potential biomarker for CHD diagnosis. The predicted genes involved in CHD participate in many signaling pathways, of which PI3K-Akt signaling may be particularly relevant to CHD. Our results provide a basis for further research on the molecular mechanism of hsa_circ_0001445 in CHD pathogenesis.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Ethics statement
The studies involving human participants were reviewed and approved by No. 2019-SB-060. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
PSD interpreted data, performed the statistical analyses and drafted the manuscript. TPHD and CMTT participated in the design and analyzed the bioinformatics results. TLT and DFW carried out the epidemiological survey and collected the samples. JHP, SLP designed the study, revised the manuscript and supervised the whole work. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1104223/full#supplementary-material.
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|
---
title: Genetic polymorphisms in CYP4F2 may be associated with lung cancer risk among
females and no-smoking Chinese population
authors:
- Hongyang Shi
- Yonghong Zhang
- Yu Wang
- Ping Fang
- Yun Liu
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10043406
doi: 10.3389/fonc.2023.1114218
license: CC BY 4.0
---
# Genetic polymorphisms in CYP4F2 may be associated with lung cancer risk among females and no-smoking Chinese population
## Abstract
### Background
Our study aimed to explore the potential association of CYP4F2 gene polymorphisms with lung cancer (LC) risk.
### Methods
The five variants in CYP4F2 were genotyped using Agena MassARRAY in 507 cases and 505 controls. Genetic models and haplotypes based on logistic regression analysis were used to evaluate the potential association between CYP4F2 polymorphisms and LC susceptibility.
### Results
This study observed that rs12459936 was linked to an increased risk of LC in no-smoking participants (allele: OR = 1.38, $$p \leq 0.035$$; homozygote: OR = 2.00, $$p \leq 0.035$$; additive: OR = 1.40, $$p \leq 0.034$$) and females (allele: OR = 1.64, $$p \leq 0.002$$; homozygote: OR = 2.57, $$p \leq 0.006$$; heterozygous: OR = 2.56, $$p \leq 0.001$$; dominant: OR = 2.56, $p \leq 0.002$; additive: OR = 1.67, $$p \leq 0.002$$). Adversely, there was a significantly decreased LC risk for rs3093110 in no-smoking participants (heterozygous: OR = 0.56, $$p \leq 0.027$$; dominant: OR = 0.58, $$p \leq 0.035$$), rs3093193 (allele: OR = 0.66, $$p \leq 0.016$$; homozygote: OR = 0.33, $$p \leq 0.011$$; recessive: OR = 0.38, $$p \leq 0.021$$; additive: OR = 0.64, $$p \leq 0.014$$), rs3093144 (recessive: OR = 0.20, $$p \leq 0.045$$), and rs3093110 (allele: OR = 0.54, $$p \leq 0.010$$; heterozygous: OR = 0.50, $$p \leq 0.014$$; dominant: OR = 0.49, $$p \leq 0.010$$; additive: OR = 0.54, $$p \leq 0.011$$) in females.
### Conclusions
The study demonstrated that CYP4F2 variants were associated with LC susceptibility, with evidence suggesting that this connection may be affected by gender and smoking status.
## Introduction
Lung cancer (LC) has been regarded as one of the most common causes of cancer-related death worldwide over the past few decades, with an estimated 2.1 million new diagnoses of LC in 2018, accounting for $12\%$ of the total increase in cancer cases [1]. In recent years, the incidence of LC in China has been consistent with the global trend, showing a rapid increase, and LC has since become the main cause of cancer-related deaths in China [2]. It is predicted that the mortality of LC in *China is* likely to increase by about $40\%$ between 2015 and 2030 [3]. Despite advances in early detection, the majority of LC patients are often diagnosed at a later stage, resulting in a 5-year overall survival rate of only $10\%$ to $15\%$, according to statistics [4]. The burden of LC on our society is increasing day by day and cannot be ignored. Various factors can predispose people to LC, with smoking being the most prevalent factor. In addition, other potential risk factors include gender, age, race, ethnicity, and especially single nucleotide polymorphisms (SNPs) [5, 6].
Cytochrome P450s (CYP), phase I drug metabolizing enzymes, encode 57 CYP proteins in the human genome and are responsible for the metabolism of numerous endogenous and xenobiotic compounds [7]. The CYP4F2 gene, a member of the CYP450 superfamily, is an ω-hydroxylase that catalyzes the first step of the vitamin E metabolic pathway [8], as well as the metabolism of arachidonic acid (AA) to generate 20-hydroxyethyl hexadecanoic acid (20-HETE) through ω-hydroxylation [9]. 20-HETE is known to promote tumorigenesis by increasing a variety of pro-inflammatory mediators, cytokines, and chemokines. Previous studies have demonstrated that the elevated expression of CYP4F2 enzymes and 20-HETE is closely related to ovarian cancer [10]. We hypothesized that CYP4F2 might be involved in tumor genesis and development by accelerating the production of 20-HETE. Additionally, Geng et al. have proved that rs1558139 and rs2108622 of CYP4F2 are associated with hypertension, and the association between rs1558139 and hypertension is particularly strong in men [11]. Despite this, there is a lack of studies investigating the association between CYP4F2 polymorphisms and LC risk.
In this case–control study, five SNPs (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) in CYP4F2 were genotyped by the Agena MassARRAY platform. The gender- and smoking-stratified analyses on the correlation between CYP4F2 variants and LC risk were performed.
## Study subjects
A total of 507 newly diagnosed LC patients (353 males and 154 females) were randomly recruited from the Second Affiliated Hospital of Xi’an Jiaotong University in the case–control association analysis between CYP4F2 polymorphisms and the risk of LC. All patients had no history of any other cancers and had not received chemotherapy before acquiring blood samples. Further, the control group comprised 505 unrelated healthy controls (354 males and 151 females) from the physical examination center of the hospital. Information about all subjects, including age, gender, height (cm), weight (kg), smoking status, drinking status, tumor stage, and lymph node metastasis, was collected from questionnaires and clinical data. Peripheral blood samples were collected from all study subjects into vacutainer tubes containing EDTA, and genomic DNA was then isolated from the collected blood samples using the GoldMag-Mini Purification Kit (GoldMag Co. Ltd., Xi’an, China) and stored at −80°C. DNA concentration and purity were determined by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
## SNP selection and genotyping
In this study, five SNPs (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) in CYP4F2 were selected according to previously published studies on the association between CYP4F2 polymorphisms and disease susceptibility (12–14). The genotype distributions of the candidate SNPs in controls met Hardy–*Weinberg equilibrium* (HWE) ($p \leq 0.05$). All the candidate SNPs had a minor allele frequency (MAF) of >$5\%$ in the Han Chinese in Beijing (CHB) population from the 1,000 Genomes Project (http://www.internationalgenome.org/). The primers for five SNPs were designed by Agena Bioscience Assay Design Suite version 2.0 software. The polymorphisms were genotyped using the Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA) with iPLEX gold chemistry. Ultimately, Agena Bioscience TYPER version 4.0 software was used for data management and genotyping result analysis.
## Expression analysis
We extracted the data for CYP4F2 expression in normal lung tissues and lung squamous cell carcinoma (LUSC) tissues under different subgroups from the TCGA database and analyzed them via UALCAN (http://ualcan.path.uab.edu/index.html), which is an interactive web resource for tumor subgroup gene expression analysis and survival analysis.
## Statistical analysis
SPASS version 22.0 software was applied for statistical analysis. HWE was calculated for the control group by the chi-square test. Differences in the continuous characteristic (age) and categorical variable (gender) between patients with LC and controls were measured by the student’s t-test and Pearson Chi-Square test, respectively. The correlation between CYP4F2 variants and LC susceptibility was evaluated by logistic regression analysis adjusted for age and gender using PLINK software (version 1.07) under multiple genetic models (allele, genotype, dominant, recessive, and additive). Odds ratio (OR) and $95\%$ confidence interval (CI) were calculated to assess the relationship between CYP4F2 SNPs and LC risk (OR = 1: no impact; OR <1: protective factor; OR >1: risk factor). Finally, PLINK (version 1.07) and Haploview (version 4.2) softwares were used to analyze the pairwise linkage disequilibrium (LD) among five SNPs and generate an LD map to observe the linkage degree among them based on D’ and r-squared values. The SNPStats software (https://www.snpstats.net/start.htm) was used to estimate the correlation between CYP4F2 haplotypes and LC risk. In our study, the p-values of all tests were two-sided, and $p \leq 0.05$ was considered statistically significant.
## Participant characteristics
The mean ages of 507 LC patients and 505 unrelated healthy controls were 61.30 ± 8.32 years and 58.91 ± 9.58 years, respectively (Table 1). In our study, there were no statistically significant differences in age ($$p \leq 0.525$$) and gender ($$p \leq 0.870$$) distribution between cases and controls.
**Table 1**
| Variables | Cases (N = 507) | Controls (N = 505) | p-value |
| --- | --- | --- | --- |
| Age (mean ± SD), years | 61.30 ± 8.32 | 58.91 ± 9.58 | 0.525 |
| >60 | 271 (53%) | 270 (53%) | 0.973 |
| ≤60 | 236 (47%) | 235 (47%) | |
| Sex | | | 0.870 |
| Male | 353 (70%) | 354 (70%) | |
| Female | 154 (30%) | 151 (30%) | |
| BMI (kg/m2) | BMI (kg/m2) | BMI (kg/m2) | BMI (kg/m2) |
| <24 | 316 (62%) | 146 (29%) | |
| ≥24 | 177 (35%) | 161 (32%) | |
| Absence | 14 (3%) | 198 (39%) | |
| Smoking status | Smoking status | Smoking status | Smoking status |
| Yes | 251 (50%) | 136 (27%) | |
| No | 250 (49%) | 140 (28%) | |
| Absence | 6 (1%) | 229 (45%) | |
| Drinking status | Drinking status | Drinking status | Drinking status |
| Yes | 114 (22%) | 109 (22%) | |
| No | 356 (70%) | 135 (27%) | |
| Absence | 27 (8%) | 261 (51%) | |
| Histology | Histology | Histology | Histology |
| Adenocarcinoma | 187 (37%) | | |
| Squamous | 119 (23%) | | |
| Absence | 201 (40%) | | |
| LN metastasis | LN metastasis | LN metastasis | LN metastasis |
| Yes | 214 (42%) | | |
| No | 84 (17%) | | |
| Absence | 209 (41%) | | |
| Stage | Stage | Stage | Stage |
| I, II | 83 (16%) | | |
| III, IV | 260 (51%) | | |
| Absence | 164 (33%) | | |
## Basic information about the selected SNPs in CYP4F2
The basic information about the five SNPs in CYP4F2 (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) among cases and controls was displayed (Table 2), including gene, SNP ID, position, alleles, HWE, and OR ($95\%$ CI). The five SNPs in controls were in accordance with HWE ($p \leq 0.05$). We further evaluated the association between the five SNPs and LC susceptibility by logistic regression (Table 2). The four genetic models (genotype, dominant, recessive, and additive) were also applied to analyze the association by logistic regression adjusted for age and gender (Table S1). Unfortunately, there was no significant association between these five SNPs in CYP4F2 and LC susceptibility under the allelic and genetic models.
**Table 2**
| SNP ID | Position | Alleles | Role | MAF | MAF.1 | HWE p-value | OR (95% CI) | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SNP ID | Position | A/B | Role | Case | Control | HWE p-value | OR (95% CI) | p-value |
| rs3093203 | Chr19:15878374 | C/T | 3’UTR | 0.240 | 0.229 | 1.000 | 1.06 (0.86–1.31) | 0.558 |
| rs3093193 | Chr19:15881104 | C/G | intronic | 0.288 | 0.301 | 0.525 | 0.94 (0.78–1.14) | 0.533 |
| rs12459936 | Chr19:15882231 | C/T | intronic | 0.463 | 0.450 | 0.720 | 1.05 (0.88–1.26) | 0.557 |
| rs3093144 | Chr19:15891487 | A/G | intronic | 0.187 | 0.172 | 0.755 | 1.11 (0.88–1.39) | 0.377 |
| rs3093110 | Chr19:15896974 | C/T | intronic | 0.105 | 0.129 | 0.694 | 0.79 (0.06–1.03) | 0.084 |
## Stratification analysis by smoking status
The smoking-stratified analysis (Table 3) was performed to examine the relationship between CYP4F2 variants and LC risk. Our results showed that rs12459936 in CYP4F2 was associated with an increased risk of LC in no-smoking individuals under the allele (T vs. C: OR = 1.38, $95\%$ CI: 1.02–1.85, $$p \leq 0.035$$), genotype (TT vs. CC: OR = 2.00, $95\%$ CI: 1.05–3.82, $$p \leq 0.035$$), and additive (OR = 1.40, $95\%$ CI: 1.03–1.92, $$p \leq 0.034$$) models. On the contrary, rs3093110 was found to have a protective effect against LC risk in no-smoking individuals under the genotype (GA vs. AA: OR = 0.56, $95\%$ CI: 0.33–0.94, $$p \leq 0.027$$) and dominant (GG + GA vs. AA: OR = 0.58, $95\%$ CI: 0.35–0.96, $$p \leq 0.035$$) models.
**Table 3**
| SNP ID | Models | Genotypes | No smoking | No smoking.1 | No smoking.2 | No smoking.3 | Smoking | Smoking.1 | Smoking.2 | Smoking.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SNP ID | Models | Genotypes | Cases (%) | Controls (%) | OR (95% CI) | p-value | Cases (%) | Controls (%) | OR (95% CI) | p-value |
| rs12459936 | Allele | C | 259 (51.8%) | 167 (59.6%) | 1 | | 279 (55.6%) | 151 (55.5%) | 1 | |
| rs12459936 | | T | 241 (48.2%) | 113 (40.4%) | 1.38 (1.02–1.85) | 0.035 | 223 (44.4%) | 121 (44.5%) | 1.00 (0.74–1.34) | 0.987 |
| rs12459936 | Genotype | CC | 63 (25.2%) | 47 (33.6%) | 1 | | 83 (33.1%) | 45 (33.1%) | 1 | |
| rs12459936 | | TT | 54 (21.6%) | 20 (14.3%) | 2.00 (1.05–3.82) | 0.035 | 55 (21.9%) | 30 (22.1%) | 1.00 (0.56–1.77) | 0.993 |
| rs12459936 | | TC | 133 (53.2%) | 73 (52.1%) | 1.35 (0.83–2.18) | 0.223 | 113 (45.0%) | 61 (44.9%) | 1.00 (0.62–1.62) | 0.989 |
| rs12459936 | Dominant | CC | 63 (25.2%) | 47 (33.6%) | 1 | | 83 (33.1%) | 45 (33.1%) | 1 | |
| rs12459936 | | TT + TC | 187 (74.8%) | 93 (66.4%) | 1.49 (0.94–2.35) | 0.090 | 168 (66.9%) | 91 (66.9%) | 1.00 (0.64–1.56) | 0.995 |
| rs12459936 | Recessive | TC + CC | 196 (78.4%) | 120 (85.7%) | 1 | | 196 (78.1%) | 106 (77.9%) | 1 | |
| rs12459936 | | TT | 54 (21.6%) | 20 (14.3%) | 1.65 (0.94–2.91) | 0.084 | 55 (21.9%) | 30 (22.1%) | 1.00 (0.60–1.65) | 0.986 |
| rs12459936 | Additive | TT + TC + CC | – | – | 1.40 (1.03–1.92) | 0.034 | – | – | 1.00 (0.75–1.33) | 0.996 |
| rs3093110 | Allele | A | 453 (90.6%) | 243 (86.8%) | 1 | | 445 (88.6%) | 244 (90.4%) | 1 | |
| rs3093110 | | G | 47 (9.4%) | 37 (13.2%) | 0.68 (0.43–1.08) | 0.099 | 57 (11.4%) | 26 (9.6%) | 1.20 (0.74–1.96) | 0.461 |
| rs3093110 | Genotype | AA | 206 (82.4%) | 104 (74.3%) | 1 | | 198 (78.9%) | 111 (82.2%) | 1 | 0.719 |
| rs3093110 | | GG | 3 (1.2%) | 1 (0.7%) | 1.26 (0.13–12.41) | 0.844 | 4 (1.6%) | 2 (1.5%) | 1.09 (0.20–6.04) | 0.923 |
| rs3093110 | | GA | 41 (16.4%) | 35 (25.0%) | 0.56 (0.33–0.94) | 0.027 | 49 (19.5%) | 22 (16.3%) | 1.26 (0.72–2.20) | 0.417 |
| rs3093110 | Dominant | AA | 206 (82.4%) | 104 (74.3%) | 1 | | 198 (78.9%) | 111 (82.2%) | 1 | |
| rs3093110 | | GG + GA | 44 (17.6%) | 36 (25.7%) | 0.58 (0.35–0.96) | 0.035 | 53 (21.1%) | 24 (17.8%) | 1.25 (0.73–2.13) | 0.425 |
| rs3093110 | Recessive | GA + AA | 247 (98.8%) | 139 (99.3%) | 1 | | 247 (98.4%) | 133 (98.5%) | 1 | |
| rs3093110 | | GG | 3 (1.2%) | 1 (0.7%) | 1.44 (0.15–14.16) | 0.755 | 4 (1.6%) | 2 (1.5%) | 1.04 (0.19–5.78) | 0.961 |
| rs3093110 | Additive | GG + GA + AA | – | – | 0.63 (0.40–1.02) | 0.590 | – | – | 1.20 (0.74–1.94) | 0.468 |
## Stratification analysis by gender
In addition, the analysis stratified by gender (Table 4) demonstrated that rs3093193 (G vs. C: OR = 0.66, $95\%$ CI: 0.47–0.92, $$p \leq 0.016$$; GG vs. CC: OR = 0.33, $95\%$ CI: 0.14–0.77, $$p \leq 0.011$$; GG vs. GC + CC: OR = 0.38, $95\%$ CI: 0.17–0.86, $$p \leq 0.021$$; additive: OR = 0.64, $95\%$ CI: 0.45–0.91, $$p \leq 0.014$$) was related to a decreased risk of LC in females. Rs3093144 in the recessive model (TT vs. TC + CC: OR = 0.20, $95\%$ CI: 0.04–0.96, $$p \leq 0.045$$) and rs3093110 in the allele, genotype, dominant, and additive models (G vs. A: OR = 0.54, $95\%$ CI: 0.33–0.87, $$p \leq 0.010$$; GA vs. AA: OR = 0.50, $95\%$ CI: 0.29–0.87, $$p \leq 0.014$$; GG + GA vs. AA: OR = 0.49, $95\%$ CI: 0.29–0.84, $$p \leq 0.010$$; additive: OR = 0.54, $95\%$ CI: 0.33–0.87, $$p \leq 0.011$$) showed a protective effect on LC in females. However, the CYP4F2 rs12459936 was associated with an increased risk of LC in females under the allele, genotype, dominant, and additive models (T vs. C: OR = 1.64, $95\%$ CI: 1.19–2.27, $$p \leq 0.002$$; TT vs.CC: OR = 2.57, $95\%$ CI: 1.49–4.39, $$p \leq 0.006$$; TC vs.CC: OR = 2.56, $95\%$ CI: 1.49–4.39, $$p \leq 0.001$$; TT + TC vs.CC: OR = 2.56, $95\%$ CI: 1.53–4.28, $p \leq 0.001$; additive: OR = 1.67, $95\%$ CI: 1.20–2.33, $$p \leq 0.002$$).
**Table 4**
| SNP ID | Models | Genotypes | Males | Males.1 | Males.2 | Males.3 | Females | Females.1 | Females.2 | Females.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SNP ID | Models | Genotypes | Cases (%) | Controls (%) | OR (95% CI) | p-value | Cases (%) | Controls (%) | OR (95% CI) | p-value |
| rs3093193 | Allele | C | 500 (70.8%) | 515 (72.9%) | 1 | | 222 (72.1%) | 190 (62.9%) | 1 | |
| rs3093193 | | G | 206 (29.2%) | 191 (27.1%) | 1.11 (0.88–1.40) | 0.375 | 86 (27.9%) | 112 (37.1%) | 0.66 (0.47–0.92) | 0.016 |
| rs3093193 | Genotype | CC | 179 (50.7%) | 183 (51.8%) | 1 | | 77 (50.0%) | 60 (39.7%) | 1 | |
| rs3093193 | | GG | 32 (9.1%) | 21 (5.9%) | 1.59 (0.88–2.87) | 0.124 | 9 (5.8%) | 21 (13.9%) | 0.33 (0.14–0.77) | 0.011 |
| rs3093193 | | GC | 142 (40.2%) | 149 (42.2%) | 0.98 (0.72–1.34) | 0.911 | 68 (44.2%) | 70 (46.4%) | 0.75 (0.47–1.21) | 0.243 |
| rs3093193 | Dominant | CC | 179 (50.7%) | 183 (51.8%) | 1 | | 77 (50.0%) | 60 (39.7%) | 1 | |
| rs3093193 | | GG + GC | 174 (49.3%) | 170 (48.2%) | 1.06 (0.79–1.42) | 0.717 | 77 (50.0%) | 91 (60.3%) | 0.66 (0.42–1.04) | 0.071 |
| rs3093193 | Recessive | GC + CC | 321 (90.9%) | 332 (94.1%) | 1 | | 145 (94.2%) | 130 (86.1%) | 1 | |
| rs3093193 | | GG | 32 (9.1%) | 21 (5.9%) | 1.60 (0.90–2.84) | 0.107 | 9 (5.8%) | 21 (13.9%) | 0.38 (0.17–0.86) | 0.021 |
| rs3093193 | Additive | GG + GC + CC | – | – | 1.12 (0.89–1.42) | 0.333 | – | – | 0.64 (0.45–0.91) | 0.014 |
| rs12459936 | Allele | C | 395 (55.9%) | 372 (52.5%) | 1 | | 150 (48.7%) | 184 (60.9%) | 1 | |
| rs12459936 | | T | 311 (44.1%) | 336 (47.5%) | 0.87 (0.71–1.08) | 0.199 | 158 (51.3%) | 118 (39.1%) | 1.64 (1.19–2.27) | 0.002 |
| rs12459936 | Genotype | CC | 116 (32.9%) | 95 (26.8%) | 1 | | 32 (20.8%) | 60 (39.7%) | 1 | |
| rs12459936 | | TT | 74 (21.0%) | 77 (21.8%) | 0.79 (0.52–1.00) | 0.269 | 36 (23.4%) | 27 (17.9%) | 2.57 (1.32–5.00) | 0.006 |
| rs12459936 | | TC | 163 (46.2%) | 182 (51.4%) | 0.74 (0.52–1.05) | 0.088 | 86 (55.8%) | 64 (42.4%) | 2.56 (1.49–4.39) | 0.001 |
| rs12459936 | Dominant | CC | 116 (32.9%) | 95 (26.8%) | 1 | | 32 (20.8%) | 60 (39.7%) | 1 | |
| rs12459936 | | TT + TC | 237 (67.1%) | 259 (73.2%) | 0.76 (0.55–1.05) | 0.090 | 122 (79.2%) | 91 (60.3%) | 2.56 (1.53–4.28) | <0.001 |
| rs12459936 | Recessive | TC + CC | 279 (79.0%) | 277 (78.2%) | 1 | | 118 (76.6%) | 130 (82.1%) | 1 | |
| rs12459936 | | TT | 74 (21.0%) | 77 (21.8%) | 0.95 (0.66–1.36) | 0.784 | 36 (23.4%) | 27 (17.9%) | 1.41 (0.80–2.47) | 0.233 |
| rs12459936 | Additive | TT + TC + CC | – | – | 0.88 (0.71–1.08) | 0.212 | – | – | 1.67 (1.20–2.33) | 0.002 |
| rs3093144 | Allele | C | 571 (80.9%) | 595 (84%) | 1 | | 253 (82.1%) | 241 (79.8%) | 1 | |
| rs3093144 | | T | 135 (19.1%) | 113 (16%) | 1.25 (0.95–1.64) | 0.118 | 55 (17.9%) | 61 (20.2%) | 0.86 (0.57–1.29) | 0.461 |
| rs3093144 | Genotype | CC | 232 (65.7%) | 248 (70.1%) | 1 | | 101 (65.6%) | 99 (65.6%) | 1 | |
| rs3093144 | | TT | 14 (4.0%) | 7 (2.0%) | 2.15 (0.85–5.43) | 0.105 | 2 (1.3%) | 9 (6.0%) | 0.21 (0.04–1.02) | 0.053 |
| rs3093144 | | TC | 107 (30.3%) | 99 (28.0%) | 1.16 (0.84–1.61) | 0.370 | 51 (33.1%) | 43 (28.5%) | 1.16 (0.71–1.90) | 0.549 |
| rs3093144 | Dominant | CC | 232 (65.7%) | 248 (70.1%) | 1 | | 101 (65.6%) | 99 (65.6%) | 1 | |
| rs3093144 | | TT + TC | 121 (34.3%) | 106 (29.9%) | 1.23 (0.89–1.68) | 0.206 | 53 (34.4%) | 52 (34.4%) | 1.00 (0.62–1.60) | 0.997 |
| rs3093144 | Recessive | TC + CC | 339 (96.0%) | 347 (98.0%) | 1 | | 152 (98.7%) | 142 (94.0%) | 1 | |
| rs3093144 | | TT | 14 (4.0%) | 7 (2.0%) | 2.06 (0.82–5.16) | 0.125 | 2 (1.3%) | 9 (6.0%) | 0.20 (0.04–0.96) | 0.045 |
| rs3093144 | Additive | TT + TC + CC | – | – | 1.25 (0.95–1.65) | 0.110 | – | – | 0.86 (0.57–1.29) | 0.460 |
| rs3093110 | Allele | A | 631 (89.4%) | 626 (88.9%) | 1 | | 277 (89.9%) | 250 (82.8%) | 1 | |
| rs3093110 | | G | 75 (10.6%) | 78 (11.1%) | 0.95 (0.68–1.33) | 0.783 | 31 (10.1%) | 52 (17.2%) | 0.54 (0.33–0.87) | 0.010 |
| rs3093110 | Genotype | AA | 283 (80.2%) | 277 (78.7%) | 1 | | 125 (81.2%) | 103 (68.2%) | 1 | |
| rs3093110 | | GG | 5 (1.4%) | 3 (0.9%) | 1.70 (0.40–7.19) | 0.474 | 2 (1.3%) | 4 (2.6%) | 0.42 (0.07–2.32) | 0.318 |
| rs3093110 | | GA | 65 (18.4%) | 72 (20.5%) | 0.89 (0.61–1.30) | 0.549 | 27 (17.5%) | 44 (29.1%) | 0.50 (0.29–0.87) | 0.014 |
| rs3093110 | Dominant | AA | 283 (80.2%) | 277 (78.7%) | 1 | | 125 (81.2%) | 103 (68.2%) | 1 | |
| rs3093110 | | GG + GA | 70 (19.8%) | 75 (21.3%) | 0.92 (0.64–1.33) | 0.668 | 29 (18.8%) | 48 (31.8%) | 0.49 (0.29–0.84) | 0.010 |
| rs3093110 | Recessive | GA + AA | 348 (98.6%) | 349 (99.1%) | 1 | | 152 (98.7%) | 147 (97.4%) | 1 | |
| rs3093110 | | GG | 5 (1.4%) | 3 (0.9%) | 1.74 (0.41–7.35) | 0.454 | 2 (1.3%) | 4 (2.6%) | 0.48 (0.09–2.68) | 0.405 |
| rs3093110 | Additive | GG + GA + AA | – | – | 0.96 (0.69–1.35) | 0.832 | – | – | 0.54 (0.33–0.87) | 0.011 |
## Haplotype analysis
Finally, the results of haplotype analysis indicated a strong 18-kb LD block among the five SNPs (rs3093203, rs3093193, rs12459936, rs3093144, and rs3093110) (Figure 1 and Table S2). Compared with haplotype “GCTCA,” haplotypes “GGCTA” (OR = 0.63, $95\%$ CI: 0.40–1.00, $$p \leq 0.048$$) and “GGCCG” (OR = 0.46, $95\%$ CI: 0.27–0.78, $$p \leq 0.004$$) were associated with a decreased risk of LC in females (Table 5). For non-smokers, the haplotype “GGCCG” (OR = 0.54, $95\%$ CI: 0.32–0.90, $$p \leq 0.046$$) was also associated with decreased susceptibility to LC (Table 5).
**Figure 1:** *Haplotype block map for SNPs in the CYP4F2 gene. The numbers inside the diamonds indicate the D′ value × 100 for pairwise analyses.* TABLE_PLACEHOLDER:Table 5
## Bioinformatics analysis of CYP4F2 expression in LC
The analysis of the expression level of CYF4F2 in normal and LUSC tissues and its effect on the survival of these patients was conducted using UALCAN online analysis software based on the TCGA database, as shown in Figure 2. We observed that the expression level of CYP4F2 was significantly different between normal and LUSC tissues ($p \leq 0.001$). In addition, the expression level of CYP4F2 was higher in non-smoking LUSC patients than in normal and smoking ones ($p \leq 0.001$). The expression level was higher in males than in females ($p \leq 0.001$). Moreover, a high expression level of CYP4F2 was found to be significantly related to the poor prognosis of non-smoking LUSC patients ($$p \leq 0.033$$).
**Figure 2:** *The expression of CYP4F2 in normal lung squamous cell carcinoma tissues based on different types.*
## Discussion
In our study, the connection between five variants in CYP4F2 and LC risk in the Chinese Han population was detected. Association analyses revealed that CYP4F2 rs12459936 increased susceptibility to LC in non-smoking individuals and females. In contrast, rs3093110 showed a protective effect on LC susceptibility in non-smoking groups and females. The two SNPs (rs3093193 and rs3093144) were also associated with a decreased risk of LC in females.
The CYP4F2 gene, a member of the CYP450 superfamily, located on chromosome 19p13.12, has been shown to be expressed at higher levels in certain types of cancerous tissues, such as the thyroid, ovarian, breast, and colon [10]. Eun et al. have confirmed that low expression of CYP4F2 may contribute to the progression of hepatocellular carcinoma (HCC) and decrease survival rates due to its involvement in various metabolic pathways [15]. A similar study showed that CYP4F2 expression was higher in pancreatic ductal adenocarcinoma (PDA) patients than in normal ones and negatively correlated with age [16]. Database prediction found that CYP4F2 was highly expressed in lung cancer tissues. The expression of CYP4F2 was higher in men than women and higher in non-smokers than smokers. Additionally, Xu et al. have reported that CYP4F generates 20-HETE by catalyzing ω-hydroxylation of arachidonic acid [17]. According to previous studies, 20-HETE plays a significant role in tumor progression. Colombero et al. have demonstrated that HET0016, a selective inhibitor of 20-HETE synthesis, can reduce the proliferation of prostate cancer [18], while another study has revealed that the antagonist of 20-HETE, WIT002, is able to inhibit tumor growth in a renal cell carcinoma cell line [19]. This suggests that CYP4F2 polymorphisms may be related to susceptibility to LC by affecting the metabolism of 20-HETE, although further verification is required. Studies have also indicated a significant association between CYP4F2 polymorphisms and a variety of diseases, including ischemic stroke and various other cardiovascular and cerebrovascular diseases [12, 20].
Our study focused on the association between CYP4F2 polymorphisms and susceptibility to LC. Five sites were selected for statistical analyses: rs3093203, rs3093193, rs12459936, rs3093144, and rs3093110. However, none of these loci were found to be significantly associated with LC susceptibility under the allelic model or any of the five genetic models. The actual increase in LC risk may be underestimated due to the limited sample size. To further examine the potential influence of LC, we conducted a stratified analysis. Tobacco has long been recognized as an independent risk factor for tumorigenesis, as it contains many carcinogens, such as nitrosamines, polycyclic aromatic hydrocarbons, and volatile organic compounds [21]. However, our analysis stratified by smoking revealed that the rs12459936 and rs3093110 loci were significantly associated with increased susceptibility to LC in the non-smoking population but not in the smoking population.
In addition, gender has been found to have a notable impact on the toxicity of therapeutic treatments and the response to them in many types of cancer. The underlying cause of this difference is likely related to a complex interplay of several factors, including sex hormones, which have been shown to affect the self-renewal of tumor stem cells, the tumor microenvironment, the immune system, and metabolism [22]. It is well established that there are considerable differences in the immune system between men and women. *In* general, women have a stronger immune system than men, leading to distinct sex-based differences in both innate and adaptive immune responses. These disparities in immune systems likely play a role in cancer susceptibility between males and females [23]. In our study, analysis stratified by gender was performed, and we found that rs309319, rs12459936, and rs3093110 all had a protective role against LC in females.
Taken together, our study observed that variants in CYP4F2 were associated with LC susceptibility. However, our research had some limitations. First, the potential functional implications of CYP4F2 polymorphisms were not addressed in this study. The expression data for CYP4F2 in LC cases were sourced from the database. To properly elucidate the genetic mechanism of CYP4F2 in LC, expression analysis of CYP4F2 mRNA and annotation of the functional significance of variants are necessary. Second, the sample size was relatively small. In the following steps, we will perfect this information and expand the sample size to explore the molecular mechanism of CYP4F2 polymorphisms affecting the development of LC.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Ethics statement
Our study complied with the Declaration of Helsinki, and the protocol in our experience was approved by the Second Affiliated Hospital of Xi’an Jiaotong University. All participants have been informed and provided written informed consent for the study.
## Author contributions
HS designed this study and drafted the manuscript. YZ performed the DNA extraction and genotyping. YW revised the manuscript and performed the data analysis. PF and YL performed the samples collection and information recording. HS conceived and supervised the study. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1114218/full#supplementary-material
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|
---
title: Metabolomic analysis of the effects of a mixed culture of Saccharomyces cerevisiae
and Lactiplantibacillus plantarum on the physicochemical and quality characteristics
of apple cider vinegar
authors:
- Ya-Nan Li
- Yue Luo
- Zhen-Ming Lu
- Yan-Lin Dong
- Li-Juan Chai
- Jin-Song Shi
- Xiao-Juan Zhang
- Zheng-Hong Xu
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043408
doi: 10.3389/fnut.2023.1142517
license: CC BY 4.0
---
# Metabolomic analysis of the effects of a mixed culture of Saccharomyces cerevisiae and Lactiplantibacillus plantarum on the physicochemical and quality characteristics of apple cider vinegar
## Abstract
### Introduction
This study compared differences in physicochemical characteristics of the vinegar made by a mixed culture (MC) of *Saccharomyces cerevisiae* and *Lactiplantibacillus plantarum* and a pure culture (PC) of Saccharomyces cerevisiae.
### Methods
The fermentation process was monitored, and metabolomics analysis by Liquid Chromagraphy-Mass Spectrometry (LC-MS) was applied to the compositional differences between PC and MC vinegars, combined with quantification of organic acids, amino acids and B vitamins.
### Results
A total of 71 differential metabolites including amino acids, organic acids and carbohydrates, and six possible key metabolic pathways were identified. MC enhanced the malic acid utilization and pyruvate acid metabolism during fermentation, increasing substrate-level phosphorylation, and supplying more energy for cellular metabolism. Higher acidity at the beginning of acetic acid fermentation, resulting from lactic acid production by *Lactiplantibacillus plantarum* in MC, suppressed the cellular metabolism and growth of Acetobacter pasteurianus, but enhanced its alcohol metabolism and acetic acid production in MC. MC vinegar contained more vitamin B, total flavonoids, total organic acids, amino acids and had a higher antioxidant capacity. MC enhanced the volatile substances, particularly ethyl lactate, ethyl caprate and ethyl caproate, which contributed to a stronger fruity aroma.
### Discussion
These results indicated the mixed culture in alcoholic fermentation can effectively enhance the flavor and quality of apple cider vinegar.
## 1. Introduction
Apple cider vinegar is a popular consumer product with a long history of use, and is consumed for multiple purposes, including as a condiment, soft drink and as a natural remedy/health product. Apple cider vinegar is produced by two-stage submerged fermentation of apple juice (alcoholic fermentation by Saccharomyces cerevisiae, followed by acetic acid fermentation by Acetobacter pasteurianus).
Apple cider vinegar is rich in amino acids, organic acids, phenolic acids, and flavonoids (1–5). Some of these compounds are associated with health benefits and contribute to antioxidant capacity, antimicrobial, and immunostimulatory activity (4–7). The potential health benefits of apple cider vinegar have attracted increasing research interest. Apple cider vinegar reduces blood triglyceride levels in mice [8] and has beneficial effects on blood sugar, insulin resistance and oxidative stress in patients with diabetes and dyslipidemia [9]. The organoleptic qualities of vinegar are determined by both its volatile and non-volatile components. The dominant flavor component of apple cider vinegar is acetic acid, a volatile and pungent organic acid, resulting in high astringency and sourness; these sensory properties limit consumer acceptance for natural health purposes. Minor taste and flavor components also contribute strongly to the sensory attributes of vinegar. Non-volatile organic acids such as malic, citric, succinic and lactic acids may reduce astringency, but increase sourness. Amino acids contribute sweetness, bitterness and umami to the taste. and the bioconversion of these amino acids during vinegar fermentation affects the overall taste. The volatile compound profile of vinegar is a key determinant of its organoleptic properties. Phenylethanol, octanoic acid, ethyl acetate, butyl acetate, and isoamyl alcohol are key compounds contributing to the floral aroma of apple cider vinegar [2, 10]. The final quality and sensory properties of apple cider vinegar are determined by the chemical complexity generated by the fermentation process used. Most industrial submerged vinegar fermentation is produced by a two-stage culture liquid fermentation, which involves alcoholic fermentation, the anaerobic conversion of fermentable sugars to ethanol by yeast (usually S. cerevisiae), followed by acetic acid fermentation, the aerobic oxidation of ethanol to acetic acid (usually by A. pasteurianus or undefined starter cultures of acetic acid bacteria) [11]. Solid-state fermentation of grains, or natural liquor-state fermentation (surface fermentation) with multiple microbial strains produces greater diversity of metabolites and more varied sensory properties. For example, although the processes and raw materials of Chinese cereal vinegar and sherry vinegar are markedly different, they are both produced by multi-strain, collaborative fermentation, and lactic acid bacteria are important fermentation strains, with high abundance and activity (12–14). The relative abundance of L. plantarum was reported as 33.9 and $88.6\%$ in sherry vinegar and Zhenjiang aromatic vinegar fermentation, respectively [12, 13]. Lactiplantibacillus plantarum and S. cerevisiae are found in many natural fermentation systems, such as wine and kefir. The [GAR +] Prion Induction ability of lactic acid bacteria can establish a mutually beneficial relationship with S. cerevisiae during wine fermentation. During kefir fermentation, Lactobacilli and yeasts can promote each other’s viability through nutrient and metabolite exchange (15–19). Therefore, considering the frequently observed positive interactions between these two species, incorporation of Lactobacillus in the alcoholic fermentation stage of vinegar production has potential to enrich the flavor compound composition and organoleptic properties of vinegar.
Fermentation of Mahali cherries by yeast and Lactobacilli reduced the sourness of the fruit [20]. Addition of Lactobacilli to the alcoholic fermentation stage of citrus vinegar improved its antioxidant properties, organic acid, flavor ester and alcohol content, umami taste and sweet amino acid content, thereby improving its flavor and quality [6]. Multi-strain vinegar fermentation has clear potential to improve cider vinegar quality, but there is little information on differences from single-strain fermentation at the metabolomic level, or the associated bioconversion pathways responsible for the differences.
Previously, we comprehensively investigated the microbiome of Chinese solid-state fermented cereal vinegar, and isolated L. plantarum F from the alcoholic fermentation stage of Chinese traditional cereal vinegar, which has good fermentation capacity and alcohol tolerance in both liquid and solid-state vinegar fermentation. In this study, apple cider vinegar co-fermented by L. plantarum F and S. cerevisiae in alcoholic fermentation stage was compared with that fermented by single-strain S. cerevisiae. Dynamic fermentation parameters were collected, combined with liquid chromatography-tandem mass spectrometry (LC-MS) based metabolomics analysis and quantification of amino acids, vitamins and other physicochemical characteristics. The effects of mixed culture (MC) and pure culture (PC) on cell growth, and quality traits of apple cider vinegar were determined, to elucidate the important metabolic pathways in the two fermentation systems. The volatile compound profiles were also compared to identify differential volatiles between the two fermentation systems. This study will deepen understanding of the metabolic mechanisms of multi-strain collaborative vinegar fermentation.
## 2.1. Chemicals and materials
Apple juice was from Shandong Lvjie (China). L. plantarum F, S. cerevisiae R and A. pasteurianus X were from our lab culture collection. Standards of amino acids, organic acids, and n-pentane (C5H12) were from Sigma-Aldrich (St. Louis, MO, USA). All other chemicals and solvents were from Sinopharm Chemical Reagents (Shanghai, China). The ATP assay kit was from Jiancheng (Nanjing, China).
## 2.2. Screening strain
All strains were isolates obtained from vinegar pei during the fermentation process of traditional cereal vinegar. The detailed isolation procedure were described in Supplementary Information (Note 1).
## 2.3. Culture medium
YPD medium (yeast extract 10 g/L, glucose 20 g/L, peptone 20 g/L) was used for fermentation of S. cerevisiae R. MRS medium (peptone 10 g/L, yeast extract 5 g/L, ammonium citrate 2 g/L, glucose 20 g/L, dipotassium phosphate 2 g/L, magnesium sulfate 0.58 g/L, manganese sulfate 0.28 g/L, beef extract 10 g/L, tween-80 1 mL) was used for fermentation of L. plantarum F. LB medium (yeast extract 10 g/L, glucose 20 g/L, ethanol $2\%$) was used for fermentation of A. pasteurianus X.
## 2.4. Fermentation of cider and vinegar
Submerged fermentation was conducted by shake-flasks in this study. Apple juice was adjusted to 12°Brix. For the alcoholic fermentation stage, the apple juice was incubated for 5 days at 30°C with S. cerevisiae R (106 CFU/mL, $2\%$ v/v) and L. plantarum F (106 CFU/mL, $2\%$ v/v) for MC. For PC, the apple juice was incubated for 5 days at 30°C with S. cerevisiae R alone (106 CFU/mL, $2\%$ v/v). When an alcoholic fermentation was complete, the cells were removed by centrifugation at 3,214 g and the supernatant was inoculated with 2–$4\%$ A. pasteurianus X seed culture (OD600 0.8), then incubated on a rotary shaking incubator (180 rpm) for 5 days at 30°C. When an acetic acid fermentation was complete, the cells were removed by centrifugation at 3,214 g and the supernatant was retained. All the fermentations were carried out independently in triplicate.
## 2.5. Analysis of the pH, soluble solids, reducing sugar, ethanol, acetic acid, ATP, ADH, ALDH, and cell growth
The pH was measured by FE28-Standard pH meter (Mettler-Toledo, Greifensee, Switzerland). The soluble solids content was determined at 20°C using RA-250WE refractometer (KEM, Kyoto, Japan) and reported as Brix. Ethanol was determined by gas chromatography (GC). The reducing sugar content was measured by the 3,5-dinitrosalicylic acid (DNS) method The number of colony forming units (CFU) per mL of L. plantarum F and S. cerevisiae R was counted according to the dilution factor and the number of colonies on plates with 30–300 colonies after incubation at 37 and 30°C, respectively. Biomass of A. pasteurianus X was quantified by OD600. The enzyme activities of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) in cells and fermentation medium were determined by a colorimetric method, as described previously [2]. ATP content was determined by an ATP assay kit (colorimetric method). Results for metabolite production are expressed as grams per cell unit (g/U). One cell unit is defined as 109 cells (S. cerevisiae R and L. plantarum F) or cell suspension with an optical density of 1 at 600 nm (A. pasteurianus X).
## 2.6. Antioxidant capacity
DPPH radical scavenging capacity was determined as described previously [21, 22]. The ABTS radical scavenging capacity was determined as described previously [23, 24], with minor modifications. The ferric reducing antioxidant power (FRAP) was determined as described previously [25, 26], with minor modifications.
## 2.7. Total flavones, total phenolics, and water-soluble vitamin B
The total flavonoid content was determined as described previously [24, 27]. The total phenolic content of the samples was determined using Folin-Ciocalteau reagent, with gallic acid as standard, as described previously [24, 28]. Water-soluble vitamin B was determined as described previously [25]. The conditions were as follows: column, Alltima C18 (250 mm × 4.6 mm, 5 μ4); mobile phase, 50 mmolL–1 ammonium dihydrogen phosphate solution (phosphoric acid adjusted to pH 3.0)-acetonitrile (95:5); flow rate, 0.5 mLmin–1; detection wavelength, 275 nm (detection of vitamin B1, vitamin B6, nicotinamide B3) and 210 nm (detection of pantothenic acid B5); column temperature, room temperature; injection volume, 20 μm.
## 2.8. Organic acid and free amino acid concentrations
Organic acids were determined by HPLC (Shimadzu, Tokyo, Japan) as described previously [29, 30]. The organic acid was determined using 0.01 mol/L KH2PO4-H3PO4 (pH 2.7) and methanol as the mobile phase, and isocratic at a ratio of 97:3 mode for elution. The flow rate was 0.7 mL/min, and the UV–*Vis spectrum* detection was set to 210 nm. Standard solutions of different concentrations were prepared of oxalic acid, tartaric acid, malic acid, lactic acid, acetic acid, oxaloacetate, isocitrate, cis-aconitate, citric acid, and succinic acid and mixed for HPLC analysis. Each compound was quantified by comparing the peak area to the standard curve generated for that compound.
The free amino acid composition was determined with an Agilent 1,100 amino acid analyzer (Agilent Technologies, Santa Clara, CA, USA), as described previously [6, 31], with minor modifications. The free amino acid was determined using An Agilent Zorbax SB-C18 column (4.6 mm ID × 60 mm; Agilent Technologies, Palo Alto, CA, USA) was used, and the flow rate was 0.7 mL/min. The column temperature was 45°C; detector: Ex 340 nm, Em 450 nm; mobile phase A: 20 mmol sodium acetate solution; and the flow rate was 0.50 mL/min. Meanwhile, mobile phase B involved 20 mmol sodium acetate-methanol-acetonitrile (1:2:2, (v/v/v), and the flow rate was 0.4 mL/min). Each amino acid was quantified using the calibration curve of the corresponding authentic standard.
## 2.9. GC-MS analysis of volatile compounds by solid phase micro-extraction (SPME)
Volatile compounds were determined by GC-MS as described previously [6, 25] with minor modifications. NaCl (1 g) was added to a 15 mL glass vial, followed by the sample (6 mL) and 2-methyl-2-butanol (10 μL, 8 g/L, Sigma-Aldrich) as the internal standard, then the vial was pre-equilibrated for 15 min at 45°C. The SPME fiber was inserted into the head space and maintained for 40 min at 45°C. The analyses were conducted on a GC-MS system (Trace GC-1310-ISQ LT; Thermo Finnigan, Austin, TX, USA) equipped with the DB-WAX column (30 m × 0.25 mm, 0.25 μm; Agilent). The GC oven temperature was initially 40°C, increased to 230°C at 5°C/min and held constant for 8 min. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. Quantitation of volatile compounds was based on the internal standard (2-Methyl-2-butanol). Mass spectra were generated in the electron impact (EI) mode at 70 eV. The spectrum was measured over the m/z range of 40–400, and the solvent elution delay time was 4 min.
## 2.10. Metabolomics analysis by LC-MS
All samples thawed at 4°C, and then 1 μL from each sample is transferred in a 2 mL centrifuge tube. Next, 400 μL methanol was added to each tube (−20°C) and swirled for 60 s. After centrifugation at 12,000 rpm for 10 min at 4°C, the supernatant was taken and transferred to a new 2 mL centrifuge tube for vacuum concentration and drying. The supernatant (samples to be tested) was obtained by resolution of 150 μL 2-chlorophenylalanine (4 ppm) $80\%$ methanol solution, which was filtered by 0.22 μm membrane. Each sample was taken 20 μL and mixed into QC sample, and LC-MS was used for detection.
Six biological replicates were performed for each experiment. The ESI-MSn analyses were performed on a Thermo Q Exactive Plus mass spectrometer with spray voltages of 3.5 and-2.5 kV in the positive and negative modes, respectively. Sheath gas and auxiliary gas were set at 30 and 10 arbitrary units, respectively. The capillary temperature was 325°C. The analyzer scanned over a mass range of m/z 81–1,000 full scan at a mass resolution of 70,000. Data dependent acquisition (DDA) MS/MS experiments were performed with HCD scanning. The normalized collision energy was 30 eV. Dynamic exclusion was implemented to remove some unnecessary information in MS/MS spectra. More detail can be found in previous reports (32–34).
The obtained original data was converted into mzXML format (xcms input file format) by Proteowizard software (v3.0.8789). The XCMS package R (v3.3.2) was used to filtrate peaks identification, peaks filtration and peaks alignment. The main parameters are bw = 2, ppm = 15, peakwidth = c [5, 30], mzwid = 0.015, mzdiff = 0.01, method = centWave. The data matrix including mass to charge ratio (m/z), retention time (rt) and intensity was obtained. For positive ion mode, 10,049 precursor molecules have been acquired, while for negative ion mode, 8,908 precursor molecules have been acquired, and the data will be exported to excel for subsequent analysis. In order to enable comparison of data of different orders of magnitude, batch normalization of peak area was performed on the data.
## 2.11. Sensory evaluation
Sensory evaluation of apple cider vinegar was performed as described previously [6] with minor modifications. The sensory panel consisted of 15 trained individuals (aged 22–40) from the National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, China. The sensory descriptors agreed by the team members were acidic, ethanolic, flowery, fruity and burnt aromas, and sour, umami, sweet, salty, and bitter tastes. Fifteen panelists were selected and trained in the sensory room at room temperature (23 ± 2°C). The panelists scored each attribute on a linear scale from 0 (lowest intensity) to 9 (highest intensity). Water was provided to the panelists to cleanse the palate. The training and evaluation were organized according to the International Organization for Standardization [35] and conducted in a sensory laboratory that complies with the American Society for Testing and Materials criteria. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Medical Ethics Committee of Jiangnan University.
## 2.12. Data analysis
The metabolites were identified using the HMDB1 and METLIN2 metabolomic data bases. Multivariate statistical analysis including PCA and (orthogonal) projection to PLS-DA were then performed using SIMCA-P + version 14.0 software (Umetrics, Umeå, Sweden) to distinguish the overall differences in metabolic profiles between groups. In the PLS-DA analysis, a variable importance in projection (VIP) score >1.0 was considered a differential variable, and in the t-tests, the variables with $p \leq 0.05$ were considered significant [36]. The metabolites with VIP >1 and $p \leq 0.05$ were selected as differential metabolites.
All experiments were carried out in triplicate, and each sample was analyzed in triplicate. Analysis of Variance (T test) followed by using SPSS software version 25.0 (SPSS-IBM Chicago, IL, USA). Differences were considered statistically significant at $p \leq 0.05.$
## 3.1. The effect of mixed culture on physicochemical properties of apple cider vinegar
A previous study [37] demonstrated that an appropriate addition of L. plantarum has a positive effect on alcoholic fermentation. The growth of S. cerevisiae R was similar under both the PC and MC conditions, and reached the highest cell density (1.3 × 108 CFU/mL) on the fifth day (Figure 1A). L. plantarum F grew rapidly on the first day in both PC and MC, and reached its highest cell density at day one. The presence of S. cerevisiae R had a noticeable inhibitory effect on the growth of L. plantarum F, reflected as a $30\%$ lower CFU count of L. plantarum F at day one in MC, compared with PC. However, by the end of the alcoholic fermentation, the counts of L. plantarum F were similar in both systems. There was little difference in soluble solids and total acid content between MC and PC (Figure 1C). Reducing sugar was consumed faster in MC during the first 3 days of alcoholic fermentation, but less ethanol was accumulated in MC (Figures 1B, D). These results indicated that some reducing sugar was converted to other metabolites initially. At the end of acetic acid fermentation, the alcohol content of PC and MC was 0.48 and 0.53 g/L, respectively.
**FIGURE 1:** *Variation in physicochemical parameters during alcoholic and acetic acid fermentation, and differences between pure culture (PC) and mixed culture (MC). Biomass increase with time of Saccharomyces cerevisiae R, Lactiplantibacillus plantarum F and Acetobacter pasteurianus X (A); ethanol, lactic acid and acetic acid production (B), pH and total acid (C); Brix and reducing sugar concentration (D); final production of ethanol and lactic acid after alcoholic fermentation, and acetic acid and ATP after acetic acid fermentation, per cell unit (U). One cell unit (U) is defined as 109 cells (S. cerevisiae R and L. plantarum F) or cell suspension with an optical density of 1 at 600 nm wavelength (A. pasteurianus X) (E); intracellular and extracellular ADH and ALDH activity at the end of fermentation (F). From T test, *p < 0.05; **p < 0.01; ***p < 0.001.*
By the end of acetic acid fermentation, the biomass of A. pasteurianus X after PC was 1.2-fold higher than that after MC, but the acetic acid content after MC was $21\%$ higher than that after PC. At acetic acid concentrations >$0.5\%$, the cell metabolism and growth of A. pasteurianus X are affected, because of decreased intracellular pH and metabolic disturbance by acetate, as well as other deleterious effects (38–41) in MC. Ethanol is oxidized to acetic acid by two sequential reactions catalyzed by ADH and ALDH, which are both located on the periplasmic side of the cytoplasmic membrane, and these reactions are linked to the respiratory chain [42]. In this study, the activities of intracellular and extracellular ethanol-acetaldehyde-acetic acid respiratory chain enzymes (ADH and ALDH) in MC were significantly higher than those in PC (Figure 1F), which could explain the higher acetic acid production after MC. Moreover, this process (*Ethanol is* oxidized to acetic acid by two sequential reactions) generates ATP for cell metabolism, therefore, higher ATP production, but less ATP consumption owing to poorer cell growth in MC could explain the much higher ATP content detected in MC (Figure 1E).
Overall, S. cerevisiae R had a negative impact on L. plantarum F biomass, and the co-fermentation of these two strains (MC) resulted in higher acidity at the end of the alcohol fermentation, inhibited A. pasteurianus X growth but enhanced alcohol oxidation, consistent with the higher ADH and ALDH activity and ATP content in MC.
## 3.2. The effect of MC on total polyphenols, total flavonoids, antioxidant capacity, and water-soluble B vitamins
Compared with apple juice, the total polyphenol contents of MC and PC cider increased by 16.4 and $10.7\%$, respectively (Figure 2A). The total flavone content of MC and PC cider increased by 21 and $20\%$, respectively. The total flavonoid content of MC vinegar at the end of acetic acid fermentation was 0.13 mg/mL, 1.3 times of that of PC vinegar (Figure 2B). Fermentation by L. plantarum increased the total polyphenol and total flavonoid contents of mulberry juice [27]. The increased polyphenol content after L. plantarum fermentation has been attributed to its production of hydrolytic enzymes which hydrolyze complex phytochemicals, such as catechin gallate, epicatechin gallate and baicalin wogonoside into simpler forms [27, 43], which may result in higher flavonoids detected.
**FIGURE 2:** *Comparison of mixed culture (MC) of Saccharomyces cerevisiae R and Lactiplantibacillus plantarum F and pure culture (PC) of S. cerevisiae R on: Total phenolics (A); Total flavonoids (B); FRAP (C); DPPH (D) and ABTS (E) radical scavenging capacities; water-soluble B1 vitamins (F); water-soluble B2 vitamins (G); water-soluble B5 vitamins (H); water-soluble B6 vitamins (I). Different letters in the same figure indicate statistically significant differences in the results (p < 0.05).*
The DPPH and ABTS radical scavenging capacities of cider and vinegar were slightly lower than those of apple juice (Figures 2D, E). However, the DPPH radical scavenging capacity of MC cider and vinegar increased by 3 and $16\%$, respectively, compared with PC cider and vinegar. The ABTS radical scavenging capacity of MC cider and vinegar were slightly lower than PC cider and vinegar ($p \leq 0.05$). Compared with PC, the FRAP of MC cider and vinegar increased by 26 and $19\%$, respectively (Figure 2F). *In* general, MC increased the DPPH radical scavenging capacity and FRAP of cider and vinegar, which may be related to its increased flavonoid content; antioxidant capacity is closely related to polyphenol content [25].
The concentrations of water-soluble B vitamins in cider and vinegar were higher than those in apple juice (Figure 2C). In addition, the vitamin B1 and B5 contents of MC cider were 13.6- and 1.36-fold higher than those of PC cider, respectively, and the vitamin B1, B2 and B5 contents of MC vinegar were 9. 56-, 2. 09-, and 1.1-fold higher than those of PC vinegar (Figures 2G–I), respectively. The pyrimidine and thiazole ring moieties composing vitamin B1 (thiamin) are synthesized by the purine and hexose monophosphate pathways, and vitamin B2 (riboflavin) by the purine pathway, from GTP and ribulose-5-phosphate [44]. Vitamin B5 is synthesized by the pantothenate and CoA biosynthesis pathway, and is related to L-valine and L-aspartate metabolism [45]. To explain the increased vitamin biosynthesis in MC fermentation, comparative analysis of the metabolic pathways of MC and PC fermentation was undertaken.
## 3.3. Changes in organic acid and amino acid contents during fermentation of apple cider vinegar
Mixed culture with L. plantarum altered the organic acid composition of apple cider and vinegar, compared with PC (Figure 3A). Malic acid in MC decreased significantly during the alcoholic fermentation, because both S. cerevisiae and L. plantarum can metabolize malic acid as a carbon source, and L. plantarum can convert malic acid into lactic acid via malolactic fermentation. Succinic acid is an important metabolite of yeast; after alcoholic fermentation, the succinic acid concentration of MC (0.17 g/L) was 7.3-fold that of PC (0.023 g/L); after acetic acid fermentation, the succinic acid concentration of MC (0.19 g/L) was 8.3 times that of PC (0.023 g/L) (Figure 3D). Lower malic and higher succinic acid concentrations were the main differences between apple cider vinegars fermented by MC, compared with PC. The acetic acid concentration of MC was 1.3-fold that of PC after acetic acid fermentation. Lactic acid, a side product of L. plantarum F glycolysis, would have been used by A. pasteurianus X to produce acetic acid and acetoin. The other tested organic acid contents were similar between PC and MC.
**FIGURE 3:** *Comparison of organic acid and free amino acid content in mixed culture (MC) of Saccharomyces cerevisiae R and Lactiplantibacillus plantarum F and pure culture (PC) of S. cerevisiae R: Relative content (A) and absolute content (D) of organic acids; Relative content (B) and absolute content (C) of free amino acids in apple cider vinegar (relative concentration value in A and B was normalized in each column by zero-mean normalization).*
Free amino acids are considered to be an important contributor to the distinctive taste of vinegar [46]. After both cider and vinegar fermentations, the total free amino acid content decreased significantly compared with that in apple juice (Figure 3B). Aspartate, the major amino acid in apple juice, decreased by 97, 96, 97, and $77\%$ in MC cider, PC cider, MC vinegar, and PC vinegar, respectively. The total free amino acids in MC cider and vinegar were 2.6- and 2.3-fold higher, respectively, than for PC cider and vinegar. Free amino acids are classified into groups with similar taste, i.e., umami, sweet, bitter, and tasteless [47, 48]. Except for Asp and Pro, the free amino acid content in MC vinegar was significantly higher than that in PC vinegar ($p \leq 0.05$). The total umami amino acids (Asp and Glu) in MC cider and vinegar were 6.02- and 1.65-fold higher than those in PC cider and vinegar, respectively [47, 48]. The total sweet amino acids (Ala, Glu, Thr, Ser, and Pro) in MC cider and vinegar were 1.88- and 2.67-fold higher than those of PC cider and vinegar, respectively (Figure 3C; 47, 48). Thr, Met, Ile, Leu, Phe, Lys, and Val are essential amino acids, and were more abundant in MC than PC. Overall, MC increased the total free amino acid content in vinegar and enhanced its taste by increasing the content of umami and sweet tasting amino acids.
## 3.4. Multivariate statistical analysis of apple cider vinegar metabolites
Base peak intensity (BPI) chromatograms in positive and negative ionization modes for PC, MC, and juice samples were recorded (Supplementary Figure 1); there were significant differences between the PC and MC vinegars. In total, 10,049 and 8,908 metabolite ion features were detected in the vinegar samples, in the positive and negative modes, respectively.
To investigate the impact of co-fermentation on apple cider vinegar, the metabolite dataset was subjected to three-component PCA analysis (in positive and negative ion modes, Figure 4A) and the data from PC and MC samples was well separated, with significant clustering behavior (in positive ion mode: R2X = 0.529; in negative ion mode: R2X = 0.533). Four component PCA analysis included the above three sample groups as well as QC samples (mixtures of all samples) and a hierarchical cluster analysis showed similar results (Supplementary Figures 2A, B), demonstrating that the metabolic profiles of conventional PC, and MC with L. plantarum co-fermentation were markedly different and easily distinguished. A supervised chemometric analysis by PLS-DA (in positive and negative modes) was established using the PC and MC datasets. Distinct grouping according to fermentation process was achieved (R2X = 0.627, R2Y = 0.999, Q2 = 0.988) and 273 metabolites (with VIP > 1, $p \leq 0.01$) were identified as the differential compounds that differentiated PC and MC samples.
**FIGURE 4:** *Comparison of metabolite profiles and metabolic pathways in mixed culture (MC) of Saccharomyces cerevisiae R and Lactiplantibacillus plantarum F with pure culture (PC) of S. cerevisiae R: PCA score plots of positive and negative ionization LC-MS data, red = J (apple juice), blue = PC and orange = MC (A); classification and relative content of differential marker metabolites (B); relative content of marker metabolites (C); KEGG enrichment of differential metabolic pathways (D).*
The relative contents of amino acids, alkaloids, amines and other metabolites in MC were higher than those in PC (Figure 4B). Based on the fold change of relative concentration (fold change ≥ 5 or ≤ 0.25; fold change = MC/PC), 71 important differential compounds were identified as candidate markers characteristic of MC and PC samples (Figure 4C; 49). Most of the markers were organic acids, or amino acids and the less abundant compound classes included alkaloids, amino acid derivatives, carbohydrates, carbohydrates derivatives and amines (Supplementary Table 1). The relative abundance of marker compounds in each sample is shown in a heatmap (Figure 5).
**FIGURE 5:** *Comparison of differential metabolites and metabolic pathways in mixed culture (MC) of Saccharomyces cerevisiae R and Lactiplantibacillus plantarum F and pure culture of S. cerevisiae R (PC). The fold change of relative content of each metabolite compared with control (apple juice) was visualized as a heat map and combined with the possible metabolic pathways. The absolute quantity of several metabolites is visualized with charts and located in the metabolic pathways. Different letters in the same figure indicate statistically significant differences in the results (p < 0.05).*
The relative content of amino acid, amine and alkaloid metabolites identified in MC vinegar were 1. 25-, 2. 00-, and 1.59-fold higher than those in PC vinegar, respectively. In the comparison between PC and MC apple vinegar, the relative contents of the metabolites L-arginine, acetylcholine, hypoxanthine, 6-hydroxyhexanoic acid, dyphylline, indoleacetic acid, indolelactic acid, 4-guanidinobutanoic acid, melibiose, phenyllactic acid and alanine in MC vinegar were markedly higher than those in PC, with a fold change >56.6. However, the relative contents of malic and citric acids were much lower in MC vinegar, consistent with the quantitative analysis (Section “3.3. Changes in organic acid and amino acid contents during fermentation of apple cider vinegar”).
Enrichment of differential metabolic pathways between MC and PC is shown in Figure 4D. The main metabolic pathways enriched were arginine and proline metabolism, citrate cycle (TCA cycle), lysine biosynthesis, cysteine and methionine metabolism, aspartate and glutamate metabolism and pyruvate metabolism.
## 3.5. Differential key metabolic pathways
The KEGG predictions indicated that pathways related to aspartate, pyruvate, glutamate, lysine and organic acids were significantly influenced by the fermentation process (PC and MC) (Figure 4D). The differential metabolic pathways were closely related to amino acid and organic acid metabolism, indicating that the TCA cycle, glycolysis and amino acid metabolism were active pathways during vinegar production. To elucidate the specific changes metabolites related to these metabolic pathways, relative quantification of metabolites via LC-MS based metabolomics, combined with absolute quantification of amino acids, organic acids, vitamin B1, B2, and B5 metabolites by HPLC was performed (Figure 5). The TCA cycle links with various catabolic and anabolic biochemical pathways, regulating the electron transport chain and finally generating ATP [50, 51]. Malic acid, the major organic acid, decreased by $61\%$ and $93\%$ in the PC and MC vinegars, respectively, whereas citric acid increased about 2.2-fold in the PC and MC vinegars compared with apple juice. Comparing the PC and MC vinegars, many TCA cycle intermediates varied markedly. Specifically, oxaloacetic, succinic and aconitic acids were much more abundant in MC, whereas isocitric and malic acids were more abundant in PC (Figure 5). These variations suggested that in the co-fermentation with L. plantarum F, which has an incomplete TCA cycle, the utilization of the abundant malic acid as carbon source was enhanced. Regarding pyruvate metabolism, significantly more lactic acid, acetyl-CoA and acetic acid accumulated during MC, and correspondingly, their common precursor, pyruvate was markedly depleted during MC. The TCA cycle are important energy-yielding pathways, which generate ATP by substrate-level phosphorylation. Comparing with single strain fermentation, co-fermentation enhanced malic acid utilization and pyruvate acid metabolism were consistent with the higher ATP content detected in MC system (Figure 1E), and higher energy is beneficial for various cellar processes, and may explain the higher alcohol and acetic acid production [50, 52]. The relative content of pantothenate and pantothenol in MC was higher than those in PC, and can be generated from valine or aspartate. In nicotinate and nicotinamide metabolism, NAD + is an important coenzyme for production of nicotinic acid [53], Greater accumulation of NAD + and nicotinamide in MC might explain the higher nicotinic acid production in PC. Besides, NAD + is the required cofactor for thiamin synthesis, might associated with the higher abundance of thiamin [44].
In summary, these differences in metabolic pathways between MC and PC showed that flux of malic acid metabolism in the TCA cycle and pyruvate metabolism was enhanced and energy production was higher in MC. A higher relative content of NAD + was detected in MC, which may be associated with the greater accumulation of thiamine and nicotinic acid. It appears that apple cider vinegar produced by MC may have enhanced health benefits, for example, nicotinic acid reduces progression of atherosclerosis, as well as clinical events and mortality from coronary heart disease [54].
## 3.6. Comparative analysis of volatile flavor compounds
Volatile compounds, including alcohols, acids, esters, ketones, phenols, aldehydes and alkenes, in cider and vinegar made by PC and MC were compared. PC and MC cider yielded 35 and 41 volatile compounds, respectively, PC and MC vinegar yielded 31 and 37 volatile compounds, respectively (Supplementary Table 2). After the alcohol fermentation, MC cider had more variety and higher content of volatile alcohols than PC cider (Figure 6A). Specifically, the contents of phenylethanol, benzyl alcohol, geraniol and n-hexanol were relatively much higher in MC cider. Geraniol and phenylethanol have rosy aromas, and n-hexanol and benzyl alcohol contribute to a fruity aroma. Volatile acids were more abundant in MC cider compared with PC cider, and octanoic acid and decanoic acid were the two major acids, which may contribute to a richer aroma and longer-lasting tastes for MC cider. The volatile ester concentration of MC vinegar was $20\%$ higher, which is consistent with a previous report that the addition of L. plantarum F to the fermentation culture increased the production of esters in cherry wines [55]. Ethyl acetate was the most abundant ester with a low odor threshold; the relative content of ethyl acetate in MC vinegar was slightly higher ($3.85\%$) than that of PC vinegar ($p \leq 0.05$). Ethyl phenyl acetate and isoamyl acetate in MC vinegar were 5.65- and 3.47-fold more abundant than in PC vinegar ($p \leq 0.05$); they have a sweet and a banana-like aroma, respectively. In addition, some esters were found in MC vinegar but not in PC vinegar, such as ethyl lactate, ethyl caprate, methyl salicylate and ethyl caproate. Ethyl lactate, ethyl caprate and ethyl caproate contribute rum-like, creamy and fruity aromas. Alcohols were less abundant in MC vinegar; phenylethanol, β-citronellol and n-propanol were significantly less abundant in MC vinegar compared with PC. A PCA score plot (Figure 6B) clearly distinguished PC and MC vinegar. Overall, the mixed fermentation with L. plantarum F markedly modified the aroma profile of apple cider vinegar by lowering its volatile alcohol content and increasing its ester content, thereby enhancing its fruity aroma.
**FIGURE 6:** *The comparative content of volatile compounds in juice, cider and vinegar prepared by PC and MC fermentation. The heatmap data was normalized within each column by zero-mean normalization (A). Principal components analysis score plot of vinegar samples based on the relative abundance of volatile compounds (B). The differential volatile compounds between vinegar of MC and PC systems with VIP greater than 1 are labeled with * in panel (A).*
## 3.7. Sensory evaluation
The sensory analysis of MC and PC vinegars is shown in Figures 7A, B (aroma) (taste). The taste of vinegar is generally considered to be dominated by sourness, followed by sweet and umami tastes, with slight salty and bitter tastes, which appears to result from interactions among the different taste components [46]. MC had a significantly higher fruity aroma intensity than PC ($p \leq 0.05$), probably resulting from the high content of ethyl phenylacetate, isoamyl acetate and ethyl acetate in MC, whereas PC had a significantly higher flowery aroma intensity ($p \leq 0.05$). MC had significantly higher umami and sweet taste intensities than PC ($p \leq 0.05$), which is consistent with the significantly higher sweet and umami amino acid content of MC vinegar ($p \leq 0.05$). Although the total acidity of MC was higher than that of PC, MC had a significantly lower sour taste intensity ($p \leq 0.05$). Higher abundance of sweet and umami amino acids, and flavor esters could have masked the sour taste and sharp acidic odor of acetic acid (Figure 4D). Overall, the PC apple cider vinegar had a stronger flowery aroma, whereas the MC vinegar had a stronger fruity aroma and stronger sweet and umami tastes.
**FIGURE 7:** *Sensory evaluation of aroma (A) and taste (B) in mixed culture vinegar and pure culture vinegar (* represents p < 0.05).*
## 4. Conclusion
This study demonstrated that alcoholic fermentation of apple juice using a mixed culture of S. cerevisiae R and L. plantarum F can significantly improve the flavor and quality of the final vinegar. LC-MS based metabolomics analysis was applied to compositional variations between PC and MC vinegars, and combined with quantification of organic acids, amino acids and B vitamins. A total of 71 differential metabolites including amino acids, organic acids and carbohydrates were identified, and six possible key metabolic pathways which could account for the differential metabolites were elucidated. MC appeared to enhance the malic acid utilization and pyruvate acid metabolism, thereby increasing substrate level phosphorylation, and yielding more energy for cellular metabolism. Higher acidity resulting from lactic acid accumulation at the beginning of the acetic acid fermentation after MC fermentation resulted in greater suppression of cellular metabolism and cell growth of A. pasteurianus, but enhanced its ethanol-acetaldehyde-acetic acid respiratory chain, which stimulated ATP accumulation, and higher acetic acid production. MC vinegar had higher contents of B vitamins, pantothenate, total flavonoids, total organic acids, amino acids and higher antioxidant capacity. MC fermentation enhanced the volatile content of the resulting apple cider vinegar, particularly ethyl lactate, ethyl caprate and ethyl caproate, which contribute to a fruity aroma. This is consistent with the sensory evaluation, i.e., MC apple cider vinegar had a higher fruity aroma intensity, as well as stronger umami and sweet tastes. These findings improve understanding of the metabolic mechanism of multi-strain collaborative vinegar fermentation processes. Future studies should focus on using mixed strains to rationally modify the flavor and increase the functional substance content of apple cider vinegar.
## Data availability statement
The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
Y-NL: conceptualization, data curation, methodology, and writing–original draft. X-JZ and Z-HX: conceptualization, funding acquisition, supervision, and writing–review and editing. YL: investigation. Z-ML: data curation and methodology. L-JC: resources. Y-LD: validation. J-SS: supervision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Y-LD was employed by Lvjie Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1142517/full#supplementary-material
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|
---
title: 'Changes in the gut microbiota composition of healthy young volunteers after
administration of Lacticaseibacillus rhamnosus LRa05: A placebo-controlled study'
authors:
- Zhonghui Gai
- Yao Dong
- Fei Xu
- Junli Zhang
- Yujiao Yang
- Yuwen Wang
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043436
doi: 10.3389/fnut.2023.1105694
license: CC BY 4.0
---
# Changes in the gut microbiota composition of healthy young volunteers after administration of Lacticaseibacillus rhamnosus LRa05: A placebo-controlled study
## Abstract
The gut microbiota promotes gastrointestinal health in humans; however, the effect of probiotics on the gut microbiota of healthy adults has not been documented clearly. This placebo-controlled study was conducted to assess the effect of *Lacticaseibacillus rhamnosus* LRa05 supplementation on the gut microbiota of healthy adults. The subjects ($$n = 100$$) were randomized 1:1 to receive [1] maltodextrin (placebo, CTL group) and [2] maltodextrin + strain LRa05 (1 × 1010 colony-forming units/day, LRa05 group). The duration of the intervention was 4 weeks, and changes in the gut microbiota from before to after the intervention were investigated using 16S rRNA high-throughput sequencing. In terms of alpha diversity, no significant difference in the composition of the gut microbiota was found between the LRa05 and CTL groups. 16S rRNA sequencing analysis showed that the relative abundance of Lacticaseibacillus significantly increased after supplementation with LRa05. Furthermore, a decreasing trend in the abundance of Sellimonas and a significant decrease in the salmonella infection pathway were observed in the LRa05 group compared with the CTL group. These findings indicate the potential of LRa05 to colonize the human gut and reduce the abundance of harmful bacteria in the microbiota.
## Introduction
Functionally, microbes in the human gut contribute to various aspects of health by regulating the immune system, fermenting dietary fiber, inhibiting pathogen colonization, and synthesizing vitamins [1]. A disturbance of the gut microbiota is associated with the incidence and development of many diseases [2]. Probiotic supplementation is a common approach used to alter the gut microbiota and thus improve health. Probiotics are living microorganisms that confer health benefits when consumed in sufficient numbers [3]. The mechanisms by which probiotics support the intestinal environment and host health include improving intestinal barrier function through their effects on the epithelial and mucus layers of the gut and producing antimicrobial substances [4].
Probiotics can improve the clinical symptoms of patients with gastrointestinal diseases and regulate the gut microbiota [5, 6]. However, few studies have investigated the effects of probiotics on the gut microbiota of healthy people, and these studies have yielded inconsistent conclusions. For example, probiotics have been shown to modulate the gut microbiota and reduce the relative abundance of harmful bacteria in healthy subjects (7–10). However, several clinical studies have shown that probiotic interventions do not cause significant changes in the fecal microbial composition (11–15). Individual differences in susceptibility to probiotics and cross-study differences in probiotic dosage and intervention duration can influence the observed effects of probiotics [16]. Therefore, the health benefits of probiotic supplementation should be demonstrated in light of the impact on relevant host phenotypes, especially in healthy participants.
The therapeutic effects of *Lacticaseibacillus rhamnosus* strain LRa05 (hereafter, LRa05) have been described in mouse models of obesity and type 2 diabetes [17, 18]. In animal experiments, we demonstrated the safety of LRa05 and its efficacy in regulating the gut microbiota [17]. Animal experiments have also shown that LRa05 has gut-localized immunomodulatory effects [18]. However, LRa05 has not been reported to have been tested in humans. Accordingly, we focused on assessing the effect of LRa05 on the gut microbiota of healthy adult humans. In this study, healthy young adults were administered 1 × 1010 colony-forming units (CFU) of LRa05 daily for 28 days. Each subject’s gut microbiota composition was analyzed before and after probiotic consumption and subjected to assessments of tolerability, safety, and intestinal colonization to determine the effect of LRa05 supplementation on the gut microbiota.
## Population recruitment and ethical statement
The study subjects were healthy volunteers who met the following inclusion criteria: an age between 19 and 45 years, a body mass index (BMI) between 18.5 and 25 kg/m2, voluntary acceptance of and adherence to the experimental protocol, and ability to participate in a timely review and follow-up. The exclusion criterion was any autoimmune or other chronic disease. The study was conducted at Henan University of Technology from April 21 to 31 June 2022, and the flow of the trial is shown in Figure 1. Of the 110 initial volunteers, 100 met the above-listed criteria and were included in the study. To exclude the influence of volunteers’ use of other probiotic products on the trial, the probiotic products of all kinds were stopped by communicating with volunteers 2 weeks before enrollment. Prior to formal enrollment, we conducted further inquiry, confirmed that all volunteers meet the conditions before enrollment. The study followed the guidelines of the Declaration of Helsinki, and all procedures involving the human body were approved by the Ethics Committee of Henan University of Technology (No. HautEC202277). Written informed consent was obtained from the study subjects after they had received a detailed explanation of the nature of the study.
**FIGURE 1:** *Flowchart of the trial involving healthy volunteers.*
## Experimental design
Following a 14 day washout period, the subjects were divided into placebo (CTL) and LRa05 groups. The CTL group received a 2-g supplement of maltodextrin per day, and the LRa05 group received 1.9 g maltodextrin plus 0.1 g LRa05 bacterial powder (1 × 1010 CFU) per day. The placebo and probiotic products used in this study were obtained from Wecare Probiotics Co., Ltd. The experimental period was 4 weeks, and the volunteers did not consume products containing probiotics during this period and 2-week washout period. No additional dietary restrictions were imposed, and the subjects maintained their original lifestyle habits during the experimental period.
This was a single-blind placebo-controlled trial, meaning that the subjects were unaware of their assigned group. The trial design and process of the study are shown in Figure 2. The study consisted of two visits. During the first visit (T0), each subject’s height, weight, and blood pressure were measured. In the end (T1), each subject’s body fat percentage was measured. We used a questionnaire to evaluate the effect of the placebo or probiotic intervention on the subjects’ defecation. As a questionnaire, Bristol Stool Form Scale (BSFS) is the most widely used evaluation of stool consistency [19], in which types 1 and 2 are considered hard stools (associated with symptoms of constipation); types 3, 4, and 5 are generally considered normal stool forms; and types 6 and 7 are considered abnormally loose or liquid stools (associated with symptoms of diarrhea). To eliminate individual differences, a longitudinal study approach was applied, using samples collected on Day 0 as the control. Fecal samples were collected at T0 and T1 and used to assess the subjects’ gut microbiota (Figure 2).
**FIGURE 2:** *Experimental design of the study. One hundred subjects were enrolled, and 94 subjects completed the trial. We collected fecal samples at baseline (T0) and after the 4-week intervention (T1) and administered questionnaires.*
## Fecal samples, DNA extraction, and sequencing
The subjects were asked to self-collect stool samples, freeze them immediately at −20°C, and bring them to the visit site for storage at −80°C until analysis. DNA was isolated from the samples for testing using the QIAamp DNA kit according to the manufacturer’s instructions. Polymerase chain reaction was used to amplify the V3–V4 variable region of the prokaryotic 16S rRNA gene as previously described [20] using the primers F1 and R2 (5′- CCTACGGGNGGCWGCAG-3′ and 5′-GACTACHVGGGTATCTAATCC-3′). Sequencing was performed on the Miseq platform (Illumina, San Diego, CA, USA) using a 2 × 300 bp paired-end protocol. In preparation for sequencing experiments, a sample that has been sequenced was prepared as a positive control to complete DNA extraction and PCR simultaneously with the sample to be tested.
## Bioinformatic analysis
Methods for bioinformatics analysis of amplicons were adopted from our previous publications [20, 21]. Trimmomatic was used to filter low quality sequences [22]. Denoising was performed using the UNOISE algorithm, reads were clustered into amplicon sequence variants (ASVs), and classification assignments and construction of ASV tables were performed by using USEARCH software. The sequencing results were analyzed using USEARCH software (version 11.0667),1 which produced amplicon sequence variants (ASVs). The 16S rRNA database from the RDP reference training set (version 18) was used as the reference database for sequence annotation.2 Community diversity (Shannon and Simpson indices) and richness [Chao1 and abundance-based coverage estimators (ACE)] in the gut microbiota were analyzed at the level of ASVs using the vegan 2.5-7 package [23] on the R platform. The PICRUSt v2.5.0 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) pipeline [24] was applied to the 16S rRNA sequencing data, and the imputed relative abundances of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in each sample were used to predict alterations in fecal microbiome function using the picrust2_pipeline.py command.
## Statistical analysis
Continuous variable that followed a normal distribution were analyzed using the t-test. Continuous variable that did not follow a normal distribution were analyzed using the non-parametric test to identify differences between the two groups. Kruskal–Wallis test followed by dunn.test function (in the dunn.test package) were used for multiple comparison testing. Linear discriminant analysis combined with effect size (LEfSe) algorithm measurements was used to identify biomarkers unique to each group based on the abundance values [25]. The cutoff of LDA score in the LEfSe analysis is 2.0. Principal coordinate analysis (PCoA) of data on the gut microbiota was conducted according to the Bray–Curtis distance, and significant differences between the groups were determined using the adonis2 function of vegan 2.5-7. PICRUSt data were analyzed using Statistical Analysis of Metagenomic Profiles (STAMP, version 2.1.3) [26]. The Mantel test [23] (mantel function in vegan 2.5-7) was used to test the correlation between the gut microbiota at T0 and T1 according to the Bray–Curtis distance, and the reported mantel r and p-values are based on 999 permutations. All graphs were generated using the ggplot2 package in R [27]. Statistical analyses were performed using R version 4.2, and p-values < 0.05 were considered statistically significant.
## Nucleotide sequence accession numbers
The sequence data used in this article have been deposited in the NCBI database (ac-cession number, SRA: PRJNA899929).
## Baseline characteristics of the subjects
One hundred subjects were included in this study, and four and two subjects in the CTL and LRa05 groups, respectively, dropped out due to restrictions pertaining to COVID-19 (Figure 1). Table 1 illustrates that there were no significant differences in age, sex, blood pressure, and BMI between the two groups at baseline. The metadata of all volunteers were provided in Supplementary Table 1.
**TABLE 1**
| Unnamed: 0 | CTL_0 N = 46 | LRa05_0 N = 48 | P-value |
| --- | --- | --- | --- |
| Age (Year) | 23.0 ± 2.4 | 22.6 ± 1.6 | 0.838 |
| Gender (Female) | 31 (67.4%) | 31 (64.6%) | 1.0 |
| Systolic blood pressure (mmHg) | 111.5 ± 12.2 | 111.6 ± 10.5 | 1.0 |
| Diastolic blood pressure (mmHg) | 75.4 ± 9.7 | 76.8 ± 6.7 | 0.814 |
| Body mass index | 22.6 ± 3.8 | 24.1 ± 4.4 | 0.305 |
## Changes in physical indicators from before to after the intervention
As shown in Table 2, no significant changes in BMI, body fat percentage, and BSFS results were observed in either the CTL or the LRa05 group. This outcome demonstrates the safety of LRa05 supplementation.
**TABLE 2**
| Unnamed: 0 | CTL_0 N = 46 | CTL_1 N = 46 | LRa05_0 N = 48 | LRa05_1 N = 48 | P-value |
| --- | --- | --- | --- | --- | --- |
| Body mass index | 22.6 ± 3.8 | 22.5 ± 3.6 | 24.1 ± 4.4 | 24.0 ± 4.3 | 0.103 |
| Body fat | 28.5 ± 7.5 | 27.9 ± 7.7 | 31.6 ± 7.6 | 30.1 ± 7.4 | 0.114 |
| Gastrointestinal tract score# | 0 (0.0%) | 0 (0.0%) | 2 (4.2%) | 0 (0.0%) | 0.246 |
## Effects of the intervention on gut microbiota diversity
The species accumulation curves (Figure 3A) show successive increases in species in each group, indicating that saturation had been reached in species accumulation, and additional samples could not provide new records. In addition, the Venn diagram in Figure 3A indicates 1,155 species that were common between the CTL and LRa05 groups, accounting for $93.8\%$ of the 1,232 found species. This result suggests that both the placebo and LRa05 intervention resulted in detectable changes in species diversity in the subjects’ gut microbiota. Further diversity analysis (Figure 3B) revealed that both the placebo and LRa05 intervention led to increased species richness (Chao1 and ACE) in the gut microbiota but did not cause significant changes in community diversity (Shannon and Simpson indices). These findings are consistent with the findings from our species accumulation analysis and Venn diagram.
**FIGURE 3:** *Changes in alpha and beta diversity in the gut microbiota of subjects from before to after the intervention. (A) Species accumulation curve. The Venn diagram shows the distribution of species across the CTL and LRa05 groups. (B) Changes in alpha diversity from before to after the intervention. (C) Changes in beta diversity from before to after the intervention. Principal component analysis based on the Bray–Curtis distance at different consumption stages and in different groups. Each point represents the gut microbiota composition of one subject. (D) Comparison of the significance of beta diversity between the groups.*
Although alpha diversity analysis did not reveal significant changes in the gut microbiota with placebo and LRa05 intervention, beta diversity analysis revealed (Figure 3C) that both the placebo and LRa05 intervention had a significant effect on the gut microbiota composition. No significant differences in the gut microbiota were observed between the CTL and LRa05 groups at T0 and T1 (Figure 3D, $$p \leq 0.317$$ and $$p \leq 0.857$$). This result suggests that changes in the gut microbiota after intervention were mainly caused by maltodextrin. The effect of probiotic supplementation on the gut microbiota of healthy individuals was not significantly different from the effect of the placebo.
At the phylum level, Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were the four most predominant bacterial phyla in the subjects’ gut microbiota (Figure 4A). Our analysis of the gut microbial data at T0 and T1 showed no significant phylum-level difference in the gut microbiota between the CTL and LRa05 groups at either time point (Figure 4B), except for Proteobacteria. However, both the placebo and LRa05 intervention resulted in significant changes in the gut microbiota composition at T1 relative to T0; these changes mainly involved significant reductions in the relative abundances of Firmicutes and Actinobacteria and a significant increase in the relative abundance of Bacteroidetes.
**FIGURE 4:** *Changes in the gut microbiota composition in the CTL and LRa05 groups from before to after the intervention. (A) Distribution of gut microbiota abundance at the phylum level across different groups. (B) Changes in abundance at the phylum level from before (T0) to after the intervention (T1). (C) Results of LEfSe analysis at the genus level before (T0) and after the intervention (T1) in the CTL group. The cutoff of LDA score in the LEfSe analysis is 2.0. (D) Results of LEfSe analysis at the genus level before (T0) and after the intervention (T1) in the LRa05 group. (E) Results of LEfSe analysis at the genus level for the CTL and LRa05 groups after the intervention (T1). (F) Results of LEfSe analysis at the genus level for the CTL and LRa05 groups at baseline (T0).*
## Genus-level changes in the gut microbiota and functional analysis
We further investigated changes in the gut microbiota at the genus level using LEfSe analysis. Compared with T0, placebo supplementation resulted in significant increases in the relative abundances of Prevotella, Phocaeicola, Bacteroides, and Parabacteroides and significant decreases in the relative abundances of Escherichia/Shigella, Gemmiger, and Bifidobacterium at T1 (Figure 4C). LRa05 intervention led to changes in the gut microbiota similar to those observed with the placebo, including significant increases in the relative abundances of Phocaeicola, Bacteroides, and Parabacteroides and significant decreases in the relative abundances of Escherichia/Shigella, Gemmiger, and Bifidobacterium at T1 relative to T0 (Figure 4D). Further investigation of the differences between the LRa05 and CTL groups revealed that probiotic intervention led to significant increases in the relative abundances of Weissella, Lacticaseibacillus, Enterococcus, and Mitsuokella and significant decreases in the relative abundances of Dialister, Negativibacillus, and Anaeromassilibacillus at T1 (Figure 4E). However, these differences were not observed at T0 (Figure 4F). These results indicate that although the observed changes in the gut microbiota were mainly caused by maltodextrin, the addition of LRa05 led to changes in the relative abundances of some specific microorganisms, such as an increase in Lacticaseibacillus. As LRa05 belongs to the genus Lacticaseibacillus, this result implies that LRa05 colonization of the gut occurred. Gut microbiota composition information at the family and genus level was also analyzed (Supplementary Figures 1, 2).
PICRUST analysis showed that the placebo and LRa05 intervention caused similar changes in the function of the microbiota, consistent with the LEfSe analysis results. Compared with T0, both the placebo and LRa05 intervention resulted in the increased abundance of pathways such as chaperones and folding catalysts, alanine, lysosome, and other glycan degradation, as well as significantly decreased abundance of pathways such as ABC transporters, transporters, secretion system, and bacterial motility proteins, at T1 (Figures 5A, B). Only small differences in gut microbiota function were observed between the CTL and LRa05 groups at T0 (Figure 5C), as indicated by smaller differences in mean proportion. At the end of the trial, the abundances of the *Salmonella infection* and NOD-like receptor signaling pathway were reduced and the abundance of the nitrogen metabolism pathway was increased in the LRa05 group compared with the CTL group (Figure 5D). The reduced abundance of the *Salmonella infection* pathway implies the inhibitory potential of LRa05 against harmful bacteria. Therefore, we further compared the abundance of the genus Sellimonas at T1 between the CTL and LRa05 groups (Figure 5E). We found that the relative abundance of Sellimonas tended to decrease in the probiotic group compared with the placebo group, but this difference was not significant ($$p \leq 0.6872$$).
**FIGURE 5:** *Predictions of functional profiles into KEGG level 3 using PICRUSt and STAMP. The two-sided Welch’s t-test was used to compare KEGG functions between (A) CTL at T0 (CTL_0) and T1 (CTL_1), (B) LRa05 at T0 (LRa05_0) and T1 (LRa05_1), (C) CTL_0 and LRa05_0, and (D) CLT_1 and LRa05_1 to identify significant differences (corrected p < 0.05). The bar plot (E) compares the relative abundance of Sellimonas between CLT_1 and LRa05_1.*
## Correlation analysis of the gut microbiota before and after intervention
We also assessed the association between the gut microbiota before and after the intervention in both groups using Mantel correlation analysis. As shown in Figure 6, there was a significant correlation ($$p \leq 0.001$$) between the gut microbiota at T0 and T1 in all subjects (Figure 5A), the CTL group (Figure 5B), and the LRa05 group (Figure 5C) after the intervention. However, the correlation coefficient was higher in the CTL group ($r = 0.417$) than in the LRa05 group ($r = 0.182$), indicating the effect of LRa05 on the gut microbiota.
**FIGURE 6:** *Results of Mantel tests to investigate correlations between the Bray–Curtis distance matrices of the gut microbiota at T0 and T1. (A) All subjects. (B) Placebo group. (C) LRa05 group.*
## Discussion
LRa05 is an isolate found in infant feces. In vitro and in vivo studies have demonstrated several properties of LRa05, including acid and bile tolerance, antagonism against enteropathogenic E. coli, and immunomodulatory, cholesterol-lowering, and antioxidant effects in vivo [17, 18]. Therefore, LRa05 is considered a probiotic with potential health benefits. In this study, we analyzed the effect of dietary supplementation with LRa05 on the stability and composition of the gut microbiota of healthy adults using 16S rRNA high-throughput sequencing. We did not observe significant differences in alpha diversity of the gut microbiota between the LRa05 and CTL groups, indicating that this probiotic intervention did not alter the overall stability of the gut microbiota. PCoA visualization further supported the lack of systematic differences in the gut microbiota between the study groups. Our conclusions are consistent with those of a study on L. rhamnosus LGG, wherein probiotic intervention did not alter the overall gut microbiota composition in healthy people [28]. Additionally, our results showed that although LRa05 supplementation did not significantly affect microbiota stability, it specifically increased the abundance of Lacticaseibacillus species, which implies colonization by LRa05 and may also reflect excretion of the ingested strain.
Some studies have indicated that probiotics are conducive to the regulation of the gut microbiota and amelioration of disease (29–31). For individuals with dysbiosis or disruption of the gut microbiota, ingestion of probiotics may modulate the microbiota and reduce gastrointestinal symptoms [32, 33]. However, the effects of probiotics are less easily assessed in healthy people than in people with disease due to the lack of an internationally accepted consensus on normal or healthy fecal microbial communities. Studies on the positive effects of probiotics on gut microbiota regulation in healthy individuals have yielded varied results. Clinical studies have shown an increase in the abundance of lactic acid bacteria in healthy adults after the ingestion of Lactobacillus strains [7, 9, 34]. However, McNulty et al. [ 35] showed that consumption of yogurt containing five probiotics did not alter the gut microbiota composition in young adults. Several other clinical studies have revealed that probiotic intervention did not cause significant changes in the fecal microbiota composition in terms of alpha and beta diversity compared with a placebo. In addition, several clinical studies have shown that probiotic intervention did not cause significant changes in fecal microbiota composition in terms of alpha diversity and beta diversity compared with placebo (11–15, 28). In a clinical study, probiotic intervention did not significantly affect the alpha diversity of the gut microbiota but had a significant effect on beta diversity [36]. Our study involved healthy adult volunteers, namely, young college students whose microbial communities appeared to be well established and balanced.
Despite the lack of consensus regarding a healthy gut microbial composition, the evidence from phylum-level gut microbial gene analyses supports the prioritization of Firmicutes and Bacteroidetes in healthy individuals [37]. Our results are consistent with this prioritization. Additionally, the variance in gut microbiota composition among healthy individuals indicates that the microbiota of each person contains a specific and variable number of bacterial species in addition to the predominant species (38–40). Our results show that maltodextrin significantly modulated the gut microbiota of our healthy subjects, whereas probiotics did not significantly alter the gut microbiota. From an ecological point of view, one bacterial strain would be unlikely to cause fundamental changes to established intestinal communities [41]. PICRUSt analysis showed significant changes in gut microbiota function in both the CTL and LRa05 groups at T1 relative to T0. The functional characterization of a healthy gut microbial community remains elusive [42]. The effect of probiotics on the gut microbiota composition is only an intermediate result, and the effect on host health should be carefully considered when interpreting it. In addition, we observed the enrichment of Lacticaseibacillus in the LRa05 group compared with the CTL group and demonstrated the potential of LRa05 to inhibit Salmonella infection, as indicated by the results of our PICRUSt analysis.
In conclusion, we identified a modulatory effect of LRa05 on the gut microbiota via high-throughput sequencing analysis. Our results partially confirm and extend previous observations from animal studies and inform hypotheses to support subsequent studies in humans. However, this study has some limitations. First, the analysis is based on a Chinese cohort of young adults within a small age range. Second, our analysis included only two time points and did not consider dynamic changes in the gut microbiota. Third, although probiotics have been reported to enhance immunity, we did not test other blood parameters. Despite these limitations, the changes that we observed in the fecal microbiota composition of healthy adults after LRa05 intervention could provide insight into the mechanisms underlying the effects of probiotics and the fecal microbiota. Furthermore, the use of blood parameters or metabolic profiling and clinical studies of disease cohorts to confirm our findings is necessary to assess the regulatory effects of probiotics on the gut microbiota and the associated effects on human health.
## Conclusion
In summary, both the placebo and LRa05 intervention led to significant changes in the gut microbiota of healthy adults relative to baseline. However, there were no significant differences in alpha and beta diversity between the CTL and LRa05 groups, indicating that the observed changes in the gut microbiota were mainly caused by maltodextrin supplementation. Additionally, a significant increase in the abundance of Lacticaseibacillus was found in the LRa05 group compared with the CTL group, implying the potential of LRa05 to colonize the human gut. Overall, our findings suggest the tolerance and colonization potential of LRa05 and, in particular, its potential ability to modulate the gut microbiota and reduce the abundance of harmful bacteria.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in this article/Supplementary material.
## Ethics statement
The study followed the guidelines of the Declaration of Helsinki, and all procedures involving the human body were approved by the Ethics Committee of Henan University of Technology (No. HautEC202277). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YY and YW: conceptualization, data curation, formal analysis, funding acquisition, software, and writing–original draft. ZG and YD: conceptualization, investigation, methodology, writing–review and editing, and funding acquisition. FX: project administration, resources, supervision, formal analysis, software, validation, and visualization. JZ: data curation, formal analysis, and software. All authors contributed to the article and approved the submitted version.
## Conflict of interest
ZG, YD, and JZ were employed by the Wecare-Bio Probiotics (Suzhou) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1105694/full#supplementary-material
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|
---
title: Impact of lenvatinib-induced proteinuria and renal dysfunction in patients
with thyroid cancer
authors:
- Yuma Shibutani
- Shinya Suzuki
- Atsunobu Sagara
- Tomohiro Enokida
- Susumu Okano
- Takao Fujisawa
- Fumiaki Sato
- Tetsuro Yumoto
- Motohiko Sano
- Toshikatsu Kawasaki
- Makoto Tahara
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10043443
doi: 10.3389/fonc.2023.1154771
license: CC BY 4.0
---
# Impact of lenvatinib-induced proteinuria and renal dysfunction in patients with thyroid cancer
## Abstract
### Background
Proteinuria is the most frequent adverse event of lenvatinib use. However, the association between lenvatinib-induced proteinuria and renal dysfunction remains unclear.
### Methods
We retrospectively reviewed medical records of patients with thyroid cancer without proteinuria treated with lenvatinib as a first-line systemic therapy at the initiation of treatment to assess the association between lenvatinib-induced proteinuria and renal function and the risk factors for the development of ≥3+ proteinuria on a dipstick test. Proteinuria was assessed by the dipstick test throughout the treatment in all cases.
### Results
Of the 76 patients, 39 developed ≤2+ proteinuria (low proteinuria group) and 37 developed ≥3+ proteinuria (high proteinuria group). There was no significant difference in estimated glomerular filtration rate (eGFR) between high and low proteinuria groups at each time point, but there was a trend toward a significant decrease in eGFR of -9.3 ml/min/1.73 m2 in all patients after 2 years of treatment. The percentage of change in eGFR (ΔeGFR) significantly decreased in the high proteinuria group compared to that in the low proteinuria group (ΔeGFR: -$6.8\%$ vs. -$17.2\%$, $$p \leq 0.04$$). However, there was no significant difference in development of severe renal dysfunction with eGFR <30 ml/min/1.73 m2 between the two groups. Moreover, no patients permanently discontinued treatment because of renal dysfunction in both groups. Furthermore, renal function after completion of lenvatinib was reversible.
### Conclusions
There was no association between the degree of lenvatinib-induced proteinuria and renal function. Therefore, treatment should be continued with attention to renal function, regardless of the degree of proteinuria.
## Introduction
Differentiated thyroid cancer (DTC) is the most common histologic type of thyroid cancer and surgery is the first choice of treatment, with postoperative radioiodine (RAI) therapy if necessary (1–3).. Lenvatinib is a multi-kinase inhibitor that blocks a variety of receptors, including vascular endothelial growth factor receptor (VEGFR) 1-3 (4–6). In the SELECT study, a global Phase III trial in RAI-refractory DTC, lenvatinib significantly improved progression-free survival to 18.3 months, compared to 3.6 months with a placebo [7]. As a result, lenvatinib is recommended for use in patients with RAI-refractory DTC [8].
Proteinuria associated with anti-cancer drug use is mainly caused by damage to the glomerulus of the kidney by targeting VEGF [9, 10]; for instance, bevacizumab, an anti-VEGF drug causes the event in a dose-dependent manner in $41\%$–$63\%$ of patients [11, 12]. Lenvatinib-induced proteinuria is one of the most frequent adverse events, represented by $31\%$ for all grades and $10\%$ for grade ≥3 in the SELECT trial, and notably, occurs more frequently in Japanese patients than in others [7, 13].. However, protocols and strategies for monitoring and managing proteinuria induced by lenvatinib remain unestablished. According to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v4.0, which were used to evaluate adverse effects in the SELECT study, a proteinuria ≤2+ or less is defined as grade 2 proteinuria, but if a proteinuria ≤3+ develops, a 24-hour urine sample should be performed when proteinuria ≥3+ develops; a urine sample with >3.5 g/gCre is defined as a grade ≥3 proteinuria. Because this 24-hour urine sample test relies on the patient collecting overnight urine samples, which is difficult to test during outpatient therapy, lenvatinib was often withdrawn when proteinuria ≥3+ developed [14]. Thus, the details of renal dysfunction with continued lenvatinib after the onset of proteinuria ≥3+ are unclear. Since lenvatinib was continued regardless of the degree of proteinuria in the absence of a rapid decrease in either eGFR or edema, and to not decrease the intensity of treatment at our hospital, we investigated the effect of the degree of proteinuria on renal function in this study.
## Participants
Seventy-six patients with thyroid cancer without proteinuria treated with lenvatinib as a first-line systemic therapy at the initiation of treatment at the National Cancer Center Hospital East from October 2011 to June 2021 were retrospectively reviewed. The data cutoff date was 31st October 2021, which is the period covered by the study. The management of proteinuria, including interruption or dose reduction of lenvatinib, was performed by the attending physician.
## Study design and methods
This was a single-center, retrospective study conducted using medical records. The status of the development of proteinuria, change in estimated glomerular filtration rate (eGFR), and effect of the degree of proteinuria on the continuation of lenvatinib treatment were evaluated. To estimate the effect of lenvatinib-induced proteinuria on renal function, we compared two groups: the low proteinuria group, in which the participant developed ≤2+ proteinuria on dipstick, and the high proteinuria group with ≥3+ proteinuria on dipstick. The eGFR formula used was specific to the Japanese population [15]. In all cases, proteinuria was assessed by a dipstick test throughout the treatment [16]. Hypertension was defined as patients who had been diagnosed with hypertension prior to starting lenvatinib and were taking antihypertensive treatment. Patients were included if they had been diagnosed with diabetes mellitus prior to starting lenvatinib. Performance status (PS) was assessed with the Eastern Cooperative Oncology Group scale. The time point at which the laboratory test was performed was defined as follows: “pre-treatment” for the most recent laboratory value before lenvatinib treatment, “end of treatment” for the latest value during the treatment period, and “post treatment” for the latest value of observation period after end of treatment. Thus, the percentage change in eGFR (ΔeGFR) was calculated using the pre-treatment and post treatment values. The ΔeGFR was calculated using the formula used in the report by Iwasaki et al. [ 17].
## Statistical analysis
For comparisons between high and low proteinuria groups, the univariate chi-square, Fisher’s exact, Mann–Whitney U, and Wilcoxon signed-rank tests were used. In addition, logistic regression analysis was used to identify risk factors for proteinuria ≥3+. Four factors were selected for logistic regression using multivariate analysis via the backward selection method. Statistical analyses were performed using SPSS ver.28.0 (IBM, Armonk, NY, USA), and $p \leq 0.05$ was considered statistically significant.
## Ethical considerations
This study was approved according to its compliance with the Ethical Guidelines for Life Sciences and Medical Research Involving Human Subjects and was subjected to the ethical review procedures of the National Cancer Center. Compliance with the relevant guidelines was also ensured while performing research involving the participants during the original studies (Research Project No. 2022-115).
## Development of proteinuria and patient characteristics
Of the 76 patients, 39 patients developed ≤2+ proteinuria on the dipstick test during lenvatinib treatment and were categorized as the low proteinuria group, and 37 patients developed ≥3+ proteinuria on the dipstick test and were categorized as the high proteinuria group. The patients in the high proteinuria group had a significantly higher median age than those in the low proteinuria group ($$p \leq 0.004$$), of which a significantly higher percentage was aged ≥65 years ($$p \leq 0.01$$) (Table 1). In addition, the high proteinuria group was more significantly associated with a history of hypertension than the low proteinuria group ($$p \leq 0.04$$). No significant difference in other items, including eGFR for pre-treatment, history of diabetes, use of antihypertensive medication, and lenvatinib treatment duration, were observed between the two groups.
**Table 1**
| Unnamed: 0 | Low proteinuria group N=39 | High proteinuria group N=37 | p value |
| --- | --- | --- | --- |
| Sex (male), % | 17 (44) | 12 (33) | 0.31a |
| Age, median (range) | 65 (36–79) | 70 (36–84) | 0.004b |
| Age (≥65 years), % | 20 (51) | 29 (78) | 0.01a |
| Body weight (kg), median (range) | 54.3 (37.8–98.7) | 54.7 (34.7–98.7) | 0.80d |
| Treatment period (day), median (range) | 447 (48–3280) | 554 (46–3678) | 0.27c |
| PS (≤1), % | 38 (97) | 36 (97) | 0.74d |
| eGFR (ml/min/1.73m²) at pre-treatment, median (range) | 73.7 (46.9–126.3) | 65.4 (42.2–96.3) | 0.12b |
| eGFR (<60 ml/min/1.73m²) at pre-treatment, % | 9 (23) | 13 (35) | 0.24a |
| Hypertension, % | 12 (30) | 20 (54) | 0.04a |
| CCB*, % | 33 (84) | 35 (94) | 0.14d |
| ACEI/ARB*, % | 31 (79) | 32 (86) | 0.41a |
| Diabetes, % | 6 (15) | 9 (24) | 0.32a |
## Temporal changes in eGFR and ΔeGFR
The median eGFR at each point in all 76 patients was calculated (Figure 1). eGFR at each time point decreased significantly at 2 to 6 years of treatment when compared to pre-treatment eGFR. The median decreased eGFR changes (and ΔeGFR) were -9.3 (-12.4), -16.7 (-27.9), and -30.5 ml/min/1.73 m2 (-$32.2\%$) at 2, 4, and 6 years, respectively. However, the trends in ΔeGFR in the high and low proteinuria groups were not significantly different between the two groups at each point (Figure 2).
**Figure 1:** *Temporal changes of eGFR. *p < 0.05, Time points showing a significant difference compared to pre-treatment eGFR. Analysis Method: Wilcoxon signed-rank test. Summary: eGFR at each time point compared to pre-treatment eGFR decreased significantly at 2 to 6 years of treatment. The median decreased eGFR change (and ΔeGFR) was -9.3 (-12.4), -16.7 (-27.9) and -30.5 ml/min/1.73 m2 (-32.2%) at 2, 4 and 6 years, respectively. Abbreviations: eGFR: estimated glomerular filtration rate, ΔeGFR: percentage of change in eGFR.* **Figure 2:** *Temporal changes in ΔeGFR for each group. Analysis Method: Mann–Whitney U test. eGFR, estimated glomerular filtration rate; ΔeGFR, percentage of change in eGFR.*
## Changes in eGFR by time point in each group
There was no significant decrease in eGFR from pre-treatment to the end of treatment in the low proteinuria group ($$p \leq 0.15$$) (Figure 3). However, the high proteinuria group showed a significant decrease in eGFR from pre-treatment to the end of treatment ($$p \leq 0.002$$). There was no significant difference in eGFR at pre-treatment between the two groups ($$p \leq 0.12$$). However, the high proteinuria group demonstrated a significant decrease of eGFR compared to the low proteinuria group ($$p \leq 0.004$$). Furthermore, the high proteinuria group demonstrated a significant decrease in ΔeGFR from pre-treatment to the end of treatment compared with the low proteinuria group (-$17.2\%$ vs. -$6.8\%$, $$p \leq 0.04$$).
**Figure 3:** *Changes in eGFR at pre-treatment to the end of treatment for each group. Analysis Method: *Wilcoxon signed-rank test, †Mann–Whitney U test. eGFR, estimated glomerular filtration rate; ΔeGFR, percentage of change in eGFR.*
In both the low and high proteinuria groups, patients who were followed up after the end of treatment were identified, and renal function changes at pre-treatment, end of treatment, and post treatment are shown (Figure 4). There was no significant decrease in eGFR from pre-treatment to the end of treatment in the low proteinuria group ($$p \leq 0.77$$). There was a significant increase in eGFR from the end of treatment to post treatment and from pre-treatment to post treatment in the low proteinuria group ($$p \leq 0.02$$). In the high proteinuria group, there was a significant decrease in eGFR from pre-treatment to the end of treatment ($$p \leq 0.01$$). However, eGFR was significantly increased from the end of treatment to post treatment ($$p \leq 0.01$$). There was no significant difference in eGFR from pre-treatment to post treatment in the high proteinuria group ($$p \leq 0.61$$). There was no significant difference in eGFR at pre-treatment between the low proteinuria and high proteinuria groups ($$p \leq 0.20$$). However, eGFR was significantly lower for the high proteinuria group than for the low proteinuria group at the end of treatment and post treatment (end of treatment: $$p \leq 0.01$$, post treatment: $$p \leq 0.004$$).
**Figure 4:** *Changes in eGFR at different time points in each group. Analysis Method: *Wilcoxon signed-rank test, †; Mann–Whitney U test. eGFR, estimated glomerular filtration rate.*
## Degree of proteinuria and its effect on renal function
Regarding renal dysfunction corresponding to the degree of proteinuria, an eGFR measurement of 30-15 ml/min/1.73m2 was observed in 3 patients within the high proteinuria group, but there was no significant difference between the two groups ($$p \leq 0.11$$) (Table 2). In addition, an eGFR measurement <15 ml/min/1.73 m2 occurred in 1 patient within the high proteinuria group, but there was no significant difference between the two groups ($$p \leq 0.48$$). No patients required discontinuation of treatment or dialysis as a result of worsening renal function. Regarding the incidence of proteinuria, although the maximum proteinuria value during the treatment period tended to increase with longer treatment periods, there was no correlation (Figure 5).
**Figure 5:** *Scatter plots of maximum proteinuria values for treatment period. Scatter plots of maximum proteinuria values for treatment period. The horizontal axis represents the treatment period (month), and the vertical axis represents the maximum proteinuria value (from 0 to +4). Abbreviations: $95\%$ CI: $95\%$ confidence interval.* TABLE_PLACEHOLDER:Table 2
## Decrease in eGFR and risk factors
The median ΔeGFR from baseline to the latest value of the treatment period for all patients was -$7.6\%$. Therefore, a multivariate analysis of risk factors for patients with ΔeGFR >$7.6\%$ decrease revealed that diabetes (odds ratio [OR]; 5.28, $95\%$ confidence interval [CI]; 1.27-21.85, $$p \leq 0.02$$) and proteinuria ≥3+ (OR: 2.95, $95\%$CI; 1.06-8.17, $$p \leq 0.03$$) were significantly related (Table 3). Thus, the change in eGFR in patients with a history of diabetes at the start of treatment showed that such patients had a lower eGFR after 4 years of lenvatinib treatment compared to both the overall population and the group without a history of diabetes (Figure 6).
## Discussion
In this study, we examined the impact of the development of proteinuria ≥3+ on renal function during lenvatinib treatment. There was a significant decrease in eGFR in all patients from 2 years after the start of treatment, but it was clear that eGFR improved after the end of treatment. Furthermore, in the latest values of eGFR during treatment, the development of severe renal dysfunction or renal failure corresponding to a chronic kidney disease stage ≥G4 or higher was not significantly different between the two groups, and there was no permanent discontinuation of treatment due to renal dysfunction in both groups [18]. Proteinuria induced by VEGF inhibitors, such as bevacizumab, increases in a dose-dependent manner, but is not associated with any renal dysfunction, such as decreased renal clearance and azotemia [19, 20]. However, lenvatinib use causes proteinuria more frequently than use of other VEGF inhibitors because of the stronger VEGF inhibitory action of lenvatinib; furthermore, renal dysfunction occurred in $4.2\%$ of patients in the SELECT study [7, 21]. Patients with thyroid cancer treated with lenvatinib long-term have a significant decrease in eGFR after 2 years of treatment, which was also seen in this study [22]. However, the change in ΔeGFR showed no significant difference between the high proteinuria and low proteinuria group at each time point, indicating that eGFR decreased regardless of the degree of proteinuria. In contrast, patients who ended treatment in both the high and low proteinuria groups showed a significant improvement in eGFR after the end of treatment, indicating that the decrease in eGFR during treatment was not due to irreversible renal dysfunction. In summary, although lenvatinib produces proteinuria more frequently than other VEGF inhibitors, eGFR declines after 2 years of treatment, regardless of the degree of proteinuria, and this decline in eGFR with lenvatinib is reversible. This suggests that lenvatinib-induced proteinuria may be only one of the phenotypes of renal dysfunction caused by VEGF inhibitors, and it is important to be aware of changes in renal function, regardless of the degree of proteinuria.
*In* general, risk factors for impaired renal function include age >65 years, diabetes mellitus, and hypertension [23]. In addition, hypertension is a frequent side effect of lenvatinib use; and calcium channel blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers are often used to treat this side effect. However, these antihypertensive drugs can cause renal dysfunction (24–27). In this study, the multivariate analysis revealed that patients with a history of diabetes mellitus and patients who developed proteinuria ≥3+ were significantly associated with the risk of decrease in ΔeGFR. Unlike for other malignancies, the available treatment options for thyroid cancer are limited. Therefore, patients who respond to lenvatinib are more likely to use it long-term, and background factors such as medical history and age at the time of treatment initiation should also be considered. Diabetes mellitus can cause diabetic nephropathy, which manifests as a decrease in renal function over a long period [28], and increased proteinuria, which is strongly associated, along with decreased eGFR, with the risk of future end stage kidney disease in patients with diabetes mellitus [28, 29]. In this study, renal failure was observed in one patient with a history of diabetes who developed proteinuria ≥3+. In addition, patients in the study with a history of diabetes showed a decrease in eGFR after 4 years of treatment compared to both the overall population and the population with no history of diabetes. These results suggest that eGFR and proteinuria should be carefully monitored in patients with a history of diabetes at the time of lenvatinib initiation. In addition, factors that were not identified in the multivariate analysis are reportedly associated with decreased renal function, suggesting that it is important to continue treatment with attention to the decrease in eGFR.
Two important limitations to this study must be considered. The first is that eGFR is a Japanese-specific value, which is affected by muscle mass. Therefore, eGFR may be overestimated in patients with reduced muscle mass, such as those with sarcopenia. Second, the protocol did not provide for temporary interruption or dose reduction of lenvatinib administration as a result of worsening proteinuria or renal function. Therefore, interruption or dose reduction was done at the discretion of the physician, and judgments based on the physician’s experience and knowledge may have affected renal function. However, there was no significant difference in ΔeGFR trends over time depending on the degree of proteinuria or in the development of severe renal dysfunction in the two groups. Thus, the results suggest that renal function should be monitored, and treatment continued cautiously regardless of the degree of urinary protein, but larger study are needed to confirm these findings.
## Conclusion
Regardless of the degree of proteinuria, eGFR decreased after 2 years of lenvatinib treatment; however, the incidence of end stage renal dysfunction or renal failure was not significantly different between the two groups. In addition, patients with a history of diabetes mellitus should continue treatment with careful attention because it is a risk factor for a decrease in eGFR. Therefore, it is recommended to continue lenvatinib cautiously until more prospective data are published, paying attention to patient background and renal function.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Shinya Suzuki, National Cancer Center Hospital East. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YS, SS, and AS designed this concept, performed the statistical analyses, and wrote the manuscript. TE, SO, TF, FS, TY, MS, TK, and MT interpreted and discussed the data. All authors contributed to the article and approved the submitted version.
## Conflict of interest
TE: Received personal fees from MSD, Bayer, Merck Biopharma, unrelated to the submitted work. MT: Received grants and personal fees from Eisai during the conduct of the study; grants and personal fees from Ono Pharmaceutical, MSD, Bayer, BMS, Pfizer, Rakuten Medical, Novartis, Lilly, GSK, and Boehringer Ingelheim and personal fees from Merck Biopharma, LOXO, Celgene, and Amgen, unrelated to the submitted work.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Lifestyle factors in Black female breast cancer survivors—Descriptive results
from an online pilot study
authors:
- Avonne E. Connor
- Kate E. Dibble
- Kala Visvanathan
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10043444
doi: 10.3389/fpubh.2023.1072741
license: CC BY 4.0
---
# Lifestyle factors in Black female breast cancer survivors—Descriptive results from an online pilot study
## Abstract
### Background
Breast cancer (BC) research examining disparities in cancer survivorship and modifiable risk behaviors has been mostly cancer-specific, leaving relevant gaps in disparities research relating to other cancer survivorship outcomes, including cardiovascular disease (CVD). Maintaining healthy lifestyle behaviors is a critical component of successful cancer survivorship, where unhealthy behaviors may increase the risk for recurrence, second primary cancers, and incidence of new comorbid conditions, including CVD. The current study describes BC survivorship factors among an online pilot study of Black BC survivors in Maryland, with a focus on the burden of obesity, comorbidity, and behavioral factors associated with CVD risk.
### Methods
Utilizing social media recruitment strategies and survivor networks, we recruited 100 Black female BC survivors to complete an online survey. Descriptive characteristics (demographic, clinical, and lifestyle factors) were analyzed in terms of frequencies, means, standard deviations (SD) overall and by county.
### Results
The average ages at time of survey and at primary BC diagnosis were 58.6 years (SD = 10.1) and 49.1 years (SD = 10.2), respectively. More than half of the survivors reported having hypertension ($51\%$); and while only $7\%$ reported being obese at the time of BC diagnosis, $54\%$ reported being obese at the time of survey which was on average 9 years post BC diagnosis. Only $28\%$ of the survivors reported meeting weekly exercise recommendations. While $70\%$ were never smokers, most ever smokers resided in Baltimore City/Baltimore County ($$n = 18$$ ever smokers).
### Conclusion
Our pilot study identified at-risk BC survivors in Maryland due to the high prevalence of CVD risk factors (hypertension, obesity, limited exercise). These pilot study methods will inform a future statewide multilevel prospective study to improve health behaviors among Black BC survivors.
## Introduction
African American/Black female breast cancer patients have the highest breast cancer mortality rates and shortest overall survival than any racial/ethnic group of women in the US [1]. When diagnosed with breast cancer, Black women are diagnosed at younger ages, with more advanced stages, and have a higher prevalence of more aggressive breast cancer subtypes that can grow rapidly with a worse prognosis compared to non-Hispanic white women with the same tumor factors [2]. Black breast cancer patients also experience significant healthcare system-related disparities including delays in diagnostic and therapeutic care (3–5) and insufficient knowledge of cancer follow-up care [6].
The cause of these disparities in breast cancer and overall survival among Black women is multifactorial and reflects the interplay between biological factors (differences in tumor biology, advanced stage of disease, individual health status) and socioeconomic and societal disparities. The high prevalence of obesity and obesity-associated comorbid conditions can impact late stage of presentation, cancer progression, choice of treatment, and overall survival (7–11). Unequal access to opportunities and resources such as wealth, income, and education can also influence access to high-quality healthcare services, such as health education, disease prevention/screening, early detection, and treatment services [1] and can directly affect health outcomes and risk factors associated with poor prognosis [12]. Independent of socioeconomic status, minority cancer patients might also be exposed to additional stressors associated with forms of discrimination and racism which can impact health outcomes throughout life [13]. The persistent exposures of these factors together, socioeconomic adversity, racism, and discrimination, have been coined the term “weathering” which was specifically hypothesized by Geronimus to affect the health of Black women [14].
Partially due to these psychosocial stressors described, Black women have high rates of hypertension ($50\%$) compared to women of other racial/ethnic groups ($39\%$ of non-Hispanic white women and $38\%$ of Hispanic women); and furthermore, among Black women with hypertension, only $26.5\%$ have their blood pressure controlled [15, 16]. More specifically for Black breast cancer survivors, having a co-existing hypertension is very common. In a study of co-existing and newly diagnosed comorbidities among breast cancer patients in Missouri, we found that Black patients were 1.4 times more likely to have a diagnosis of hypertension ($53\%$) compared to White patients [17]. Hypertension and obesity were also found to be highly prevalent among young Black breast cancer patients with triple-negative breast cancer [18].
Co-existing comorbidities can increase risk of breast cancer mortality by 20–$50\%$ and competing-cause mortality up to six-fold [19]. While the risk of cardiovascular disease (CVD) mortality following breast cancer has been studied (20–22), few studies have examined these associations among Black women [23, 24], who have a high prevalence of CVD risk factors at time of cancer diagnosis. Among a study of 25,181 White and 10,907 Black female breast cancer patients identified from the Maryland Cancer Registry, we observed a $33\%$ increased risk of CVD mortality among Black women compared to White women, with almost 3-fold increased hazard of CVD mortality among women 50–59 years of age in age-stratified results (p-int. < 0.01) [25]. A notable limitation of our study was that we lacked data on important CVD risk factors that can be treated and modified, such as obesity, hypertension, and behavioral lifestyle factors.
As a next step to address this limitation and to acquire a better understanding of the comorbidity burden and prevalence of behavioral risk factors associated with CVD risk among Black breast cancer survivors in Maryland, we conducted a pilot study of Black breast cancer survivors living in Maryland using social media recruitment strategies. Our team piloted study methods to inform a future community-based intervention study of Black breast cancer survivors at high risk of poor outcomes due to comorbid conditions and social inequities [26]. Maryland is a significant resource to research breast cancer disparities due to its racial and socioeconomic diversity [27], particularly by county of residence as Prince George's County has one of the highest median household incomes in the US according to 2019 data ($84,920 compared to Baltimore City with a median household income of $50,379) [28]. The current pilot study describes breast cancer survivorship factors and the burden of obesity, comorbidity, and behavioral factors associated with CVD risk among a sample of Black breast cancer survivors across the state and explores differences by county to determine if potential societal differences may exist.
## Study design and population
Detailed recruitment methods of this cross-sectional study have been previously described [26]. Recruitment from social media platform click-oriented ads via Meta and Instagram began on January 5, 2022 and completed on August 18, 2022. After ~6 months of recruitment, our Facebook ad reached 118,461 individuals on Facebook and 2,650 link clicks, had 3,539 post engagements, 611 post reactions, and 181 post shares. A total of 127 women completed the online screener and consented to participate, and of those women, 100 completed the online survey. All participants had to identify as biologically female, African American/Black, have been diagnosed with breast cancer (stages in situ-IV), and reside in Maryland (identified by geolocation).
Social media accounts were collected but were utilized for ad purposes, as there was only direct contact between participant (or potential participant) and research team through Facebook study “Pages” direct messaging and via institutional email addresses. By private messaging through the “Pages” forum, research administration could view names and information (depending on participants' Meta and Instagram privacy settings) without research administration using their own social media accounts. Therefore, they were directly conversing with participants via a “Page” front. This study was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board (IRB #00018654).
Recruitment took place using an online, anonymous eligibility screening mechanism via REDCap© [29] managed by the Johns Hopkins University Information Technology department. Individuals who met inclusion criteria for the current study were then redirected to the electronic consent page to review passive consent information and by clicking the “next” button they provide consent to participate in the full online survey. Eligible participants provided their email address for compensation and recontact purposes (regarding future contact for new studies). While most participants found the study information through the Facebook advertisement, some participants were also recruited directly from their social networks (friend referrals), including local cancer survivorship support groups. Of note, some of the participants did not have Facebook accounts. Upon completion of the online survey, participants were emailed a $25 Amazon e-gift card to the email address provided.
## Online survey
The online survey was administered via REDCap© and was designed to take ~30 min to complete. Survey sections focused on several factors including: demographic characteristics (current age, age at breast cancer primary diagnosis, state of residence, county of residence, annual income, education, employment), clinical characteristics (cancer diagnoses, stage at breast cancer diagnosis, cancer treatments, recurrences, metastases), recent and former cancer screening history [mammography, magnetic resonance imaging (MRI)], genetic testing information (if they completed genetic testing), health promotion questions [weight history, smoking, alcohol consumption, diet/nutrition, physical activity (PA)], comorbid conditions, COVID-19 impact, healthcare access and utilization, and quality of physician-patient communication (treatment decisions, sufficient time, understanding, respectful, genuine, available treatment options, etc.). The online survey also collected preferences for future interventions, willingness to participate in medical record abstraction, and email addresses for compensation and future contact.
## Variables of interest
The following variables were of interest for the current analysis: demographic factors (current age, age at primary breast cancer diagnosis, county of residence, annual income, marital status, health insurance status, education, employment), clinical characteristics (cancer diagnoses, stage of breast cancer at diagnosis, cancer treatments, recurrences, second primary diagnoses, metastases), cancer screening history, ever completed genetic testing, behavioral/lifestyle factors [weight history and height for body mass index (BMI) calculation currently, BMI at breast cancer diagnosis and BMI at age 25 years, smoking status, alcohol consumption, diet/nutrition, PA], and ever diagnosed with specific comorbid conditions. County of residence was collapsed into the following groups based on frequencies (Baltimore City/Baltimore County, Prince George's County; Other). Comorbid conditions were also grouped by the total number (calculated from ever diagnosed + breast cancer diagnosis) and then categorically: breast cancer only; 2–5 comorbid conditions; 6+ comorbid conditions. PA was determined using the Centers for Disease Control and Prevention's (CDC) National Health and Nutrition Examination Survey (NHANES) Physical Activity Questionnaire for vigorous- and moderate-intensity and strength training minutes/week, days/week, hours/day, and minutes/day types of PA. These variables were utilized to calculate minutes of each type of activity per week and on how many days per week each activity was completed. Number of days per week and minutes/week were utilized to determine if each participant had met aerobic (moderate- or vigorous-intensity PA) and/or strength training PA per week, determined by the American College of Sports Medicine (ACSM) exercise recommendations for cancer populations [30]. Recommended weekly aerobic PA recommendations were met (did not meet recommendations vs. met recommendations) if participants reported at least 150 min/week of moderate-intensity or at least 75 min/week of vigorous-intensity (or a combination of both) activity on three or more days/week. Weekly strength training recommendations were met (did not meet recommendations vs. met recommendations) if the participant reported strength training PA of at least moderate-intensity on at least 2 days/week. Sedentary and light-intensity PA minutes/week were not utilized in ACSM categorizations, however, descriptive statistics were analyzed.
## Statistical analysis
Responses from the online survey were downloaded from REDCap into SAS version 9.4 and Stata version 16. Descriptive characteristics (demographic, clinical, and lifestyle factors) were analyzed in terms of frequencies, means, standard deviations (SD) overall and by county. Statistical analyses were performed using Stata version 16.
## Results
Table 1 shows the demographic characteristics of the study population ($$n = 100$$ completed surveys) overall and by county of residence. The average ages at time of survey and at primary breast cancer diagnosis were 58.6 years (SD = 10.1) and 49.1 years (SD = 10.2), respectively, with an average of 9.2 years (SD = 8.4) between primary breast cancer diagnosis and time of survey. Women reported living in Prince George's County ($33\%$) and Baltimore City/Baltimore County ($33\%$) Maryland, and other counties ($34\%$). For sociodemographic factors, $36\%$ reported having a college degree as their highest level of education, and $47.1\%$ have an annual household income of ≥$75,000. When asked about participation in future research studies, $99\%$ consented to be contacted in the future and $79\%$ reported they would be interested in having their medical records abstracted for research purposes (data not shown).
**Table 1**
| Unnamed: 0 | Total (N = 100) N (%) | Maryland County | Maryland County.1 | Maryland County.2 |
| --- | --- | --- | --- | --- |
| | Total (N = 100) N (%) | Baltimore City/County (N = 33) N (%) | Prince George's (N = 33) N (%) | Other (N = 34) N (%) |
| Education | Education | Education | Education | Education |
| High school/GED or less | 13 (13.0) | 7 (21.2) | 3 (9.1) | 3 (8.8) |
| Some college | 23 (23.0) | 9 (27.3) | 5 (15.2) | 9 (26.5) |
| 4-year college degree | 36 (36.0) | 12 (36.4) | 16 (48.5) | 8 (23.5) |
| Any graduate degree | 28 (28.0) | 5 (15.2) | 9 (27.3) | 14 (41.2) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Current marital status | Current marital status | Current marital status | Current marital status | Current marital status |
| Married or living as married | 38 (38.0) | 8 (24.2) | 11 (33.3) | 19 (55.9) |
| Divorced | 24 (24.0) | 7 (21.2) | 9 (27.3) | 8 (23.5) |
| Separated | 3 (3.0) | 1 (3.0) | 0 (0.0) | 2 (5.9) |
| Widowed | 5 (5.0) | 4 (12.1) | 0 (0.0) | 1 (2.9) |
| Single (never married) | 29 (29.0) | 12 (36.4) | 13 (39.4) | 4 (11.8) |
| Missing | 1 (1.0) | 1 (3.0) | 0 (0.0) | 0 (0.0) |
| Current employment status | Current employment status | Current employment status | Current employment status | Current employment status |
| Working full-time | 45 (45.0) | 14 (42.4) | 13 (39.4) | 18 (52.9) |
| Retired | 31 (31.0) | 14 (42.4) | 8 (24.2) | 9 (26.5) |
| Other (disability, unemployed, homemaker, working part-time) | 24 (24.0) | 5 (15.2) | 12 (36.4) | 7 (20.6) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Annual household income | Annual household income | Annual household income | Annual household income | Annual household income |
| Less than $40,000 | 24 (24.0) | 9 (27.3) | 8 (24.2) | 7 (20.6) |
| $40,000–75,000 | 26 (26.0) | 17 (51.5) | 6 (18.2) | 3 (8.8) |
| $75,000–99,000 | 21 (21.0) | 3 (9.1) | 7 (21.2) | 11 (32.4) |
| $100,000 or more | 26 (26.0) | 3 (9.1) | 11 (33.3) | 12 (35.3) |
| Missing | 3 (3.0) | 1 (3.0) | 1 (3.0) | 1 (2.9) |
| Health insurance at diagnosis | Health insurance at diagnosis | Health insurance at diagnosis | Health insurance at diagnosis | Health insurance at diagnosis |
| No insurance | 2 (2.0) | 0 (0.0) | 1 (3.0) | 1 (2.9) |
| Private | 69 (69.0) | 22 (66.7) | 23 (69.7) | 24 (70.6) |
| Medicare or Medicaid | 16 (16.0) | 5 (15.2) | 4 (12.1) | 7 (20.6) |
| Other | 13 (13.0) | 6 (18.2) | 5 (15.2) | 2 (5.9) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Health insurance currently | Health insurance currently | Health insurance currently | Health insurance currently | Health insurance currently |
| No insurance | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Private | 50 (50.0) | 12 (36.4) | 18 (54.5) | 20 (58.8) |
| Medicare or Medicaid | 36 (36.0) | 15 (45.5) | 9 (27.3) | 12 (35.3) |
| Other | 13 (13.0) | 5 (15.2) | 6 (18.2) | 2 (5.9) |
| Missing | 1 (1.0) | 1 (3.0) | 0 (0.0) | 0 (0.0) |
| Residency status | Residency status | Residency status | Residency status | Residency status |
| Homeowner | 63 (63.0) | 19 (57.6) | 22 (66.7) | 22 (64.7) |
| Not a homeowner | 37 (37.0) | 14 (42.4) | 11 (33.3) | 12 (35.3) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| | M (SD) Range | M (SD) Range | M (SD) Range | M (SD) Range |
| Current age (in years) | 58.6 (10.1) 32.0–78.0 | 62.4 (8.63) 41.0–76.0 | 58.0 (9.29) 33.0–78.0 | 55.3 (11.2) 32.0–71.0 |
| Age at primary breast cancer diagnosis (in years) | 49.1 (10.2) 25.0–78.0 | 47.7 (9.99)26.0–64.0 | 49.9 (10.3) 25.0–78.0 | 49.6 (10.5) 28.0–69.0 |
Table 2 shows the clinical characteristics of the study population overall and by county. Most women ($70\%$) were diagnosed with stages I-IV BC and only $22\%$ were currently undergoing treatment at the time of the survey. A total of 19 women reported experiencing a breast cancer recurrence, $15\%$ reported a metastasis, and $15\%$ also reported experiencing a second primary cancer. A total of 60 women reported having genetic testing and 32 had first-degree relatives with breast cancer. While $77\%$ reported having breast cancer screening in the past year, the lowest prevalence of recent breast cancer screening was observed among survivors living in Baltimore City/Baltimore County ($60.6\%$).
**Table 2**
| Unnamed: 0 | Total (N = 100) N (%) | Maryland County | Maryland County.1 | Maryland County.2 |
| --- | --- | --- | --- | --- |
| | Total (N = 100) N (%) | Baltimore City/County (N = 33) N (%) | Prince George's (N = 33) N (%) | Other (N = 34)N (%) |
| Stage of breast cancer at diagnosis | Stage of breast cancer at diagnosis | Stage of breast cancer at diagnosis | Stage of breast cancer at diagnosis | Stage of breast cancer at diagnosis |
| In situ | 28 (28.0) | 11 (33.3) | 9 (27.3) | 8 (23.5) |
| Stages I-IV | 70 (70.0) | 21 (63.6) | 23 (69.7) | 26 (76.5) |
| Missing | 2 (2.0) | 1 (3.0) | 1 (3.0) | 0 (0.0) |
| Previous breast cancer treatment completed | Previous breast cancer treatment completed | Previous breast cancer treatment completed | Previous breast cancer treatment completed | Previous breast cancer treatment completed |
| Breast-conserving surgery | 51 (51.0) | 18 (54.5) | 16 (48.5) | 17 (50.0) |
| Mastectomy | 46 (46.0) | 17 (51.5) | 14 (42.4) | 15 (44.1) |
| Radiation | 58 (58.0) | 18 (54.5) | 22 (66.7) | 18 (52.9) |
| Adjuvant chemotherapy | 36 (36.0) | 16 (48.5) | 11 (33.3) | 9 (26.5) |
| Neoadjuvant chemotherapy | 14 (14.0) | 1 (3.0) | 6 (18.2) | 7 (20.6) |
| Hormone or endocrine therapy | 47 (47.0) | 13 (39.4) | 13 (39.4) | 21 (61.8) |
| Targeted drug therapy | 6 (6.0) | 0 (0.0) | 3 (9.1) | 3 (9.1) |
| Immunotherapy or biological therapies | 3 (3.0) | 0 (0.0) | 1 (3.0) | 2 (5.9) |
| Stem cell transplant | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Precision medicine | 1 (1.0) | 0 (0.0) | 0 (0.0) | 1 (2.9) |
| Clinical trial | 4 (4.0) | 1 (3.0) | 1 (3.0) | 2 (5.9) |
| No treatment(s) | 3 (3.0) | 0 (0.0) | 2 (6.1) | 1 (2.9) |
| Current status of cancer treatment | Current status of cancer treatment | Current status of cancer treatment | Current status of cancer treatment | Current status of cancer treatment |
| Currently undergoing treatment | 22 (22.0) | 3 (9.1) | 9 (27.3) | 10 (29.4) |
| Completed treatment | 69 (69.0) | 26 (78.8) | 22 (66.7) | 21 (61.8) |
| Did not receive treatment | 5 (5.0) | 2 (6.1) | 2 (6.1) | 1 (2.9) |
| Missing | 4 (4.0) | 2 (6.1) | 0 (0.0) | 2 (5.9) |
| Experienced a recurrence | Experienced a recurrence | Experienced a recurrence | Experienced a recurrence | Experienced a recurrence |
| No | 80 (80.0) | 25 (75.8) | 27 (81.8) | 28 (82.4) |
| Yes | 19 (19.0) | 8 (24.2) | 5 (15.2) | 6 (17.6) |
| Missing | 1 (1.0) | 0 (0.0) | 1 (3.0) | 0 (0.0) |
| Metastases in breast cancer diagnosis | Metastases in breast cancer diagnosis | Metastases in breast cancer diagnosis | Metastases in breast cancer diagnosis | Metastases in breast cancer diagnosis |
| No | 85 (85.0) | 28 (84.8) | 27 (81.8) | 30 (88.2) |
| Yes | 15 (15.0) | 5 (15.2) | 6 (18.2) | 4 (11.8) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Experienced second primary | Experienced second primary | Experienced second primary | Experienced second primary | Experienced second primary |
| No | 84 (84.0) | 27 (81.8) | 29 (85.3) | 28 (84.8) |
| Yes | 15 (15.0) | 6 (18.2) | 5 (14.7) | 4 (12.1) |
| Missing | 1 (1.0) | 0 (0.0) | 0 (0.0) | 1 (3.0) |
| Experienced any other cancer diagnoses in lifetime | Experienced any other cancer diagnoses in lifetime | Experienced any other cancer diagnoses in lifetime | Experienced any other cancer diagnoses in lifetime | Experienced any other cancer diagnoses in lifetime |
| No | 85 (85.0) | 27 (81.8) | 27 (81.8) | 31 (91.2) |
| Yes | 15 (15.0) | 6 (18.2) | 6 (18.2) | 3 (8.8) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Completed breast cancer screening measured in the last year | Completed breast cancer screening measured in the last year | Completed breast cancer screening measured in the last year | Completed breast cancer screening measured in the last year | Completed breast cancer screening measured in the last year |
| No | 22 (22.0) | 13 (39.4) | 3 (9.1) | 6 (17.6) |
| Yes | 77 (77.0) | 20 (60.6) | 29 (87.9) | 28 (82.4) |
| Missing | 1 (1.0) | 0 (0.0) | 1 (3.0) | 0 (0.0) |
| First-degree relatives with breast cancer | First-degree relatives with breast cancer | First-degree relatives with breast cancer | First-degree relatives with breast cancer | First-degree relatives with breast cancer |
| No | 68 (68.0) | 22 (66.7) | 23 (67.6) | 23 (69.7) |
| Yes | 32 (32.0) | 11 (33.3) | 11 (32.4) | 10 (30.3) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Undergo genetic testing for breast cancer-related genetic mutations | Undergo genetic testing for breast cancer-related genetic mutations | Undergo genetic testing for breast cancer-related genetic mutations | Undergo genetic testing for breast cancer-related genetic mutations | Undergo genetic testing for breast cancer-related genetic mutations |
| No | 40 (40.0) | 18 (54.5) | 11 (32.4) | 11 (33.3) |
| Yes | 60 (60.0) | 15 (45.5) | 23 (67.6) | 22 (66.7) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
Tables 3, 4 show the prevalence of comorbidities among the study population and the number of survivors with multiple chronic conditions/multimorbidity, respectively. More than half of the study participants reported having hypertension ($51\%$, of which most were taking hypertension medication $$n = 43$$), followed by arthritis ($45\%$), lymphedema ($32\%$) and self-reported obesity ($32\%$) (Table 3), and $28\%$ reported having ≥6 comorbid conditions (Table 4). General health perceptions were mostly good to excellent ($79\%$); however, $28\%$ reported worse health currently than at the time of breast cancer diagnosis (data not shown).
Table 5 shows the prevalence of behavioral factors and obesity for the study population overall and by county. Of note, while only $7\%$ reported being obese (BMI ≥ 30 kg/m2) at the time of the breast cancer diagnosis, $54\%$ reported being obese at the time of their online survey. Most women ($95\%$) reported drinking one or fewer alcoholic drinks/day and $70\%$ were never smokers; however, most ever smoking participants resided in Baltimore City/Baltimore County ($$n = 18$$). Only 6 women reported current smoking overall. Only $28\%$ of the survivors reported meeting ACSM weekly exercise recommendations of more than 150 min/week of vigorous or moderate activity and $35\%$ reported meeting ACSM strength training recommendations weekly.
**Table 5**
| Unnamed: 0 | Total (N = 100) N (%) | Maryland County | Maryland County.1 | Maryland County.2 |
| --- | --- | --- | --- | --- |
| | Total (N = 100) N (%) | Baltimore City/County (N = 33) N (%) | Prince George's (N = 33) N (%) | Other (N = 34) N (%) |
| Alcohol in the past 12 months | Alcohol in the past 12 months | Alcohol in the past 12 months | Alcohol in the past 12 months | Alcohol in the past 12 months |
| Never drink alcohol | 27 (27.0) | 8 (24.2) | 7 (21.2) | 12 (35.3) |
| Drink alcohol | 73 (73.0) | 25 (75.8) | 26 (78.8) | 22 (64.7) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| ACS Alcohol intake recommendations | ACS Alcohol intake recommendations | ACS Alcohol intake recommendations | ACS Alcohol intake recommendations | ACS Alcohol intake recommendations |
| One or fewer drinks/day | 95 (95.0) | 33 (100.0) | 30 (90.9) | 32 (94.1) |
| Two or more drinks/day | 5 (5.0) | 0 (0.0) | 3 (9.1) | 2 (5.9) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Currently smoke cigarettes | Currently smoke cigarettes | Currently smoke cigarettes | Currently smoke cigarettes | Currently smoke cigarettes |
| No | 94 (94.0) | 29 (87.9) | 33 (100.0) | 32 (94.1) |
| Yes | 6 (6.0) | 4 (12.1) | 0 (0.0) | 2 (5.9) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Ever smoke cigarettes | Ever smoke cigarettes | Ever smoke cigarettes | Ever smoke cigarettes | Ever smoke cigarettes |
| No | 70 (70.0) | 15 (45.5) | 27 (81.8) | 28 (82.4) |
| Yes | 30 (30.0) | 18 (54.5) | 6 (18.2) | 6 (17.6) |
| Missing | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Overall health of diet | Overall health of diet | Overall health of diet | Overall health of diet | Overall health of diet |
| Excellent | 11 (11.0) | 4 (12.1) | 3 (9.1) | 4 (11.8) |
| Very good | 18 (18.0) | 5 (15.2) | 7 (21.2) | 6 (17.6) |
| Good | 32 (32.0) | 11 (33.3) | 14 (42.4) | 7 (20.6) |
| Fair | 29 (29.0) | 11 (33.3) | 5 (15.2) | 13 (38.2) |
| Poor | 3 (3.0) | 0 (0.0) | 1 (3.0) | 2 (5.9) |
| Missing | 7 (7.0) | 2 (6.1) | 3 (9.1) | 2 (5.9) |
| Obesity status currently | Obesity status currently | Obesity status currently | Obesity status currently | Obesity status currently |
| Underweight/normal weight (BMI < 24.9) | 10 (10.0) | 1 (3.0) | 7 (21.2) | 2 (5.9) |
| Overweight (BMI 25.0 to < 29.9) | 34 (34.0) | 13 (39.4) | 9 (27.3) | 12 (35.3) |
| Obese (BMI ≥ 30) | 54 (54.0) | 18 (54.5) | 17 (51.5) | 19 (55.9) |
| Missing | 2 (2.0) | 1 (3.0) | 0 (0.0) | 1 (2.9) |
| Obesity status at age 25 | Obesity status at age 25 | Obesity status at age 25 | Obesity status at age 25 | Obesity status at age 25 |
| Underweight/normal weight (BMI < 24.9) | 53 (53.0) | 20 (60.6) | 16 (48.5) | 17 (50.0) |
| Overweight (BMI 25.0 to < 29.9) | 23 (23.0) | 7 (21.2) | 9 (27.3) | 7 (20.6) |
| Obese (BMI ≥ 30) | 14 (14.0) | 5 (15.2) | 5 (15.2) | 4 (11.8) |
| Missing | 10 (10.0) | 1 (3.0) | 3 (9.1) | 6 (17.6) |
| Obesity status at diagnosis | Obesity status at diagnosis | Obesity status at diagnosis | Obesity status at diagnosis | Obesity status at diagnosis |
| Underweight/normal weight (BMI < 24.9) | 74 (74.0) | 21 (63.6) | 26 (78.8) | 27 (79.4) |
| Overweight (BMI 25.0 to < 29.9) | 13 (13.0) | 6 (18.2) | 5 (15.2) | 2 (5.9) |
| Obese (BMI ≥ 30) | 7 (7.0) | 3 (9.1) | 2 (6.1) | 2 (5.9) |
| Missing | 6 (6.0) | 3 (9.1) | 0 (0.0) | 3 (8.8) |
| Meeting ACSM weekly aerobic PA recommendations (>150 min/wk vigorous or moderate activity) | Meeting ACSM weekly aerobic PA recommendations (>150 min/wk vigorous or moderate activity) | Meeting ACSM weekly aerobic PA recommendations (>150 min/wk vigorous or moderate activity) | Meeting ACSM weekly aerobic PA recommendations (>150 min/wk vigorous or moderate activity) | Meeting ACSM weekly aerobic PA recommendations (>150 min/wk vigorous or moderate activity) |
| Does not meet recommendations | 56 (56.0) | 18 (54.5) | 19 (57.6) | 19 (55.9) |
| Meets aerobic recommendations | 28 (28.0) | 12 (36.4) | 8 (24.2) | 8 (23.5) |
| Missing | 16 (16.0) | 3 (9.1) | 6 (18.2) | 7 (20.6) |
| Meets ACSM weekly strength training PA recommendations (2×per week) | Meets ACSM weekly strength training PA recommendations (2×per week) | Meets ACSM weekly strength training PA recommendations (2×per week) | Meets ACSM weekly strength training PA recommendations (2×per week) | Meets ACSM weekly strength training PA recommendations (2×per week) |
| Does not meet recommendations | 51 (51.0) | 19 (57.6) | 17 (51.5) | 15 (44.1) |
| Meets aerobic recommendations | 35 (35.0) | 10 (30.3) | 12 (36.4) | 13 (38.2) |
| Missing | 14 (14.0) | 4 (12.1) | 4 (12.1) | 6 (17.6) |
| | M (SD) Range | M (SD) Range | M (SD) Range | M (SD) Range |
| Current body mass index (BMI) | 31.7 (6.95) 19.7–63.0 | 32.5 (5.98) 23.3–46.1 | 30.6 (8.61) 19.7–63.0 | 32.0 (5.95) 20.9–43.8 |
| BMI at age 25 | 24.9 (5.06) 15.6–46.8 | 24.1 (4.32) 15.6–32.2 | 25.4 (5.64) 17.2–46.8 | 25.2 (5.25) 18.1–39.4 |
| BMI at diagnosis | 21.0 (6.18) 10.1–42.4 | 21.7 (6.47) 13.6–38.0 | 21.4 (6.74) 10.1–42.4 | 20.0 (5.25) 11.7–36.3 |
| Vigorous PA—min/wk | 102.0 (225.2) 0.0–1,220.0 | 129.8 (273.5) 0.0–1,220.0 | 104.6 (238.8) 0.0–854.0 | 66.2 (131.2) 0.0–427.0 |
| Moderate PA—min/wk | 301.5 (541.1) 0.0–2,520.0 | 317.7 (589.7) 0.0–2,520.0 | 155.2 (244.2) 0.0–840.0 | 424.4 (666.9) 0.0–2,310.0 |
| Walking PA—min/wk | 454.5 (627.7) 0.0–2,940.0 | 480.0 (688.5) 50.0–2,940.0 | 333.1 (426.3) 0.0–1,680.0 | 538.2 (713.7) 0.0–2,520.0 |
| Sedentary activity—min/wk | 2,640.0 (1,886.7) 70.0–10,080.0 | 2,448.3 (1,967.8) 240.0–86,840.0 | 2,771.0 (1,979.6) 120.0–10,080.0 | 2,704.8 (1,748.2) 70.0–8,400.0 |
| Aerobic (Vig+Mod PA)—min/wk | 385.7 (713.8) 0.0–3,740.0 | 442.5 (858.4) 0.0–3,740.0 | 270.3 (447.1) 0.0–1,389.0 | 453.1 (790.6) 0.0–2,615.0 |
| Strength training—min/wk | 89.6 (136.1) | 68.6 (104.2) 0.0–420.0 | 100.0 (153.0) 0.0–630.0 | 100.5 (148.6) 0.0–630.0 |
| Age began smoking cigarettes | 18.2 (7.85) 12.0–50.0 | 17.9 (6.06) 12.0–35.0 | 15.1 (2.31) 12.0–18.0 | 22.0 (13.9) 12.0–50.0 |
| Time since quitting smoking cigarettes (years) | 20.5 (14.9) 0.0–50.0 | 22.7 (15.0) 0.0–50.0 | 20.5 (10.6) 10.0–38.0 | 10.6 (19.6) 0.4–40.0 |
| When quit, # of cigarettes smoked daily | 9.50 (9.55) 0.0–40.0 | 9.75 (10.3) 0.0–40.0 | 13.50 (8.57) 2.0–23.0 | 2.50 (1.00) 1.0–3.0 |
| Past 30 days on the days smoked, how many cigarettes smoked | 2.65 (3.06) 0.0–10.0 | 3.08 (3.08) 0.0–10.0 | 1.67 (2.16) 0.0–6.0 | 2.80 (4.20) 0.0–10.0 |
## Discussion
Our pilot study examined a new recruitment strategy that could increase the enrollment and retention of minority breast cancer survivors into community-based studies. Maryland is the ideal state to conduct such a study given that the African American/Black population represents more than $30\%$ of the total population, unlike the US where the African American/Black population is close to $14\%$ [27]. For initial study findings, we have identified at-risk breast cancer survivors in Maryland due to high prevalence of CVD risk factors including high prevalence of hypertension, current obesity due to weight gain post diagnosis, and lack of meeting PA recommendations.
Hypertension is the most prevalent comorbidity reported among breast cancer patients, regardless of age, followed by CVD and type-2 diabetes [31, 32]. While we observed hypertension to be the most reported comorbidity among our study population, interestingly none of our study participants reported a history of CVD. Hypertension is an important comorbidity to study among Black women with breast cancer, as it has been found to account for $30\%$ of the Black-White racial disparity in all-cause mortality [33]. The timing of when hypertension develops (before or after breast cancer diagnosis) should also be considered in studies of survival disparities to determine if interventions to prevent hypertension and/or decrease its severity could be beneficial to decreasing risk of poor health outcomes. Among Black women diagnosed with invasive breast cancer in Missouri ($$n = 3$$,039), we identified a significant difference in risk of CVD mortality among women with a diagnosis of hypertension within 2 years of breast cancer diagnosis, in comparison with women without hypertension (subdistribution hazard ratio, 1.87; $95\%$ confidence interval: 1.02–3.44) [17]. While our current pilot study did not account for when hypertension was diagnosed, our future studies will include the timing of comorbidity diagnoses as some may be related to receipt of certain cancer treatment associated with risk of cardiac toxicity (23, 34–38) which has also been found to be prevalent among Black breast cancer patients [39, 40].
Cancer research examining disparities in cancer survivorship and modifiable risk behaviors has been mostly cancer-specific, leaving relevant gaps in disparities research relating to other cancer survivorship outcomes, including CVD outcomes. Maintaining healthy lifestyle behaviors is a critical component of successful cancer survivorship, where unhealthy behaviors may increase the risk for recurrence, second primary cancers, and incidence of new comorbid conditions, including CVD and impact quality of life post-cancer [41]. A systematic literature review conducted by Tollosa et al. described health behaviors of cancer survivors, finding that the recommended health behaviors most frequently adhered to were smoking cessation and low (or no) alcohol intake [42]. Also comparable with our results, research has documented that Black breast cancer survivors report higher prevalence of obesity [43, 44]. Our finding that women became obese after diagnosis should be further explored. We also recently examined racial/ethnic disparities in maintaining healthy lifestyle behaviors among 2,044 female cancer survivors utilizing data from the NHANES, with almost $30\%$ of the sample being breast cancer survivors and $14\%$ of the sample were non-Hispanic Black (NHB) [45]. Overall, NHB survivors were more likely to be overweight/obese and were more likely to report not meeting weekly exercise recommendations compared to NHW survivors. NHB survivors were also less likely to report ever smoking and were less likely to drink two or more alcoholic drinks per day, compared to NHW survivors [45].
The current analysis has strengths and limitations. Past literature on the use of social media among breast cancer survivors has shown that limited research has focused on non-Caucasian populations [46]. Our study adds to the growing body of literature focused on the use of social media and networks for recruiting and conducting population-based studies among African American/Black breast cancer survivors. There are study limitations associated with social media study recruitment methods which can introduce biases. Social media recruitment has shown to capture geographic diversity, but could attract a more highly distressed subgroup with online activity that connects to patient groups and/or online activity makes them targets for recruitment ads [47]. These factors could introduce selection bias with respect to lifestyle factors and comorbidity burden. For example, we observed that none of our study participants reported a history of CVD, one of the most reported comorbidities among breast cancer survivors. This finding could be attributed to a healthy survivor bias. Furthermore, social media recruitment has had limited participation of older adults [48]. With comparison of our previous study of newly diagnosed breast cancer patients in Maryland utilizing Maryland Cancer Registry data, the mean age at diagnosis was 58.2 years among Black women diagnosed between 2007 and 2017 [25], while the mean age at diagnosis for the current pilot study was 49.1 years.
We also must consider there could be differences in participation by time since diagnosis, as we did not limit our inclusion criteria by active treatment or time since breast cancer diagnosis. Women who are sicker due to their cancer and/or on active treatment may have been less likely to participate and among those who participated, may have different lifestyle factors and healthcare experiences. Additionally, with respect to collection of data for lifestyle factors, it should be noted that behavioral factors showed the highest prevalence of missing values, which limits the interpretation of this data. Lastly, most of our study population reported both higher incomes and education levels and were from more urban areas. Taking into account these study limitations, our descriptive results may not be generalizable for most Black breast cancer patients and survivors in Maryland; however, our future research studies utilizing this recruitment approach will address these study limitations.
## Conclusion
Our research goal was to utilize our pilot study of community-based Black breast cancer survivors to gain a better understanding of the comorbidity burden and describe the prevalence of behavioral risk factors associated with CVD risk among Black survivors in Maryland. We identified at-risk breast cancer survivors due to the high prevalence of CVD risk factors including hypertension, current obesity, and the lack of meeting aerobic and strength training PA recommendations. These research methods will inform a statewide multilevel prospective population-based study to improve health behaviors and disease management among Black women breast cancer survivors in Maryland at high risk of poor outcomes due to biological differences and socioeconomic inequities.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Johns Hopkins Bloomberg School of Public Health Institutional Review Board. Passive consent for participation was obtained electronically from the participants. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
AC was responsible for the study design, design of the analysis, and manuscript writing. KD was responsible for the data analysis, tables, and manuscript writing. KV was responsible for reviewing and editing the manuscript. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Efficacy of Perilla frutescens (L.) Britton var. frutescens extract on mild
knee joint pain: A randomized controlled trial'
authors:
- NamHoon Kim
- Si-Yeon Kim
- Sang-Woo Kim
- Jung Min Lee
- Sung-Kyu Kim
- Mi-Houn Park
- Ki-Hwan Kim
- Minseok Oh
- Chang-Gue Son
- In Chul Jung
- Eun-Jung Lee
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10043449
doi: 10.3389/fphar.2023.1114410
license: CC BY 4.0
---
# Efficacy of Perilla frutescens (L.) Britton var. frutescens extract on mild knee joint pain: A randomized controlled trial
## Abstract
Objectives: This study aimed to evaluate the clinical efficacy and safety of PE extracts developed for the purpose of relieving pain and improving knee joint function on semi-healthy people with mild knee joint pain.
Methods: A randomized, double-blind, two-arm, single-center, placebo-controlled clinical trial was conducted. Individuals with knee joint pain and a visual analogue scale (VAS) score < 50 mm were included in the study, and participants with radiological arthritis were excluded. Participants were administered either PFE or a placebo capsule (700 mg, twice a day) orally for eight weeks. The comparisons of the changed VAS score and Western Ontario and McMaster Universities Osteoarthritis (WOMAC) scores between the PFE and placebo groups were primary outcomes, while the five inflammation-related laboratory tests including cartilage oligomeric matrix protein, cyclooxygenase-2, neutrophil and lymphocyte ratio, high sensitive C-reactive protein, and erythrocyte sedimentation rate were secondary outcomes. Also, a safety assessment was done.
Results: Eighty participants (mean age, 38.4 ± 14.0, male: female, 28:52) were enrolled; 75 completed the trial (PFE 36 and placebo 39). After eight weeks, both VAS and WOMAC scores were reduced in the PFE and placebo groups. The changed scores were significantly higher in the PFE group compared to the placebo group: 19.6 ± 10.9 vs. 6.8 ± 10.5; VAS scores ($p \leq 0.001$), and 20.5 ± 14.7 vs. 9.3 ± 16.5; total WOMAC scores ($p \leq 0.01$) including the sub-scores for pain, stiffness, and functions. No significant changes were reported in the five inflammation-related laboratory parameters. All adverse events were considered minor and unlikely to result from the intervention.
Conclusion: Eight weeks of PFE intake was more effective than placebo in reducing knee joint pain and improving knee joint function in sub-healthy people with mild knee joint pain, and there were no major safety concerns.
Clinical Trial Registration: https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=23101&status=5&seq_group=19745, identifier CRIS: KCT0007219
## 1 Introduction
The knee is a modified hinge joint, having both tibiofemoral and patellofemoral components. The human knee joint evolved to adapt to bipedalism more than 300 million years ago (Dye, 2003). The continuous and repetitive stress from everyday activities, such as jogging, playing sports, working, standing, or sitting, makes the knee joints susceptible to problems such as injury or even osteoarthritis (Hartmann et al., 2013). Accordingly, the knee joint is the site at which the “wear-and-tear” type of arthritis occurs most commonly (Darlow et al., 2018), and knee-joint pain has a $22.9\%$ global prevalence in individuals aged 40 and over (Cui et al., 2020).
On the other hand, knee pain is a typical symptom of mechanical disorders, likely injury, or osteoarthritis (Previtali et al., 2022). In addition, knee pain can commonly occur in conditions of functional overload prior to progression to those mechanical problems (Felson, 2013), where its prevalence rate is approximately $30\%$ of adults worldwide (Peat, 2001; Nguyen et al., 2011). Knee pain prevalence generally increases with age, especially in females; $46.2\%$ as a 1.8-fold female dominance in the Korean population aged ≥ 50 years (Kim et al., 2011).
Knee joint pain increases health risks such as fall-related injuries and limits physical movement and daily activities (Svensson et al., 2000; Lajoie and Gallagher, 2004; Lee et al., 2021). Chronic knee pain is often the result of several causes or conditions and needs certain treatments (Peat, 2001). Recommended treatments for knee pain include exercises, physiotherapy, or pharmacological medicines such as non-steroidal anti-inflammatory drugs (NSAIDs) or acetaminophen (AAP) (Conaghan et al., 2008). Several researchers have been interested in nutritional supplements and functional foods as an option to care for knee joint pain and disability (Ginnerup-Nielsen et al., 2015; Suzuki et al., 2016; Zdzieblik et al., 2017; Rao et al., 2019). Users expect that these multiple compounds provide benefits via targeting multiple pathways of knee dysfunction, including multifactorial cartilage degradation, as an alternative to pharmacological interventions that exert mainly a monomodal mode of action (Ameye and Chee, 2006).
Perilla frutescens (L.) Britton var. Frutescens (PF), also known as ‘Zisu’ in Chinese, ‘Cha-jo-ki’ or ‘So-yeop’ in Korean, and ‘Jiso’ in Japanese, is an important food ingredient and medicinal plant in East Asia (Kim et al., 2017). Its recognized bioactivities include antioxidant action Meng et al., 2008; Takahashi et al., 2011; Ahmed et al., 2022), immune control (Kwon et al., 2002), skin wound healing (Kim et al., 2021), anti-glomerulonephritis action (Makino et al., 2001), and anti-arthritis action (Jin et al., 2019). In particular, a marker compound named “Isoegomaketone” from PF has an excellent anti-inflammatory response (Kim et al., 2020), which is effective for treating arthritis and joint swelling (Jin et al., 2017). However, the aforementioned data comes from pre-clinical studies and the efficacy and safety of PF in humans has not been determined.
This trial aimed to evaluate PE extracts (PFE)’s clinical efficacy of alleviating pain and improving function of knee joint and its safety on semi-healthy people who have mild knee joint pain.
## 2.1 Participants
A total of 80 participants were enrolled for this trial at Daejeon Korean medicine hospital of Daejeon University from July 2020 to January 2021. We included male and female patients aged 20–75 years who complained of knee pain with a VAS score ≤ 50 mm (without recent any type of joint injury or similar episode; 0 and 100 indicate ‘no pain’ and ‘unbearable pain’, respectively) but without a Kellgren-Lawrence (K-L) grade ≥ 3 on plain radiographs. The range of knee pain in this study was defined as medial, lateral or peripatella pain but no radiation pain. ( www.merriam-webster.com, Nice Clinical Guidelines, 2022) We excluded any participant who had: 1) moderate to severe knee pain and taking arthritis medications; 2) moderate to severe arthritis as identified on plain radiographs; 3) a medical history of knee arthroplasty surgery; 4) been diagnosed with other musculoskeletal disorders other than knee joint pain and is being treated for pain; 5) any issues in taking products containing PFE, including allergic reaction; or 6) any other musculoskeletal pain other than knee pain; 7) plans of pregnancy or breastfeeding; and 8) heart, kidney, liver, and other organ disease. Participants were not permitted to take medication or consume health functional foods that may have an impact on the joint health. Detailed inclusion and exclusion criteria are available at CRIS: KCT0007219 (available at: https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=23101&status=5&seq_group=19745). Participants were provided with voluntary written informed consent.
In this study, the sample size was calculated based on a two-tailed alpha level of 0.05 and a power level of 0.80 respectively using G*power (version 3.1.9.2., Department of Psychology, Germany) (Faul et al., 2009). Also, the minimal detectable effect size was $r = 0.7$, and the target sample size was 80 assuming a $20\%$ dropout rate.
## 2.2 Clinical trial design
This study was conducted as a randomized, double-blind, and placebo-controlled trial (RCT) at Daejeon Korean Medicine Hospital in South Korea. The purpose of this RCT was to evaluate the efficacy and safety of an eight-week administration regime of PFE on semi-healthy people with mild knee joint pain. This trial has been conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the Institutional Review Board (IRB) of Daejeon University (DJDSKH-20-BM-11).
The automatically generated random numbers (RNs) (from 1 to 80) were allocated to participants in the order of enrolling in the trial, and the intervention (PFE or placebo) corresponding to the RN was given under double blindness (participants, assessor, principal investigator, and any officer, including statistician). Participants took PFE capsules or placebos (twice a day) for eight weeks. PFE capsules (700 mg/capsule, Lot number: FSP 2020070) are composed of PFE (486 mg, $34.7\%$) and an excipient. The excipient portion was a combination of soybean oil ($48.3\%$), palm oil ($11.3\%$), beeswax ($3.8\%$), and soybean lecithin ($2.0\%$). Meanwhile, the placebo capsule (700 mg/capsule, Lot number: FSP 2020071) consisted of only excipients (soybean oil $83\%$, palm oil $11.25\%$, beeswax $3.75\%$, and soybean lecithin $2.00\%$). PFE and placebo capsules were manufactured and supplied by Suheung Co., Ltd. (Osong-eup, Cheongju-si, South Korea). There were no differences in color, odor, consistency, packaging, or labeling between the PFE and placebo foods.
## 2.3 Efficacy assessments
The primary efficacy assessment employed pain and osteoarthritis-related parameters; VAS for knee joint pain using a 100 mm drawing bar (0 and 100 indicate ‘no pain’ and ‘worst possible, unbearable pain’, respectively) and Western Ontario and the McMaster Universities Osteoarthritis (WOMAC) score evaluating pain, stiffness, and function of the knee joint (Salaffi et al., 2003). These parameters were measured at baseline, 4 weeks, and 8 weeks. The difference of both VAS and WOMAC scores measured between at baseline and at week 8 was determined as the primary outcome of this study.
As secondary outcomes, five inflammation and cartilage-related parameters were included: cyclooxygenase-2 (COX-2), high sensitive C-reactive protein (HS-CRP), erythrocyte sedimentation rate (ESR), neutrophil and lymphocyte ratio, and cartilage oligomeric matrix protein (COMP). These parameters were measured by blood tests in fasting participants at baseline and at week 8.
## 2.4 Safety assessments
For the safety assessment of PFE, red blood cells, white blood cells, hemoglobin, hematocrit, platelets, neutrophils, and lymphocytes were analyzed as hematological tests. In addition, biochemical tests such as serum total protein, albumin, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase (ALP), blood urea nitrogen (BUN), total bilirubin, creatinine, creatine kinase, glucose (fasting blood sugar; FBS), total cholesterol, high-density lipoprotein cholesterol (HDL-Cholesterol), triglyceride, uric acid, sodium (Na), potassium (K), and chlorine were analyzed, along with routine urine analyses (pH, WBC, glucose, nitrate, protein, ketone, urobilinogen, bilirubin, and specific gravity) at baseline and eight weeks.
In addition, the participants’ vital signs [blood pressure (BP), pulse rate (PR), and body temperature (BT)] were recorded at all visits during the study. Every participant was required to report any adverse events during the trial.
## 2.5 Statistical analysis
Analyses of the efficacy of PFE in the population were mainly presented as a per-protocol (PP) set and compared with the full analysis (FAS) set, which was analyzed further. The safety of PFE was analyzed by a safety set.
Categorical variables such as sex, drinking, and smoking experiences of baseline characteristics were compared by chi-square test, and the distribution of the K-L grade was compared by Fisher’s exact test. Continuous variables within a group were compared by a paired t-test, while those between groups were compared by an independent two-sample t-test. Since baseline characteristics of age and WOMAC-pain between groups were statistically different, continuous variables of primary efficacy assessment outcomes were compared by analyzed by analysis of covariance (ANCOVA) using age and value at baseline as covariate for further analysis. Examples of continuous variables in this trial were age, height, weight, and efficacy assessment indexes. Furthermore, to compare primary efficacy assessment by week 0, week 4, week 8 we used repeated measures analysis of variance (ANOVA). A p-value of <0.05 was considered statistically significant. Statistical analysis was performed using the statistical analysis SAS software (version 9.4, SAS Institute Inc., Cary, NC, United States).
## 3.1 Characteristics of participants
This trial was conducted from July 2020 to January 2021. Of the 84 candidates screened, 80 participants fulfilling the inclusion criteria were randomized. 75 participants (36 in PFE, 39 in placebo) completed the trial, while five participants dropped out and were excluded from the PP analysis (Figure 1). Total $35\%$ of participants were men and the mean age was 38.4 ± 14.0 years. There were no differences in the baseline characteristics among the groups, except for age. The mean age of the PFE group (42.3 ± 12.6) was significantly higher than the age of placebo group (34.5 ± 14.5) (Table 1).
**FIGURE 1:** *Flow chart of the trial process. PFE: Perilla frutescens (L.) Britton var. frutescensExtract.* TABLE_PLACEHOLDER:TABLE 1
## 3.2 Primary outcomes
After the eight-week administration regime, the VAS score in the PFE group (ΔVAS PFE = 19.6 ± 10.9, $p \leq 0.001$) was significantly reduced by 12.8 ± 10.7 ($p \leq 0.001$) more than that in the placebo group (ΔVAS placebo = 6.8 ± 10.5, $p \leq 0.001$).
Additionally, the WOMAC score was significantly mitigated by 11.2 ± 15.7 ($p \leq 0.01$) more in the PFE group (ΔWOMAC-total PFE = 20.5 ± 14.7, $p \leq 0.001$) than the placebo group (ΔWOMAC-total placebo = 9.3 ± 16.5, $p \leq 0.01$). The changes in scores of all WOMAC subscales (pain, stiffness, and function) were significantly greater in the PFE group than in the placebo ($p \leq 0.01$) group after eight weeks (Table 2).
**TABLE 2**
| Variables | Variables.1 | PFE (n = 36) | Placebo (n = 39) | Differences | p-value |
| --- | --- | --- | --- | --- | --- |
| VAS | Week 0 | 33.6 ± 8.6 | 31.9 ± 7.9 | 1.7 ± 8.3 | |
| | Week 8 | 14.0 ± 10.7 | 25.1 ± 12.2 | −11.1 ± 11.5 | |
| | Change | 19.6 ± 10.9*** | 6.8 ± 10.5*** | 12.8 ± 10.7 | < 0.001*** |
| WOMAC score | WOMAC score | | | | |
| Total | Week 0 | 31.4 ± 16.1 | 25.7 ± 15.7 | 5.7 ± 16.0 | |
| Total | Week 8 | 10.8 ± 8.4 | 16.4 ± 13.1 | −5.6 ± 11.1 | |
| Total | Change | 20.5 ± 14.7*** | 9.3 ± 16.5** | 11.2 ± 15.7 | 0.005** |
| Pain | Week 0 | 6.8 ± 3.5 | 5.1 ± 3.0 | 1.7 ± 3.2* | |
| Pain | Week 8 | 2.4 ± 1.9 | 3.1 ± 2.5 | −0.7 ± 2.2 | |
| Pain | Change | 4.4 ± 3.2*** | 2.0 ± 3.2*** | 2.4 ± 3.2 | 0.040* |
| Stiffness | Week 0 | 3.2 ± 2.0 | 2.8 ± 1.7 | 0.4 ± 1.8 | |
| Stiffness | Week 8 | 1.5 ± 1.7 | 1.9 ± 1.8 | −0.4 ± 1.7 | |
| Stiffness | Change | 1.9 ± 1.5*** | 0.9 ± 1.6** | 1.0 ± 1.6 | 0.027* |
| Function | Week 0 | 21.2 ± 11.9 | 17.8 ± 12.1 | 3.4 ± 12.0 | |
| Function | Week 8 | 6.9 ± 5.3 | 11.4 ± 9.6 | −4.5 ± 7.8 | |
| Function | Change | 14.2 ± 11.2*** | 6.4 ± 12.2*** | 7.8 ± 11.7 | 0.003** |
In the PFE group, the VAS and WOMAC scores continued to decline with a similar slope until week eight. However, the change in primary outcome value in the placebo group showed a tendency to stagnate after four weeks (Figure 2). There was no statistical difference between the PP set and the FAS set (data not shown).
**FIGURE 2:** *Changes in primary outcomes between the PFE group and the placebo group for eight weeks. (A) Changes in the VAS score; (B) Changes in the WOMAC total score; (C) Changes in the WOMAC pain score; (D) Changes in the WOMAC stiffness score; (E) Changes in the WOMAC function score. Data are corrected using age and value at baseline as covariance. p-values are analyzed by repeated measures analysis of variance (ANOVA). PFE: Perilla frutescens (L.) Britton var. frutescens Extract; VAS, visual analogue scale; WOMAC, Western Ontario and McMaster Universities Osteoarthritis *p < 0.05, **: p < 0.01, ***: p < 0.001 indicates statistical significance between the change values in the PFE group and the placebo group. p-values are calculated by repeated measures analysis of variance.*
## 3.3 Secondary outcomes
In the secondary efficacy outcomes, there were no statistically significant differences in the COMP, COX-2, HS-CRP and ESR levels, and the neutrophil and lymphocyte ratio in the PFE group compared with the placebo group at the end of the intervention (Table 3). Among them, the change of CRP levels in the PFE group after eight weeks had no statistical significance but showed a tendency to increase outside the normal range, and the change of ESR levels within the PFE group increased significantly (not italics in Table 3).
**TABLE 3**
| Variables | Variables.1 | PFE (n = 36) | Placebo (n = 39) | Differences | p-value |
| --- | --- | --- | --- | --- | --- |
| Variables | Variables | PFE (n = 35)# | Placebo (n = 38)# | Differences | p-value |
| COMP (ng/mL) | Week 0 | 128.8 ± 42.9 (129.3 ± 43.4) | 127.3 ± 45.9 (127.1 ± 46.5) | 1.5 ± 44.5 (1.5 ± 44.5) | 0.297 (0.837) |
| COMP (ng/mL) | Week 8 | 142.6 ± 56.4 (143.8 ± 56.7) | 127.3 ± 43.7 (126.6 ± 44.1) | 15.3 ± 50.2 (17.2 ± 50.5) | 0.193 (0.149) |
| COMP (ng/mL) | Change | −13.7 ± 44.6 (−14.6 ± 44.9) | 0.0 ± 33.1 (0.5 ± 33.5) | 13.7 ± 39.0 (13.7 ± 39.0) | 0.132 (0.107) |
| COX-2 | Week 0 | 10.9 ± 14.3 (11.1 ± 14.5) | 16.5 ± 41.0 (16.8 ± 41.5) | −5.6 ± 31.2 (−5.6 ± 31.2) | 0.884 (0.425) |
| COX-2 | Week 8 | 11.7 ± 14.2 (11.8 ± 14.3) | 13.5 ± 23.2 (13.7 ± 23.5) | −1.8 ± 19.4 (−1.9 ± 19.6) | 0.676 (0.678) |
| COX-2 | Change | −0.7 ± 4.5 (−0.8 ± 4.6) | 3.0 ± 18.8 (3.1 ± 19.1) | 3.7 ± 13.9 (3.7 ± 13.9) | 0.232 (0.232) |
| Neutrophil | Week 0 | 2.5 ± 1.4 (2.5 ± 1.4) | 2.3 ± 0.9 (2.2 ± 0.9) | 0.2 ± 1.1 (0.2 ± 1.1) | 0.339 (0.333) |
| /Lymphocyte ratio | Week 8 | 2.3 ± 1.0 (2.2 ± 0.9) | 2.3 ± 1.0 (2.3 ± 1.0) | 0.0 ± 1.0 (−0.1 ± 1.0) | 0.906 (0.659) |
| | Change | 0.3 ± 4.8 (0.3 ± 1.4) | 0.2 ± 6.5 (−0.1 ± 1.1) | −0.1 ± 5.3 (−0.1 ± 5.3) | 0.960 (0.215) |
| HS-CRP | Week 0 | 0.8 ± 1.0 (0.8 ± 1.0) | 0.7 ± 0.7 (0.6 ± 0.7) | 0.1 ± 0.9 (0.1 ± 0.9) | 0.431 (0.410) |
| HS-CRP | Week 8 | 2.2 ± 8.8 (0.7 ± 0.8) | 0.9 ± 1.5 (0.8 ± 1.4) | 1.3 ± 6.2 (−0.1 ± 1.2) | 0.390 (0.718) |
| HS-CRP | Change | −1.4 ± 8.7 (0.1 ± 0.9) | −0.2 ± 1.1 (-0.2 ± 1.1) | 1.2 ± 6.0 (1.2 ± 6.0) | 0.434 (0.254) |
| ESR | Week 0 | 12.5 ± 6.4 (12.7 ± 6.4) | 12.7 ± 10.9 (11.6 ± 8.5) | −0.2 ± 9.0 (-0.2 ± 9.0) | 0.917 (0.545) |
| ESR | Week 8 | 14.3 ± 7.3 (14.1 ± 7.2) | 13.0 ± 11.0 (11.9 ± 9.0) | 1.3 ± 9.4 (2.1 ± 8.2) | 0.537 (0.270) |
| ESR | Change | −1.8 ± 5.2* (−1.4 ± 4.6) | −0.3 ± 3.6 (−0.3 ± 3.6) | 1.5 ± 4.4 (1.5 ± 4.4) | 0.143 (0.278) |
After precisely analyzing the CRP and ESR levels in 75 participants, we confirmed that the random number (RN) 48 had an abnormally elevated HS-CRP value after eight weeks due to severe shin inflammation and RN 46 had an abnormally elevated ESR value due to chronic lung disease (Supplementary Figure S1). Excluding these two participants (RN 46 and 48), on analysing again, it has been found that HS-CRP levels in the PFE group showed a tendency to decrease in the normal range after eight weeks, and the change in ESR levels in the PFE group had no statistical significance (italics in Table 3). There were no statistically significant changes in the primary outcome (data not shown).
## 3.4 Safety assessments
Routine laboratory tests, urine analyses, and vital sign results did not show any significant changes after intervention and were within the normal range throughout the trial (data not shown).
Of the 80 participants, 14 participants experienced 15 episodes of adverse events during the intervention period. Six participants (seven cases) in the PFE group and eight participants (eight cases) in the placebo group were reported to have experienced adverse events. The difference in the number of participants who experienced adverse events was not statistically significant between the groups ($p \leq 0.5$, analyzed by chi-square test). All adverse events were considered minor and were not likely caused by the intervention (Supplementary Table S1).
## 4 Discussion
In an eight-week RCT format, we evaluated the PFE effects on knee joint pain in participants with mild knee discomfort. The average VAS and WOMAC scores in 80 participants were 31.1 ± 8.6 and 28.1 ± 17.2, respectively, indicating a severity of approximately $30\%$ of the total score in both evaluation indicators. As two primary outcomes in the present RCT, both VAS and WOMAC scores were well correlated with each other (Pearson correlation coefficient 0.63, $p \leq 0.001$, data not shown), as observed in other studies, including those on moderate to severe knee osteoarthritis (KOA) (Wang, 2017; Guo et al., 2018).
While the VAS is an intuitive and persistent indicator of general pain intensity, the WOMAC score reflects pain and function during activity associated with KOA in three domains: pain (20 points), joint stiffness (8 points), and function (68 points) (Chiarotto et al., 2019). The average WOMAC function score of the participants in our study (19.4 ± 12.0) was worse than the average value of those aged 75 to 79 (14.3 ± 15.4) in the general population of 5,509 Australians (Bellamy et al., 2009). When we evaluated the 75 participants who completed the entire trial for the VAS score, the WOMAC stiffness score, physical examination, and K-L grade according to the American College of Rheumatology (ACR) clinical criteria (defined by pain plus 3 or more of 6 factors) (Altman et al., 1986), 68 participants met the criteria for mild level of KOA. Based on the above two findings, we could assume that the participants showed mild symptoms of KOA at an early stage, even though the average age of participants (38.4 ± 14.0 years) was lower than the generally known age of osteoarthritis morbidity (age > 45 years) (www.nice.org.uk, Knee, 2014).
Except for four participants in the PFE group (three had withdrawn consent and one had an adverse event) and one participant in the placebo group (withdrawn consent), 75 participants completed the entire trial course. The eight-week administration of PFE significantly reduced the VAS score of knee pain by $58.3\%$ in the PFE group (ΔVAS = 19.6 ± 10.9), which was significantly greater than the change in the placebo group ($21.3\%$ reduction, $p \leq 0.001$) (Table 2; Figure 2). This effect was repeated as a similar pattern in the WOMAC scores ($65.3\%$ in PFE vs. $36.2\%$ in placebo, $p \leq 0.01$, Table 2; Figure 2). The placebo effect in this study might be related to the mild pain symptoms experienced by the participants. Previous research suggests that the higher the placebo effect, the less severe the symptoms and the shorter the duration of the disease (Kirsch, 2013; Enck and Klosterhalfen, 2019). Adverse events reported by one withdrawn female participant in the PFE group were discomfort, such as swelling and tingling of the tongue. She had been suffering from a Korean somatization disorder (called Hwa-Byung), and the symptoms disappeared after acupuncture treatments. All adverse events, including a digestive system problem, were reported at similar levels in both groups (seven cases in PFE vs. eight cases in placebo), which indicate no relationship with the intervention (Supplementary Table S2).
Knee pain can be caused by a variety of functional or organic problems with structural components such as subchondral bone, meniscus, ligaments, tendons, and muscles (Netter and Dalley, 2003; Lane et al., 2011). One of the main causes of knee pain is KOA (Zeni et al., 2010), which is the consequence of repeated stress on the knee by wear and tear sequences, resulting in progressive loss of articular cartilage (Darlow et al., 2018). High body mass index (BMI) scores, women, and smoking act as risk factors for the onset and exacerbation of KOA (Blagojevic et al., 2010; Driban et al., 2015). The number of female participants in our study was 1.9 times that of male participants (female: male = 52: 28), while participants older than 50, with a BMI of > 25, and smoking had a higher initial pain score without any statistical significance (data not shown). When we analyzed the risk factor-related effects of PFE administration, VAS and WOMAC scores of women revealed greater changes than those of men, even without any statistical significance except for WOMAC pain (Supplementary Table S2). Participants with overweight (BMI > 23), based on the Asia-Pacific obesity criteria, (World Health Organization. Regional Office For The Western Pacific, International Association For The Study Of Obesity and International Obesity Task Force World Health orgnization, 2002) showed lesser changes in the primary outcome than participants with normal BMI, especially in the total and 3-domain score of WOMAC ($p \leq 0.05$). Age did not affect the changes in the VAS and WOMAC scores (Supplementary Table S2).
The traditional diagnostic method for KOA was to classify the presence of osteophyte and narrowed joint space into four K-L grades in radiological examination (Jang et al., 2021). It is, however, reported that knee joint pain appears before radiographic evidence of KOA in $50\%$ of patients (Morozzi et al., 2007) and that no or doubtful stage of KOA can be abruptly progressed to a severe stage with osteophytes and joint space narrowing (KL 3-4) within one year (Driban et al., 2020). Therefore, the recent guidelines on KOA emphasize the prevention and treatment of KOA in the early stages of knee pain and joint function problems (Roos and Arden, 2016). As it becomes important to prevent and manage discomfort, such as knee pain from osteoarthritis, herbal medicines or functional foods with anti-inflammatory properties have begun to be developed as safe alternative choices. Tamarindus Indica seeds (Rao et al., 2019), *Curcuma longa* L. (Calderón-Pérez et al., 2021), Krill oil (Suzuki et al., 2016) and Rosehip (Ginnerup-Nielsen et al., 2015), which have antioxidant and anti-inflammatory properties, have been reported to improve knee joint pain associated with KOA in the initial state.
PF, an intervention in our study, is one of the common foods in East Asia and has been traditionally used as a medicinal herb (Ahmed, 2018). PF is composed of approximately 400 different bioactive compounds, including perillaldehyde (Ahmed and Tavaszi-Sarosi, 2019), anthocyanins (He et al., 2015), terpenoids (Akihisa et al., 2006), and coumarins (Liu et al., 2017) etc., and has demonstrated strong anti-inflammatory (Urushima et al., 2015), antioxidant (Assefa et al., 2018), neuroprotective (Senavong et al., 2016), anticancer (Kwak and Ju, 2015), and hepatoprotective effects (Yang et al., 2013) due to these compounds. Previous studies have demonstrated that PF provided anti-arthritic effects by reducing the arthritis score and neutrophil-to-lymphocyte ratio in a Balb/c collagen-induced arthritis model based on its anti-inflammatory and antioxidant effects (Jin et al., 2019). In this current study, although PFE improved the knee joint pain and function, it did not show a significant effect on the five inflammatory markers (COMP, COX-2, neutrophil and lymphocyte ratio, HS-CRP, and ESR), which are secondary outcome measurements (Table 3). There was no significant change probably because the inflammatory markers of the participants in this study were in the normal range at baseline.
Although several related studies have been conducted in vitro and in vivo, there has been no clinical evaluation of PF for relieving knee joint pain yet. The current RCT confirmed that PFE significantly mitigated knee pain and improved knee joint function. This study has few limitations and complementary points for further study. First, to reduce the placebo effect and confirm the changes of inflammation-related markers in PFE, it is necessary to include participants with KOA assessed by K-L grade 1 or higher in the inclusion criteria of participants. Second, investigation and analysis of disease duration and amount of exercise affecting the recovery of knee joint pain should be performed. Despite these limitations, this study is meaningful enough in that it is the first clinical trial to scientifically present the efficacy of PFE on pain reduction and functional improvement of the knee joint.
## 5 Conclusion
The current findings suggest that taking PFE for eight weeks is more effective than placebo on reducing knee joint pain assessed by VAS scores and improving knee joint function assessed by WOMAC scores in sub-healthy people with mild knee joint pain. There were no significant differences between PFE and placebo on inflammatory laboratory examinations. PFE accompanied no major concerns on safety assessment compared to placebo.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) of Daejeon Korean Medicine Hospital of Daejeon University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization and designed the study: E-JL, S-KK, M-HP, and K-HK. Data curation and analysis: J-ML, S-WK, N-HK, and S-YK. Funding acquisition and resources: S-KK, M-HP, and K-HK. Investigation and supervision: E-JL, I-CJ, and C-GS. Software and Methodology: J-ML, S-WK, N-HK, M-SO, S-YK, I-CJ, and E-JL. Validation: M-SO, E-JL, and C-GS. Writing—original draft and visualization: N-HK, and E-JL. Writing—review and editing: E-JL, and C-GS. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
S-KK, M-HP, and K-HK were employed by the company SFC Bio Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1114410/full#supplementary-material
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|
---
title: 'Investigation of risk factors associated with impaired glucose regulation:
Using the momentum equation to assess the impact of risk factors on community residents'
authors:
- Mengqian Guo
- Zhen Wang
- Shumei Wang
- Jinju Wang
- Qiang Jiang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10043464
doi: 10.3389/fendo.2023.1145847
license: CC BY 4.0
---
# Investigation of risk factors associated with impaired glucose regulation: Using the momentum equation to assess the impact of risk factors on community residents
## Abstract
### Objective
To identify risk factors for impaired glucose regulation (IGR) and assess their impact on community residents, this study used a questionnaire to conduct cross-sectional surveys and analysis.
### Methods
Overall, 774 residents of an urban community in northern China (Jian city) participated in this study. Trained investigators conducted surveys using questionnaires. Based on their medical history, respondents were divided into three glucose status groups as follows: normal (NGT), IGR, and diabetes mellitus (DM). Statistical analysis of survey data was performed using SPSS v. 22.0.
### Results
Age, hypertension, family history of diabetes (FHD), dyslipidemia, obesity, and cardiovascular and cerebral disease (CVD) were positively correlated with IGR in men and women. IGR was negatively correlated with a sedentary lifestyle in men and positively correlated with being overweight in women. The number of type 2 diabetes mellitus (T2D) risk factors per subject was positively correlated with age in the NGT group. Glucose status deteriorated with increasing age and the number of risk factors. FHD was the strongest risk factor in both men and women.
### Conclusions
Prevention of IGR includes weight control, physical activity, and prevention of hypertension and dyslipidemia, especially in subjects with FHD.
## Introduction
Type 2 diabetes mellitus (T2D) increases the risk of microvascular and macrovascular complications, and as a result, puts a great economic burden on patients and society. T2D has become an important public health issue worldwide, especially in developing countries [1, 2]. Previous studies have indicated that interventions in populations at high risk of developing T2D can effectively prevent diabetes progression (3–8). Unfortunately, despite these efforts, investigations in recent decades have shown that T2D prevalence has gradually increased, as has the prevalence of prediabetes, including impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) (9–13). In China, the incidence of T2D and prediabetes among adults in 2010 was $11.6\%$ and $50.1\%$, respectively [12]. A population-based cross-sectional survey published in 2017 [14] used oral glucose tolerance test (OGTT) data from 43,846 adults aged 20 years or older from the 2007-2008 Chinese Diabetes and Metabolic Disorder Study and resulted in 2,801 newly diagnosed cases of type 2 diabetes. Of these newly diagnosed patients, 654 ($23.3\%$) were aged <43 years and thus had early-onset DM. Early-onset diabetes in China accounts for a higher proportion of new-onset diabetes, suggesting that diabetes in young people has reached pandemic levels. The reasons underlying for this increase are complicated. T2D is the result of interactions between multiple factors including genetic, environmental, lifestyle, nutrition, economic and social factors [15]. Most previous studies on T2D prevention have focused on IGT patients, in whom regulation glucose was abnormal at the time of the study [3, 4, 16]. Some studies have shown that after a 6-year lifestyle intervention, $73\%$ of people with IGT developed T2D 23 years later [17].
In recent years, IGR has been established as an independent risk factor for acute coronary events [18]. Preventive measures from clinical trials cannot eliminate the risk factors or stop the onset and progression of T2D in IGR patients [2, 15]. Individuals with normal glucose regulation may also be at risk for T2D [19].
Since IGR is the first stage of T2D, prevention of IGR should be the first stage of T2D prevention, and special attention should be given to risk factors in the general population before glucose regulation in these individuals becomes abnormal. In this study, we conducted a cross-sectional survey to assess the prevalence and ranking of risk factors for IGR and T2D in people with NGR.
There are many risk factors that increase the risk of IGR. Because their prevalence in the community varies, so does their impact on community populations. To evaluate the impact of a risk factor on the population, it is better to consider both the relevance of the risk factor and its incidence in the population. In our study, we used the product of a risk factor’s odds ratio (OR) and its prevalence in the community which we termed “risk momentum” (RM), to assess the impact of each risk factor on the community.
## Ethical approval
Informed written consent was obtained from all participants prior to enrolment.
## Study population
The study population was recruited from an urban residential community (Jian city) in China, from December 2013 to June 2014. The sampling frame consisted of the population living in the community ≥ 2 years and aged ≥15 years. Simple randomization was performed to select participants. A total of 774 subjects participated in the study.
## Questionnaire and measurements
The questionnaire on risk factors for T2D (Table 1) was designed based on the Guidelines for Prevention and Treatment of Type 2 Diabetes in China (2010 Edition) [20]. Trained investigators performed the survey using a published questionnaire (Table 1). Subjects were divided into three glucose regulation groups according to their medical history and health records: the diabetic group (the DM group=0, fasting plasma glucose (FPG) ≥7.0 mmol/L and/or 2 hours postprandial blood glucose (P2hBG) ≥11.1 mmol/L, or diagnosed as having T2D by a doctor); the impaired glucose regulation group (the IGR group=1, 6.1 mmol/L≤ FPG <7.0 mmol/L and/or 7.8 mmol/L≤ P2hBG <11.1 mmol/L, or diagnosed as having IGR by a doctor); and the normal group (the NGT group=2, FPG <6.1 mmol/L and P2HBG <7.8 mmol/L). Participants were required to answer the questionnaire based on their latest health reports.
**Table 1**
| 1. History of impaired glucose regulation (Glucose status) (diabetes = 2, yes = 1, no = 0) |
| --- |
| 2. Age ≥ 45 years (yes = 1, no = 0) |
| 3. Overweight status and obesity (body mass index (BMI) ≥28 kg/m2 was regarded as obese, BMI ≥24 kg/m2 was regarded as overweight, and BMI 18–24 kg/m2 was regarded as normal) (obesity = 2, overweight = 1, normal = 0) |
| 4. History of T2D in the immediate family (FHD) (yes = 1, no = 0) |
| 5. History of macrosomia (infant birth weight ≥4 kg) (yes = 1, no = 0) |
| 6. History of gestational diabetes mellitus (GDM) (yes = 1, no = 0) |
| 7. History of hypertension (HP, systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or current antihypertensive treatment) (yes = 1, no = 0) |
| 8. History of dyslipidemia or current lipid-lowering therapy (yes = 1, no = 0) |
| 9. History of cardiovascular and cerebral disease (CVD) (yes = 1, no = 0); |
| 10. History of glucocorticoid-induced diabetes (GID) (yes = 1, no = 0) |
| 11. History of polycystic ovary syndrome (PCOS) (yes = 1, no = 0) |
| 12. Severe mental disease/long-term use of antidepressants (SMLUA) (yes = 1, no = 0) |
| 13. Sedentary lifestyle, defined as no or very little physical activity during work, housework, transportation, and leisure time. The standard was medium-intensity physical activity of <30 min per day most days of the week (yes = 1, no = 0). |
Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (m). Blood pressure was measured three times in the sitting position with at least 5 minutes of rest before the measurement, and their average was used in the analysis. Past medical history (hypertension, HP; gestational diabetes mellitus, GDM; dyslipidemia; cardiovascular and cerebral disease, CVD; glucocorticoid-induced diabetes, GID; polycystic ovary syndrome, PCOS; and severe mental illness/long-term use of antidepressants, SMLUA) was used based on the previous diagnosis.
## Statistical analysis
Statistical analyses were performed using SPSS v. 22.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were summarized and presented as the mean ± standard deviation (SD), and nonparametric variables were presented as numbers and percentages. Only risk factors with an incidence of ≥$10\%$ were included in the correlation analysis. Bivariate correlation analyses were performed to identify factors associated with IGR. Factors identified as significant from bivariate analyses were included in a binary logistic regression analysis, and odd ratios with $95\%$ confidence intervals and p-values were reported. A bivariate analysis was used to analyze the correlation between the following: the number of T2D risk factors (excluding age) and age (Pearson test); the number of T2D risk factors (excluding age) and glucose status (Spearman test); and glucose status and age (Spearman test). The significance level was set at $p \leq 0.05$, and all analyses were two-sided. The product of a risk factor’s OR value and its prevalence in the community was calculated as RM.
## General information
Of the 774 participants, the NGT group comprised 208 men (aged 47.13 ± 15.61 years) and 381 women (aged 45.92 ± 16.13 years), the IGR group comprised 35 men (aged 60.89 ± 11.44 years) and 48 women (aged 59.27 ± 13.66 years), and the DM group comprised 41 men (aged 62.29 ± 10.08 years) and 61 women (aged 64.03 ± 8.94 years). The prevalence of T2D was $14.4\%$ in men and $12.4\%$ in women. The prevalence of IGR was $12.3\%$ in men and $9.8\%$ in women.
## Prevalence of risk factors
The prevalence of T2D risk factors in different groups is shown in Table 2. The prevalence of T2D risk factors was higher in the DM and IGR groups than in the NGT group. The prevalence of overweight was the highest among all the factors in both men and women.
**Table 2**
| T2D Risk Factor | DM | DM.1 | IGR | IGR.1 | NGT | NGT.1 |
| --- | --- | --- | --- | --- | --- | --- |
| T2D Risk Factor | MAN (%) | WOMAN (%) | MAN (%) | WOMAN (%) | MAN (%) | WOMAN (%) |
| Overweight | 46.3 | 45.9 | 48.6 | 47.9 | 41.8*† | 33.3*† |
| Sedentary Lifestyle | 29.3 | 39.3 | 20.0 | 31.3 | 33.7 | 31.5 |
| Hypertension | 48.8 | 75.4 | 60.0 | 56.3 | 24.5*† | 20.5*† |
| FHD | 31.7 | 49.2 | 48.6 | 43.8 | 20.7* | 16.3*† |
| Dyslipidemia | 61.0 | 49.2 | 51.4 | 33.3 | 17.8*† | 11.8*† |
| Obesity | 26.8 | 34.4 | 22.9 | 29.2 | 15.4*† | 14.7*† |
| CVD | 39.0 | 59.0 | 42.9 | 47.9 | 13.5*† | 17.3*† |
| Macrosomia | 9.8 | 21.3 | 11.4 | 6.3 | 7.2 | 11.3 |
| PCOS | 0.0 | 4.9 | 0.0 | 0.0 | 0.0 | 0.8 |
| GDM | 0.0 | 1.6 | 0.0 | 4.2 | 0.0 | 0.3 |
| GID | 2.4 | 1.6 | 0.0 | 4.2 | 0.0 | 0.5 |
| SMLUA | 0.0 | 4.9 | 0.0 | 2.1 | 0.0 | 0.3 |
## Number of risk factors in each group
Excluding age as a factor, the number of T2D risk factors per person in each group was as follows: DM men, 2.85 ± 1.49 factors and DM women, 3.87 ± 1.52 factors; IGR men, 3.71 ± 1.27 factors and IGR women, 3.81 ± 1.42 factors; NGT men, 1.62 ± 1.37 factors and NGT women, 1.56 ± 1.32 factors. A significant difference in the number of T2D risk factors per person was observed between the NGT group and the other two groups ($p \leq 0.01$), but there was no significant difference between the IGR and DM groups ($p \leq 0.05$). A bivariate Spearman correlation analysis showed that glucose status was negatively correlated with the number of T2D risk factors (excluding age) per person (men, R = −0.435, $p \leq 0.01$; women, R = −0.461, $p \leq 0.01$).
## Age
A bivariate Spearman correlation analysis showed that glucose status was negatively correlated with age (R= −0.412, $p \leq 0.01$). The number of T2D risk factors excluding age showed a positive correlation with age in the NGT group using the bivariate Pearson correlation analysis. In the NGT group, Pearson’s correlation coefficient was 0.32 ($p \leq 0.01$) for men and 0.487 ($p \leq 0.01$) for women. In the IGR group, Pearson’s correlation coefficient was −0.128 ($p \leq 0.05$) for men and 0.208 ($p \leq 0.05$) for women. In the DM group, Pearson’s correlation coefficient was 0.249 ($p \leq 0.05$) for men and 0.184 ($p \leq 0.05$) for women.
## Risk factors for IGR
All variables with a prevalence of $10\%$ or greater were subjected to bivariate correlation analysis to assess the strength of their association with IGR. Eight variables, including FHD, dyslipidemia, hypertension, CVD, overweight and obesity, age, macrosomia, and sedentary lifestyle, were added to a binary logistic regression model for further analysis (Table 3). A sedentary lifestyle showed a negative association with IGR, and overweight was not correlated with IGR in men, in contrast with these variables in women. FHD was the strongest risk factor for IGR in both men and women.
**Table 3**
| Unnamed: 0 | Men | Men.1 | Men.2 | Unnamed: 4 | Women | Women.1 | Women.2 | Women.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variable | OR | 95% of C.I. | p value | Product (OR-1)*Prevalence | OR | 95% of C.I. | p value | Product(OR-1)*Prevalence |
| Age | 1.052 | 1.047-1.057 | 0.000 | | 1.043 | 1.038-1.048 | 0.000 | |
| Overweight | 1.046 | 0.922-1.186 | 0.484 | 1.92 | 2.140 | 1.921-2.384 | 0.000 | 37.96 |
| Sedentary lifestyle | 0.538 | 0.471-0.614 | 0.000 | -15.57 | 1.515 | 1.374-1.670 | 0.000 | 16.23 |
| Hypertension | 1.848 | 1.650-2.070 | 0.000 | 20.78 | 1.552 | 1.406-1.713 | 0.000 | 11.32 |
| FHD | 3.342 | 2.982-3.746 | 0.000 | 48.48 | 3.553 | 3.243-3.893 | 0.000 | 41.61 |
| Dyslipidemia | 2.949 | 2.631-3.305 | 0.000 | 34.70 | 1.620 | 1.473-1.781 | 0.000 | 7.32 |
| Obesity | 1.374 | 1.170-1.613 | 0.000 | 5.77 | 2.033 | 1.803-2.293 | 0.000 | 15.19 |
| CVD | 1.569 | 1.388-1.774 | 0.000 | 7.68 | 1.415 | 1.283-1.561 | 0.000 | 7.18 |
| Constant | 0.003 | | 0.000 | | 0.004 | | 0.000 | |
## Risk momentum: The product of (OR-1) * the prevalence of risk factors for IGR
In men, FHD had the largest RM, followed by hyperlipidemia and hypertension. In women, FHD also had the largest RM, followed by an overweight, sedentary lifestyle and obesity (Table 3; Figure 1).
**Figure 1:** *The relationship between the prevalence of risk factors and IGR incidence in men (Ain diagram) and women (top diagram).*
## Discussion
Our survey revealed that age, hypertension, FHD, dyslipidemia, obesity, and CVD were positively correlated with IGR, and a sedentary lifestyle was negatively correlated with IGR in men. In women, age, overweight, sedentary lifestyle, hypertension, FHD, dyslipidemia, obesity, and CVD were positively correlated with IGR. FHD had the strongest correlation with IGR in both men and women. After excluding age as a factor, the number of T2D risk factors was positively correlated with age in the NGT group. Glucose status deteriorated with increasing age and risk factors in the NGT group. FHD had the largest RM in both men and women.
We used IGR prevention as a starting point for T2D prevention. Therefore, we assessed the prevalence of T2D-related risk factors in community residents and identified risk factors associated with IGR to inform IGR prevention strategies. Our data are also consistent with earlier observations suggesting that FHD, obesity, hypertension, dyslipidemia, sedentary lifestyle, and age are correlated with IGR [12, 21].
In our study, FHD had the strongest correlation with IGR both in men and women. The correlation of genetic factors with T2D is known [22, 23]. Research has shown that $40\%$ of first-degree relatives of T2D patients develop diabetes, compared with only $6\%$ in the general population [24]. Furthermore, monozygotic twins have higher concordance rates ($96\%$) than dizygotic twins in some but not all twin studies [24, 25]. The effect of family history may include not only genetic factors but also family aggregation. In addition to genetic factors, family members have similar lifestyles, which could lead to similar T2D risks. The results of a meta-analysis suggest that spousal diabetes can be a diabetc risk factor [26]. Although the incidence of FHD was not the highest, the RM of FHD was the largest in both men and women. This finding suggests that FHD might be the most dangerous risk factor for IGR in the community.
We found that being overweight was the most prevalent risk factor in the NGT group. When the prevalence of obesity was considered in addition to the prevalence of overweight, $50\%$ of NGT subjects were overweight. Obesity was significantly correlated with IGR in men and women, but overweight was correlated with IGR only in women. The RM was 37.96 for overweight and 15.19 for obesity in women. This suggests that the criteria for overweight should be defined separately for each gender. Other studies have shown that obesity/overweight plays important role in the progression of DM through adipose tissue factors (27–29). Being overweight (BMI 25-29.9 kg/m2) is significantly correlated with prediabetes [21]. A high BMI also correlates with other T2D risk factors [28].
Sedentary lifestyle was the second most prevalent risk factor in the NGT group for both men and women. Other studies have shown sedentary lifestyle to be correlated with diabetes [30]. In an investigation on Australian males aged 45 years and older, George ES found that time spent sitting was associated with diabetes, independent of BMI [31]. In the present investigation, we found the prevalence of a sedentary lifestyle was not significantly different among the three glucose status groups. Contrary to previous studies [32], we found sedentary lifestyle was positively correlated with IGR in men. This finding might be because doctors generally advise people with IGR to manage their diet and increase physical activity, which could lead to people with IGR performing more physical activity than those without IGR. Moreover, it was well documented that people with IGR can manage the prevalence of T2D via diet modification [33]. Our findings also indicated that sedentary lifestyles are prevalent throughout the community. The RM of a sedentary lifestyle was the third largest in women. Thus, addressing and reversing this unhealthy lifestyle trend is important.
In this study, the number of T2D risk factors per person was positively correlated with age, and glucose regulation deteriorated with age. Sandovici et al. [ 34] found changes in gene expression that were linked to T2D in rats. In a 10-year longitudinal study of 10,000 adults in Korea, Ohn et al. [ 19], found that all participants developed some degree of insulin resistance with age. In the present study, we found that once abnormalities of glucose regulation were present, regardless of IGR or DM, the number of risk factors lost its relevance with age, suggesting that prevention of IGR should begin at a young age.
Dyslipidemia, hypertension and CVD were significantly correlated with IGR in our study. Dyslipidemia had the second largest RM, and hypertension had the third largest RM in men, whereas these factors had the fifth and sixth largest RMs in women. Diabetes and IGR are often accompanied by hypertension, dyslipidemia, and CVD. Hypertension, dyslipidemia and, hyperglycemia are important risk factors for mortality and disease associated with CVD [28, 31, 35]. CVD can be considered an indicator of IGR as well as a result of IGR. However, no clear evidence exists as to whether there is a causal relationship between hyperglycemia and dyslipidemia or hypertension, and the underlying connections remain to be determined. Some lifestyle intervention studies have found that T2D incidence decreased with decreasing BMI, blood pressure, and lipid levels [3, 7, 9].
Notably, in our survey, the prevalence of diabetes and prediabetes in the community differed from the latest national survey data (DM, $10.7\%$; pre-DM, $35.7\%$) [36]. As the incidence of risk factors in different regions may differ, strategies and priorities for the prevention of diabetes and IGR may vary from region to region. Because this was a cross-sectional study, the correlation analysis could not fully account for the long-term effects of risk factors on human physiology. A large longitudinal study is required to validate our findings. In this study, self-reported health information was accepted. Because most Chinese adults conduct general medical examinations only annually, some medical information might have changed between the most recent health examinations and the time when they participated in this study. For instance, patients with latent IGR or T2D might have been missed, which could reduce the effectiveness of our statistical analysis.
## Conclusion
In conclusion, among the analyzed risk factors FHD had the strongest correlation with IGR. Overweight and obesity are the most common risk factors for IGR, and FHD has the largest RM. IGR should be addressed at a young age, especially in those with FHD. The best means of preventing IGR include weight control, prevention of hypertension and dyslipidemia, and consistent physical activity.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Medical Ethics Committee of Jinan Central Hospital Affiliated to Shandong University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MG: Concept. ZW: Review of data. SW: Correction and revision. JW: Revision of papers. QJ: Conceptualize and writing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Genomic sequencing has a high diagnostic yield in children with congenital
anomalies of the heart and urinary system
authors:
- Erika T. Allred
- Elliot A. Perens
- Nicole G. Coufal
- Erica Sanford Kobayashi
- Stephen F. Kingsmore
- David P. Dimmock
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10043482
doi: 10.3389/fped.2023.1157630
license: CC BY 4.0
---
# Genomic sequencing has a high diagnostic yield in children with congenital anomalies of the heart and urinary system
## Abstract
### Background
Congenital heart defects (CHD) and congenital anomalies of the kidney and urinary tract (CAKUT) account for significant morbidity and mortality in childhood. Dozens of monogenic causes of anomalies in each organ system have been identified. However, even though $30\%$ of CHD patients also have a CAKUT and both organs arise from the lateral mesoderm, there is sparse overlap of the genes implicated in the congenital anomalies for these organ systems. We sought to determine whether patients with both CAKUT and CHD have a monogenic etiology, with the long-term goal of guiding future diagnostic work up and improving outcomes.
### Methods
Retrospective review of electronic medical records (EMR), identifying patients admitted to Rady Children's Hospital between January 2015 and July 2020 with both CAKUT and CHD who underwent either whole exome sequencing (WES) or whole genome sequencing (WGS). Data collected included demographics, presenting phenotype, genetic results, and mother's pregnancy history. WGS data was reanalyzed with a specific focus on the CAKUT and CHD phenotype. Genetic results were reviewed to identify causative, candidate, and novel genes for the CAKUT and CHD phenotype. Associated additional structural malformations were identified and categorized.
### Results
Thirty-two patients were identified. Eight patients had causative variants for the CAKUT/CHD phenotype, three patients had candidate variants, and three patients had potential novel variants. Five patients had variants in genes not associated with the CAKUT/CHD phenotype, and 13 patients had no variant identified. Of these, eight patients were identified as having possible alternative causes for their CHD/CAKUT phenotype. Eighty-eight percent of all CAKUT/CHD patients had at least one additional organ system with a structural malformation.
### Conclusions
Overall, our study demonstrated a high rate of monogenic etiologies in hospitalized patients with both CHD and CAKUT, with a diagnostic rate of $44\%$. Thus, physicians should have a high suspicion for genetic disease in this population. Together, these data provide valuable information on how to approach acutely ill patients with CAKUT and CHD, including guiding diagnostic work up for associated phenotypes, as well as novel insights into the genetics of CAKUT and CHD overlap syndromes in hospitalized children.
## Introduction
Congenital birth defects frequently have an underlying molecular etiology. For example, there are over 40 genes each that are associated with congenital anomalies of the kidney and urinary tract (CAKUT) and congenital heart defects (CHD) [1, 2]. Causative molecular variants that span these two systems are less well characterized, and we sought to better define these.
CAKUT represent a heterogeneous group of disorders ranging from mild urinary tract dilation to bilateral renal agenesis [1]. Kidney malformations are one of the most common congenital anomalies, representing $20\%$–$30\%$ of all prenatally detected anomalies [3, 4]. CAKUT are known to carry significant morbidity in the pediatric population, accounting for $45\%$ of chronic kidney disease (CKD) and about $30\%$ of end stage kidney disease (ESKD) [5]. Over 40 single-gene defects have been implicated in CAKUT, with a Mendelian molecular etiology detected in up to $26\%$ of all cases [1, 6]. It is estimated that hundreds of additional monogenic defects have yet to be discovered in CAKUT, with identification complicated by incomplete penetrance and variable expressivity, along with suspected contributions from epigenetic and environmental factors [6, 7].
Additional causes of CAKUT include many well-known genetic syndromes which present with extra-renal manifestations, such as Fraser syndrome, CHARGE syndrome, and DiGeorge [8]. Interestingly, each of these syndromes include CHD in their phenotypes. CHD is the primary cause of congenital anomalies in children, representing almost one third of all congenital anomalies, and is the most common cause of death before the age of one year [9, 10]. CHD also has a strong genetic basis, with over 40 monogenic gene defects identified [2].
Several studies have highlighted a strong clinical correlation between non-syndromic CHD and CAKUT, with $20\%$–$30\%$ of all patients with CHD also found to have CAKUT [8]. These findings suggest a genetic overlap between the etiology of both anomalies, which has been supported by several mouse models demonstrating a single gene to be responsible for both CHD and CAKUT [7]. The monogenic overlap between CHD and CAKUT has yet to be fully evaluated in human studies, with only sporadic case reports describing single gene causes of both [11, 12]. In addition, the current known monogenic causes of CAKUT do not overlap with those of CHD, despite the well-known clinical overlap and similar embryologic origin of cardiac and kidney development [13, 14]. However, there are no human studies specifically evaluating the genetics of individuals with both CAKUT and CHD, and thus the contribution of monogenic defects to this population is still unknown.
There is a significant clinical need to further identify and characterize the genetic underpinnings and presenting phenotypes of patients with both CAKUT and CHD due to the well-known interdependent relationship of the two organ systems. Cardiorenal syndrome encompasses a spectrum of disorders in which dysfunction of one organ system induces dysfunction in the other [15]. This syndrome is a significant cause of morbidity and mortality in hospitalized patients, with both the Studies of Left Ventricular Dysfunction (SOLVD) Prevention Trial and Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) study noting almost a doubling in the mortality rate of heart failure patients if they experienced a decline in kidney function [16, 17]. This impact is magnified and more likely to occur in patients who already possess a congenital anomaly in either organ system.
Due to the pervasiveness and significant disease burden of CAKUT and CHD in the pediatric population, we performed a descriptive and analytic cross-sectional evaluation of the prevalence of patients with both CAKUT and CHD. We analyzed patients at a single-center tertiary hospital who had diagnostic whole exome sequencing (WES) and/or whole genome sequencing (WGS). Using this information, we sought to further elucidate the phenotypic spectrum and candidate genetic underpinnings of patients affected by both CAKUT and CHD. We anticipate that this information will improve patient outcomes by increasing providers' diagnostic and prognostic capabilities, with future aspirations to include implementation of prophylactic measures and the reduction of unnecessary interventions.
## Study design
We performed a retrospective chart review of electronic medical records (EMR) of all patients who had received diagnostic WES and/or WGS and had ICD10 diagnosis codes of Q20–28 (congenital malformations of cardiac chambers and connections) or Q60–69 (included congenital malformations of the urinary system) that were cared for at Rady Children's Hospital between January 2015 and July 2020. Available individual patient imaging studies were then evaluated to identify patients with both CAKUT and CHD. Imaging studies reviewed included any or all of the following: kidney and bladder ultrasounds (RUS), vesicoureteral cystograms (VCUG), dimercaptosuccinic acid (DMSA) scans, mercaptoacetyltriglycine (MAG3) scans, computed tomography (CT) scans of the abdomen, magnetic resonance imaging (MRI) of the abdomen, CT scans of the heart, MRIs of the heart, and echocardiograms (ECHO). Patients were excluded if their imaging failed to show both CAKUT and CHD, if the EMR represented the parent of a child under investigation, if cardiac imaging revealed only a patent foramen ovale (PFO) or patent ductus arteriosus (PDA) that resolved by 12 months of age without surgical intervention, or if kidney imaging was only notable for hydronephrosis that resolved within the first year of life without intervention. Data collected included: demographic data (e.g., sex, race/ethnicity, date of birth), diagnoses, patient age at time of WGS/WES sequencing, additional imaging, presenting phenotype, maternal pregnancy history, human phenotype ontology (HPO) terms, and all genome wide sequencing (GWS) results.
This study is limited due to its analysis of a single center with a small population. In addition, results were interpreted with the recognition that genetic testing was confined to WGS and WES only. These limitations will be explored further in the discussion section of this paper.
## Diagnostic WGS and WES analysis and interpretation
Clinical WES and WGS were performed in laboratories accredited by the College of American Pathologists and certified through the Clinical Laboratory Improvement Amendments. The analysis and interpretation protocol were adapted from the NSIGHT2 study [18].
WGS results were reinterpreted for this study using Opal Clinical (Fabric Genomics). Genes associated with CAKUT and CHD were identified using the Phenolyzer algorithm [19]. Opal then re-annotated variants using the resultant gene panel for the Variant Annotation, Analysis & Search Tool (VAAST) variant prioritizer Phevor algorithm, re-ranking with the specific Human Phenotype Ontology (HPO) terms, “abnormal heart morphology” and “abnormal renal morphology”. *Automatically* generated, ranked results were manually re-interpreted through iterative Opal searches and filters.
## Results
We identified 323 cases with the defined ICD10 diagnosis codes corresponding to congenital malformations of cardiac chambers and connections or the urinary system and diagnostic WGS or WES results (Figure 1). Of those cases, 105 were excluded after being identified as isolated CHD, 24 were excluded for isolated CAKUT, 11 were parental charts of children under investigation, and 105 had neither CAKUT nor CHD as defined in the current study. Twenty-three percent of all cases with CHD were also found to have CAKUT. In total, we identified 32 cases with CAKUT and CHD who had received diagnostic WGS or WES. Demographic information is detailed in Table 1. Fourteen cases underwent WES and 18 cases underwent WGS. Due to the limited number of overall cases, WGS and WES results were interpreted as one cohort. We identified eight cases with pathogenic or likely pathogenic variants in genes known to cause CAKUT and CHD. Three cases had four variants of uncertain significance (VUS) suspected to be disease causing in genes known to cause CAKUT and CHD. Three cases had four variants (2 classified as pathogenic, 2 VUS) in genes not previously associated with CAKUT and CHD. Five cases had eight reported variants (some cases had more than one variant) in genes not previously associated with CAKUT and CHD and not suspected to be the cause of the CAKUT/CHD phenotype due to a lack of supporting literature. Thirteen cases had no variants identified via focused reinterpretation of WGS or WES for genes and disorders associated with CAKUT or CHD. Of the 18 cases without genetic results related to their CAKUT/CHD phenotype, four were infants of diabetic mothers (IDM), three patients were found to have VACTERL association, and one case had both a diabetic mother and VACTERL association. Therefore, eight out of the 18 cases without a causative molecular diagnosis ($44\%$) were suspected of having an identifiable, putative etiology for their CAKUT/CHD phenotype. All causative, candidate, and novel variants were found in genes associated with genetic syndromes, with the exception of PLD1, which is associated with CHD alone (Developmental cardiac valvular defect, MIM 212093). Please see Table 2 for a detailed evaluation of all identified variants.
**Figure 1:** *Flow diagram of patient inclusion and results: Infant of diabetic mother (IDM), Congenital heart defect (CHD), Congenital anomaly of kidney and urinary tract system (CAKUT).* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 Novel, putative CAKUT/CHD gene associations were substantiated by evidence demonstrating presence of the protein product in both organ systems or research supporting physiologic or structural abnormalities in one organ system but not the other (Table 3). For example, VPS4A has been associated with cerebellar hypoplasia, cataracts, impaired intellectual development, congenital microcephaly, dystonia, dyserythropoietic anemia, and growth retardation (CIMDAG syndrome, MIM 619273) and with recurrent urinary tract infections, which frequently indicates an underlying structural anomaly (e.g., vesicoureteral reflux or obstruction) [19]. However, there were no reports of CHD associated with this disorder.
**Table 3**
| Gene | Variants | Syndrome | Prior Literature |
| --- | --- | --- | --- |
| PCDH15 | c.733C > Tp.Arg245Ter | Usher Syndrome | PCDH15 -CD2 expression has been found in heart, kidney,thymus, spleen, testis, retina and cochlea. |
| VPS4A | c.298G > Ap.Glu100Lys | CIMDAG Syndrome | Found in individuals with frequent UTIs.No reported CHD. |
| LRP4 | 1. c.4255A > Gp.Met1419Val2. c.2837C > Gp.Pro946Arg | Cenani-Lenz Syndactyly Syndrome | Causes renal agenesis.No reported CHD |
In total, GWS identified a suspected genetic etiology in $44\%$ of cases with CAKUT/CHD phenotypes, with another $13\%$ identified as having a likely environmental etiology, and an additional $13\%$ with VACTERL syndrome. In total, $70\%$ of cases were determined to have a suspected etiology for their presentation.
The most common CAKUT phenotype identified was hydronephrosis, seen in $\frac{22}{32}$ ($69\%$) of all cases. The most common CHD phenotype identified was septal defects, identified in $\frac{22}{32}$ ($69\%$) of all cases. Additional phenotypic information can be found in Table 4.
**Table 4**
| Unnamed: 0 | N | % |
| --- | --- | --- |
| CAKUT Phenotype: | CAKUT Phenotype: | CAKUT Phenotype: |
| Hydronephrosis | 22 | 69 |
| Vesicoureteral reflux | 5 | 16 |
| Renal Agenesis | 3 | 9 |
| Hypo/Dysplasia | 3 | 9 |
| Ectopy | 1 | 3 |
| CHD Phenotype: | CHD Phenotype: | CHD Phenotype: |
| Sepal Defects | 22 | 69 |
| Valvular Anomalies | 9 | 28 |
| Dilated Heart Chambers | 5 | 16 |
| Coarctation of Aorta | 4 | 13 |
| Tetralogy of Fallot | 2 | 6 |
| Hypoplastic Left Heart | 1 | 3 |
| Transposition of Great Arteries | 1 | 3 |
We also identified additional structural anomalies in other organ systems in 28 out of the 32 cases ($88\%$). Additional organ systems were classified based on ICD10 codes and included: respiratory, eye/ear/face/neck, cleft lip and palate, nervous system, musculoskeletal (msk), digestive, genital, and other circulatory. The most common additional organ systems affected were the nervous system ($53\%$ of cases) and musculoskeletal systems ($44\%$ of cases). Structural brain anomalies were the most common anomalies identified in the nervous system. Pectus anomalies and scoliosis were the most common anomalies identified in the musculoskeletal system. Additional organ system involvement is detailed in Table 5.
**Table 5**
| Pt ID | Gene | Respiratory | Eye, ear, face, neck | Cleft lip & palate | Nervous System | MSK | Digestive | Genital | Other Circulatory |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1007.0 | CHD7 | X | X | | | | | | |
| 1005.0 | EFTUD2 | | X | X | X | X | | | |
| 1008.0 | KAT6B | | X | | X | X | X | | |
| 1002.0 | ADNP | X | | | X | X | | | |
| 1010.0 | FGF10 | X | | | | | | | |
| 1003.0 | NSD1 | | | | | X | | | |
| 1011.0 | GREB1L | | | | X | | | | |
| 2007.0 | ACTA2 | X | | | | | | | |
| 2001.0 | ATP6AP2 | | | | X | X | | X | |
| 1004.0 | PCHD15 | X | X | | X | X | | | |
| 1006.0 | VPS4A | | | | X | | | | |
| 2006.0 | LRP4 | | | | X | | | | |
| 3011.0 | | | X | | X | X | | | |
| 3012.0 | | | | | X | X | X | | |
| 2003.0 | | | | | X | X | | | |
| 3004.0 | | | | | X | | X | X | |
| 2002.0 | GCK POLG2 | | | | | X | | | |
| 3014.0 | | | | | X | | | X | |
| 2005.0 | KRT25 | | | | X | | | | |
| 2004.0 | OSLB1 | | | | X | X | | | |
| 1001.0 | FLG | | | | | X | | X | |
| 3006.0 | | X | | | | | | | X |
| 3001.0 | | | | | X | X | | | X |
| 3002.0 | | | | | | X | | X | |
| 3003.0 | | | | | X | | | | |
| 3008.0 | | | | | X | | | | |
| 3015.0 | | | | X | | | X | | |
| 3007.0 | | | | | | | | X | |
| | | 6/32 (19%) | 5/32 (16%) | 2/32 (6%) | 17/32 (53%) | 14/32 (44%) | 4/32 (13%) | 6/32 (19%) | 2/32 (6%) |
Patients with only CAKUT and CHD and no other organ system involvement included the following case ID numbers: 3005, 3010, 1009, and 3013. Two of these cases did not have an identifiable etiology for their CAKUT and CHD phenotype. ID 1009 was found to have trisomy 21 and ID 3,013 had two suspected disease-causing variants in the PLD1 gene, the only gene in this group without an associated syndromic phenotype.
## Rare genes identified
In this study of a small, but ancestrally diverse cohort of 32 cases diagnosed with both CAKUT and CHD, we identified molecular etiologies in 14 cases ($44\%$). This yield is higher than that observed for patients with either CAKUT or CHD alone, suggesting that a genetic etiology should be highly suspected and evaluated for in patients with both CAKUT and CHD. We identified 12 distinct monogenic disorders, representing $86\%$ of all genetic diagnoses (Table 2). This yield is significantly higher than either CHD or CAKUT alone, for which most identified genetic causes are due to copy number variants (CNVs) or chromosomal abnormalities [13, 14]. Recently, however, a paper published by Sweeney et al. in 2021 reports point mutations as the most common genetic etiology for a small cohort of CHD cases with WGS [20]. It is feasible that the differences in genetic defects identified in our study and Sweeney et al. vs. historical literature are due to the technologic limits of genetic testing prior to the advent of WES and WGS. With increasing use of more comprehensive diagnostic technology, it is likely our identification of common genetic defects will continue to expand. These results suggest that monogenic causes of both CAKUT and CHD are much more common than previously expected and may differ in genetic etiology from CHD and CAKUT alone. In addition, these results suggest that genetic testing by GWS should be considered more frequently in the diagnostic evaluation of children with CHD, CAKUT, or both of unknown etiology.
Our study identified 15 monogenic causes of both CAKUT and CHD that would not have been identified by standard gene panels for CHD or CAKUT alone. Of the four common gene panels used for etiologic assessment of CAKUT, only 1–2 of our 14 patients ($7\%$–$14\%$) would have received a diagnosis, depending on the panel used. Genes identified in our patient population and included on CAKUT gene panels were GREB1l and LRP4. CHD gene panels were more likely to identify a molecular etiology, with most expanded panels (> 50 genes) expected to yield a diagnosis in 2–5 of our 14 patients ($14\%$–$36\%$), depending on the panel used. CHD-associated genes identified in our patient population and included in gene panels included: ACTA2, CHD7, EFTUD2, NSD1 and PLD1. Overall, these findings demonstrate substantial genetic heterogeneity in this population, and importantly they suggest a lack of significant overlap with genes commonly identified in isolated cases of CHD or CAKUT. Our results also suggest that these patients are likely to be underdiagnosed by gene panels, often the first genetic test utilized. We suggest that the etiologic evaluation of children with both CAKUT and CHD should be with diagnostic GWS and not by gene panel sequencing. The diagnostic superiority of GWS over gene panels is likely as a result of unrecognized phenotype expansion.
## Novel CAKUT and CHD genes
We identified three putative novel gene associations with CAKUT and CHD herein (LRP4, PCDH15, VPS4A). *These* genes contained a suspected disease-causing variant in an affected child, but the gene had not been previously associated with CAKUT and CHD. In one such case, variants in the PCDH15 gene have been associated with Usher syndrome (MIM 601067), which presents with hearing and vision loss, and matched the clinical phenotype for this patient [21]. The protein product of PCDH15 is also expressed in other organ systems, including the heart and kidney [21]. Thus, it was feasible that deleterious variation in this gene could be associated with both CAKUT and CHD phenotypes, as observed in this case. The variant identified in our patient was predicted to cause loss of function of the protein product through truncation or nonsense-mediated mRNA decay. Additional studies will be required to confirm that this is a recurrent phenotype expansion.
The other two genes identified have been implicated in kidney anomalies but have no prior reports of association with CHD [22, 23]. The LRP4 gene has been associated with renal agenesis [23]. Of the two variants identified in our patient, in silico prediction tools supported a deleterious effect of one variant on the protein structure/function due to the alteration of a highly conserved amino acid, and discordant results for the other variant. The VPS4 gene is associated with recurrent kidney infections, generally suggesting an underlying kidney anomaly [22]. In addition, the GeneDx in silico analysis supported a deleterious effect of the VPS4A missense variant on protein structure/function. Additional phenotype expansion case reports or in vivo studies will be required for causal gene association with CHD and CAKUT.
## Environmental factors
Several environmental teratogens have also been associated with CAKUT and CHD anomalies including certain drugs, chemicals, and fetal exposure to hyperglycemia [24]. Fetal structural defects are three to four-fold higher in infants born to diabetic mothers. These anomalies can affect a number of organ systems including: neurologic, gastrointestinal, kidney, and heart [25]. Our study identified six cases with mothers who had diabetes requiring insulin therapy during pregnancy. Of note, five of these cases ($83\%$) did not have an identifiable genetic etiology for their presentation. These findings suggest that genetic testing may have a low yield in patients with a likely environmental etiology for CAKUT and CHD.
Our study also identified three cases with VACTERL association and no known genetic etiology. The etiopathogenesis of VACTERL association is still unknown, but theories include possible teratogenic exposures, a malformation cascade, combination of environmental and epigenetic factors, or disturbances in developmental processes essential to all affected organ systems [24, 26]. Overall, of the 18 cases in our study without a genetic diagnosis, seven cases ($39\%$) had a suspected environmental etiology for their presentation. Our study suggests that the majority of patients with CAKUT and CHD will have an identifiable etiology for their anomalies, and thus a thorough history should evaluate for exposure to environmental teratogens, physical exam and imagining should evaluate for VACTERL association, and genetic evaluation should include WES or WGS.
## Expanding the phenotype of patients with CAKUT and CHD
Previously CAKUT and CHD have both been found as solitary anomalies or co-existing with other organ system malformations (3, 4, 7, 8, 27–31). Overall, $40\%$–$60\%$ of patients with a CAKUT or CHD have an additional extra-renal or extra-cardiac abnormality (7, 8, 27–30). CHD and CAKUT are known to co-occur in $25\%$–$30\%$ of affected individuals, more than any other concurrent organ system anomalies [7, 8, 31]. During study subject ascertainment, $23\%$ of hospitalized CHD cases were found to also have an identifiable CAKUT, in accord with published literature [7, 8, 31]. Electronic medical record review also identified additional organ system anomalies in 28 cases ($88\%$) with CAKUT and CHD (Table 5). The most commonly identified extra-renal/cardiac malformations were those in the nervous system ($53\%$ of all cases), with mainly brain malformations, and musculoskeletal system ($44\%$ of all cases), with primarily scoliosis and pectus anomalies identified. While underpowered for statistical analysis, certain organ system associations appeared to co-occur more commonly in those with genetic diagnoses or those without. For example, respiratory and eye/ear/face/neck anomalies were more likely to occur in those patients with a genetic etiology than those without, with nine out of 11 malformations ($82\%$) identified in those with an underlying genetic diagnosis. In contrast, cleft lip/palate, musculoskeletal, and nervous system anomalies occurred at about an equal rate in cases with and without a genetic diagnosis. Digestive, other circulatory, and genital anomalies occurred more frequently in cases without a molecular diagnosis, with 10 out of 12 malformations ($83\%$) identified in those without a genetic etiology. If substantiated in larger cohorts, these findings may guide prioritization for diagnostic GWS.
Overall, we found significantly more extra-renal/cardiac malformations in our patients with both CAKUT and CHD than has been reported with either CAKUT or CHD alone [7, 8, 31]. These findings may be related to the high rate of genetic syndromes identified in this study, with 13 of our 14 genetic diagnoses ($93\%$) herein related to a previously identified syndrome. However, additional structural malformations were identified just as commonly in those with environmental causes and no identifiable etiology for their presentation. Patients with CHD or CAKUT and additional organ system anomalies have higher morbidity and mortality, and thus our data may be biased, since hospital admission was among the selection criteria [27, 29]. Despite this possible skewing, these results still provide valuable information on what additional diagnostic work up should be considered, and suggest that hospitalized pediatric patients with both CAKUT and CHD should be evaluated for additional organ system malformations, most specifically in the musculoskeletal and nervous systems.
## Limitations
Our study has several limitations that need to be addressed. Primarily, this is a small study at a single tertiary center, which limited our ability to adequately power this analysis and provide any statistical evaluations. Secondly, we evaluated only hospitalized patients with CAKUT and CHD. This filter was set specifically to evaluate the sickest of this population, in order to better direct acute care and management. However, in limiting our population in this fashion, our findings may not be generalizable to patients who have not been hospitalized.
In addition,, we only evaluated those patients with exome and genome sequencing, missing those in which a genetic diagnosis is made via microarray, karyotype, or gene panels only. Without this information, we do not know if the patients diagnosed through these testing strategies would present with a different phenotype than our current patient population. It is also unclear if some of the more commonly known CAKUT/CHD genes were screened out with these testing methodologies, thus negating the need for more thorough genetic testing strategies. As reanalysis was only applied to those with WGS, it is also possible that additional genetic diagnoses were missed in those patients who only had WES.
Furthermore, we recognize the limitations of WES in comparison with WGS. Due to the nature of WES, this type of sequencing may have missed small CNVs, mitochondrial variants, intronic variation, repeat expansion, mobile insertion elements and coverage of PCR-free genomes [32]. Bertoli-Avella et al. found a $14\%$ increase in diagnostic yield of WGS over WES in a cohort of over 300 patients [32]. Taking these findings into account, it's possible we may have missed up to 2 molecular diagnoses in our WES cohort, that may have been identified had WGS been available.
Our study did not identify any repeat implicated genes in our population. These findings likely signify a lack of saturation due to our small patient cohort.
## Future directions
Although our study evaluated a small subset of patients at a single center, it is the first of its kind to specifically evaluate the genotype and phenotype of this patient population. This study provides a launching point to encourage additional studies interrogating all genetic testing in this patient population and to expand the evaluation to patients in both the inpatient and outpatient setting across multiple centers. Additional evaluation should also focus on those patients with CAKUT and CHD and no other affected organ system, as these may have a different and more enriched diagnostic yield.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: *All data* associated with this study are present in the paper. While no DNA sequence was generated as a part of this published work, all novel DNA sequence variants have been uploaded to ClinVar under our institutional identifier, Organization ID: 506081. Requests to access these datasets should be directed to etallred@health.ucsd.edu.
## Ethics statement
The studies involving human participants were reviewed and approved by UCSD IRB. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
ETA, EAP, NGC, ESK, and DPD contributed to the conception and design of the study. SFK, DPD, and ETA organized the database. ETA performed data collection and wrote the first manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
DPD is employed by Creyon Bio, Inc; reports previous consulting fees from Audentes and BioMarin; serves on a scientific advisory board for Taysha Gene Therapies; is an advisor to Pioneering Medicine VII, Inc; and is an inventor on United States patent 8718950B2 assigned to The HudsonAlpha Institute for Biotechnology.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Emerging of a new CD3+CD31HCD184+ tang cell phenothype in Sjögren’s syndrome
induced by microencapsulated human umbilical cord matrix-derived multipotent stromal
cells
authors:
- Pia Montanucci
- Onelia Bistoni
- Matteo Antonucci
- Teresa Pescara
- Alessia Greco
- Giuseppe Basta
- Elena Bartoloni
- Roberto Gerli
- Riccardo Calafiore
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10043489
doi: 10.3389/fimmu.2023.1095768
license: CC BY 4.0
---
# Emerging of a new CD3+CD31HCD184+ tang cell phenothype in Sjögren’s syndrome induced by microencapsulated human umbilical cord matrix-derived multipotent stromal cells
## Abstract
### Background
Sjögren’s syndrome (SS) is an autoimmune disease hallmarked by infiltration and destruction of exocrine glands. Currently, there is no therapy that warrants full recovery of the affected tissues. Umbilical cord-derived multipotent stromal cells, microincapsulated in an endotoxin-free alginate gel (CpS-hUCMS), were shown to modulate the inflammatory activity of PBMCs in SS patients in vitro, through release of soluble factors (TGFβ1, IDO1, IL6, PGE2, VEGF). These observations led us to set up the present study, aimed at defining the in vitro effects of CpS-hUCMS on pro- and anti-inflammatory lymphocyte subsets involved in the pathogenesis of SS.
### Methods and results
Peripheral blood mononuclear cells (PBMCs) upon collection from SS patients and matched healthy donors, were placed in co-culture with CpS-hUCMS for five days. Cellular proliferation and T- (Tang, Treg) and B- (Breg, CD19+) lymphocyte subsets were studied by flow cytometry, while Multiplex, Real-Time PCR, and Western Blotting techniques were employed for the analysis of transcriptome and secretome. IFNγ pre-treated hUCMS were assessed with a viability assay and Western *Blotting analysis* before co-culture. After five days co-culture, CpS-hUCMS induced multiple effects on PBMCs, with special regard to decrease of lymphocyte proliferation, increase of regulatory B cells and induction of an angiogenic T cell population with high expression of the surface marker CD31, that had never been described before in the literature.
### Conclusion
We preliminarily showed that CpS-hUCMS can influence multiple pro- and anti-inflammatory pathways that are deranged in SS. In particular, Breg raised and a new Tang phenothype CD3+CD31HCD184+ emerged. These results may considerably expand our knowledge on multipotent stromal cell properties and may open new therapeutic avenues for the management of this disease, by designing ad hoc clinical studies.
## Introduction
Sjögren syndrome (SS) is a systemic autoimmune disorder consisting of chronic inflammation of exocrine glands which leads to impairment of their secretory function and tissue damage. The disease course is rather mild in many patients, although a subgroup of subjects may develop severe extra-glandular effects. Quite often, currently available management options in these cases are ineffective (1–3). Therefore, identification of further pathogenic mechanisms that could be instrumental to therapeutic alternatives would be more than desirable.
Mesenchymal stem cells (MSC) exhibit natural propensity to exert anti-inflammatory and immunomodulatory effects. In particular, post-partum Wharton’s jelly-derived human adult mesenchymal stem cells (hUCMS [4], one of the “youngest” available mesenchymal stem cells, have been deemed to positively condition the immune system by both, reducing pathogenic T-cell subsets and potentiating their regulatory counterparts. In fact, in vitro overnight pre-treatment of hUCMS with the pro-inflammatory cytokine interferon-gamma (IFN-γ), induced the expression of molecules involved in the tolerogenic process, at the fetal-maternal interface, like indoleamine 2,3-dioxygenase 1 (IDO1), involved in the tryptophan catabolism, and led to an increase three classes of HLA: HLA-E, HLA-F and HLA-G. In particular, HLA-G5 is non-classical histocompatibility antigen complex, which was reported to contribute to hUCMS tolerogenic properties [5, 6]. These immune-active features favored application of MSC-based cell therapy to an array of autoimmune diseases including Systemic Lupus Erythematosus (SLE) [7], type 1 diabetes mellitus (T1D) and Sjögren Syndrome (SS), with the purpose of restoring a state of acquired immune tolerance (8–10).
To fully exploit hUCMS properties, we have enveloped them in our highly performing, and biocompatible alginate (AG)-based microcapsules [11] that physically separate the cells from the host’s immune system. AG derivatives still represent the most successful polymeric material associated with high biocompatibility and favorable porosity/permeability properties for microencapsulation of live cells. Meticulous purification technologies of the raw AG product (Process for the ultrapurification of alginates, Patent no. WO 2009093184 A139), originally extracted from brown seaweeds, have enabled fulfillment of regulatory criteria for human application.
We had preliminarily showed that IFN-γ-licensed microencapsuled hUCMS might positively impact on the immune system by expanding Tregs, in vitro, derived from patients with SS and T1D [9, 10]. Additionally, we proved that microencapsulated hUCMS transplanted in NOD mice with recent-onset autoimmune diabetes restored normoglycemia [12]. This outcome was ascribed to hUCMS-related both, immunomodulatory effects on regulatory T-cell subsets (Tregs), and paracrine action on islet cells.
The purpose of the present work was to further investigate the effects of in vitro co-culture of SS patient-derived PBMCs with microencapsulated hUCMS, on different T lymphocyte subsets (having Treg been investigated previously) [9] and also on B-cell subsets with special regard to regulatory Breg.
## Demographics and PBMC donor patient inclusion criteria
A cohort of patients aged 18 years or older with a diagnosis of SS according to ACR/EULAR 2016 criteria [13] were consecutively enrolled. As expected, all patients were females due to the high prevalence of the disease SS in female sex. A cohort of 6 age- and sex-matched healthy volunteers with a negative history for autoimmune/allergic diseases were included as normal controls. Exclusion criteria were: positive history for autoimmune/allergic diseases different from SS, ongoing immunosuppressive and glucocorticoid therapy, ongoing infectious disorders of any etiology, or positive history for infectious illness within 30 days prior to enrollment. Patients and healthy controls were consecutively enrolled and the study design did not require randomization or blinding. The study was approved by the local ethical committee (CEAS) and a written informed consent was obtained from all the selected subjects in accordance with the Declaration of Helsinki.
## PBMC isolation, co-culture and phenotypic analysis
30mL of heparinized peripheral blood was collected from each subject, the latter of which was used for CBC analysis. PBMCs were isolated by standard gradient separation on Lymphoprep™. For cell proliferation studies, the cells were labeled by CFSE Cell Division Tracker Kit (BioLegend) following the manufacturer’s instructions. PBMCs were co-cultured for 5 days with CPS-hUCMS after priming o/n the preparations with IFN-γ (2400U/106 cells). Unstimulated, and anti-human CD3 soluble (clone HIT3α, BioLegend) 1µg/mL-triggered PBMCs served for negative and positive controls, respectively. PBMCs: CPS-hUCMS ratio was 50:1. ( 6x103 hUCMS:3x105 PBMCs) in 300µL of complete CMRL medium. Cytofluorimetric assessment was performed using fluorochrome-conjugated monoclonal antibodies: CD3-PeCy7, CD3-FITC, CD3-eFluor450, CD4-PE, CD4-PeCy5.5, CD8-PeCy7, CD8-BV605, CD19-APC, CD24-PE, CD25-FITC, CD28-FITC, CD31-PE, CD38-PeCy7, CD45RA-BV510, CD184-APC, FOXP3-PE, IL10-AF488, IL17A-AF647. Antibodies were provided by Thermo Fisher Scientific and BD Pharmingen. As for IL17A, the cells were pre-treated with protein transport inhibitor cocktail (500x) (Thermo Fisher Scientific) following vendor’s recommendations. Before intracellular staining, the cells were fixed with $0.5\%$ formaldehyde and permeabilized with $0.1\%$ saponin. All cytofluorimetric assays were performed on the FACSCalibur™ Flow Cytometer with CellQuestPro™ (BD Bioscience) software and analyzed by FlowJo (RRID: SCR_008520) software (Tree Star Inc.).
## hUCMS procurement, isolation and culture
hUCMS isolation procedure from post-partum umbilical cords, obtained by caesarean section or natural childbirth, followed our established method [4]. At the end of the isolation/purification process, the cells were seeded, at a concentration of 6000 to 8000/cm2 per culture flask and incubated at 37°C in humidified $95\%$ air. Cell expansion throughout $80\%$ confluence was achieved by treatment with $0.05\%$ trypsin/EDTA (Gibco, Invitrogen, Milan, Italy) for 3 minutes at 37°C.
## AG procurement and purification for microencapsulation
Powdered alginate (AG) was purchased from Monsanto-Kelco (San Diego, CA) featuring the following properties: molecular weight=120-190 kDa; main AG polymeric patterns: mannuronic acid, M fraction (FM) $61\%$); and guluronic acid G fraction (FG) $39\%$. AG ultra-purification was conducted in-house under good laboratory practice (GLP), according to methods developed in our laboratory ((Process for the ultra-purification of alginates, Patent no. WO 2009093184 A139); endotoxin levels were <0.5 EU/mL, protein content <$0.45\%$, while viscosity was 100-300 cps.
## Microencapsulation of hUCMS
Briefly, hUCMS were thoroughly mixed with a $1.8\%$ AG solution, until a homogenous suspension was obtained. The cell/AG ratio was 3x106 hUCMS/1.2 ml AG. The suspension was then mechanically extruded through a microdroplet generator [11], and the AG/cell microdroplets were collected on a $1.2\%$ CaCl2 bath. Upon coating with $0.05\%$ poly-L-ornithine chloride (Sigma-Aldrich), the microbeads were partially de-gelled by 55mM sodium citrate for 10 minutes at r.t. The obtained microcapsules were finally coated with $0.1\%$ AG, and culture maintained at 37°C and $95\%$ air/CO2. These microcapsules were similar but not identical to those used in early pilot clinical trials in T1D [14]. In fact, here the longer exposure of the microbeads to sodium citrate resulted in better aggregation of the enveloped cells, and eventually facilitated the microcapsules breakage to retrieve the cells.
Microencapsulated cells (CpS-hUCMS) were exposed overnight to Interferon-γ (IFN-γ) (2400U/106 cells) (Sigma-Aldrich) that was removed prior to starting the co-culture incubation system with PBMCs. Cps-hUCMS were tested for viability, after overnight priming with IFN-γ, by ethidium bromide and fluorescein diacetate (Sigma-Aldrich) under fluorescence microscopy, using appropriate filter sets.
## Cps-hUCMS viability assay.
The cell viability assay was performed after microencapsulation and after o/n incubation with and without the addition of IFN-γ to verify cells survival within the microcapsules. On this purpose, a mixture was prepared containing Ethidium Bromide 1x (0.2mg/mL) in PBS 1x, to highlight dead cells, and Fluorescein Diacetate in acetone (5mg/mL), to assess the presence of live cells.
## Mechanical disruption of the microcapsules and analysis of IDO1 production
Upon o/n culture, after the microencapsulation procedure, two microcapsules aliquots were employed to extract total RNA and proteins. Capsules were resuspended in 4mL of saline and transferred onto a 6-well multiwell plate. Inside the well, the capsules were mechanically broken by a 22G syringe. Thereafter, the solution containing the aggregates was recovered and filtered through a 180-µm mash so that the aggregates (50-150µm in diameter) were eluted within the tube, while the residue was retained by filter. The recovered cell aggregates were washed with saline and subjected to RNA or protein extraction.
## Transcriptional expression analysis by quantitative PCR
Total cellular RNA was extracted from the cells (hUCMS or PBMC) using Direct-zol (Zymo Research corp). cDNA was synthesized using the iScript cDNA Synthesis Kit (Bio-Rad Laboratories) and was used as a template for quantitative PCR (qPCR). qPCR primers were designed using sequences from GenBank (http://www.ncbi.nlm.nih.gov/Genbank) (see supplementary material). qPCR amplifications were performed using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories) (RRID: SCR_008426) and Agilent AriaMx (RRID: SCR_019469) (Stratagene, La Jolla, CA). PCR products were demonstrated to be a single PCR product, by melting curve and electrophoresis analysis.
## Western blotting
Protein samples (40µg) were analyzed on $10\%$ SDS-PAGE and transferred onto nitrocellulose membrane (Bio-Rad Laboratories). The used detection primary antibody was mouse anti‐human IDO1 (1:10000, Millipore). Immunodetection was performed by Clarity Chemiluminescent kit (Bio-Rad Laboratories).
## Cytokine assay
Supernatants taken from the co-cultures of two representative healthy subjects while two representative SS patients were selected for simultaneous analysis of 15 cytokines (IL1a, IL1b, IL2, IL4, IL5, IL6, IL10, IL12, IL13, IL15, IL17, IL23, IFNγ, TNFα, TNFβ) by the Multiplex method. For each condition, 50µL of supernatant pre-diluted 1:2 in triplicate was used.
## Statistical analysis
Data were graphically represented and analyzed with GraphPad Prism (GraphPad Software) (RRID: SCR_002798), and Mann-Whitney, Wilcoxon. ANOVA tests were applied where necessary. In the evaluation of two single groups the unpaired Student t-test was performed. All statistical tests were two-tailed, and values of $p \leq 0.05$ were considered statistically significant. qPCR reactions were performed in triplicate and intra-assay variance was considered acceptable when SD was lower than 0.5. The results obtained have been expressed as ‘‘fold changes’’. HPRT1 served for control. All results were expressed as mean ± SEM of at least three independent experiments (*$p \leq 0.05$). The exploratory study design did not require a power analysis to determine sample or group size.
## Clinical characteristics of the selected SS patient group
The cohort included 9 female SS patients with a median age of 58 (49–68) years and a median disease duration of 2 [1,5-14,5] years. No dropouts or loss of samples and subjects were registered. As expected, xerophtalmia and xerostomia were the most frequent symptoms characterizing the whole cohort, both being reported in $83\%$ of patients. History of parotid swelling was reported in 1 ($17\%$) patient. Inflammatory articular involvement was the most frequent extra-glandular manifestation ($33\%$), followed by vasculitic purpura ($17\%$) and Raynaud phenomenon ($17\%$). As far as serologic features are concerned, anti-Ro antibodies were positive in all patients and half of them were also positive for anti-La antibodies. Two ($33\%$) patients displayed serum positivity for rheumatoid arthritis factor and 1 ($17\%$) had positive cryoglobulins without features suggestive for active cryoglobulinemia. Finally, minor salivary gland biopsy resulted positive (focus score ≥ 1) in all patients. Hemocytometry analysis, performed in conjunction with blood sampling, showed normal white blood cell count in all patients and controls. Basal Lymphocyte Counts Counts of the immune cell subsets of interest in peripheral blood of SS patients and controls were performed by the time of collection (Table 1). Data were consistent with what had been described in the literature and attributable to either recruitment of immune cells into the exocrine glands, or the inflammatory condition typical of the disease [15, 16]. In fact, in the selected SS patients, lymphocytes/μl were significantly lower than controls. Moreover, the frequency of Tang, Th17DN, Breg and was lower than in healthy people; the frequency of CD19+ B lymphocytes showed no differences.
**Table 1**
| Subpopulations | CTR(6) | PZ SS(9) | P value(significantly different p<0.05) |
| --- | --- | --- | --- |
| Mean lymph/μL | 1833 ± 378 | 1300 ± 126 | 0.0083 |
| Lymphocytes T CD3+ | 1391 ± 205 | 809 ± 104 | 0.0002 |
| Lymphocytes Tang | 403 ± 199 | 156 ± 61 | 0.0176 |
| Lymphocytes Th17 DN | 103 ± 16 | 53 ± 24 | 0.0056 |
| Lymphocytes B CD19+ | 262 ± 69 | 177 ± 101 | No |
| Lymphocytes Breg | 25 ± 6 | 8 ± 3 | 0.0011 |
## CpS-hUCMS analysis
hUCMS formed cell aggregates upon microencapsulation in AG [9, 10]. Figure 1A shows microcapsules containing hUCMS, fresh and upon overnight incubation. Within microcapsules, the cells formed compact 3D aggregates measuring 50 to 150 μm in diameter. A few cells failed to aggregate, and remained disperse within the microcapsules. Under fluorescence microscopy, upon staining with ethidium bromide and fluorescein diacetate, both disperse and clustered cells looked very viable. Aggregate formation, induced by the capsule citration procedure (to remove the capsular gel core), combined with optimal cell viability, is essential for both, cells survival in culture for 5 days (Figure 1A) and optimal IDO1 production in response to IFNγ. Specifically, we confirmed that IFN-γ concentration of 2400 U/106 cells, previously used in our experiments [9], greatly enhanced production capacity of IDO1 in response to IFNγ concentrations from microencapsulated hUCMS. In fact, it was possible to evidence both, messenger induction and protein production (Figures 1B, C). Furthermore, WB consistently showed production of the IDO1 protein after o/n priming with IFNγ in all experiments (Figure 1D).
**Figure 1:** *(A) Microcapsules containing hUCMS after the procedure and at the indicated times, ethidium bromide/fluorescein assay demonstrates the optimal viability of single cells and aggregates over the experiment duration. (B) Expression levels of IDO1 as mRNA and as protein (D) in CpS-hUCMS and CpS-hUCMS + IFNγ in comparison to a positive control. (C) Representative autoradiographic plate of the expression of IDO1 (45kDA) and tubulin (50kDA) in the indicated samples is shown. (E) Proliferation percentage of PBMCs from the group of healthy subjects (CTR) and SS patients (SS) in the various conditions (mean ± S.D., Anova Test, p<0.001, n.s. not significant). (F) Percentage reduction in PBMCs proliferation of recruited patients and controls (mean ± S.D., Mann-Whitney T-Test). (G) eTreg in control vs SS at the indicated conditions. Biological variability between samples is evident, but it is clear that PBMCs from SSs after 5 days of culture show much lower percentages of eTreg than controls while the presence of Cps-hUCMs increases their percentage.*
## Inhibition of proliferation evaluation
At the end of the five-day incubation period with and without CpS-hUCMS, PBMCs from controls and SS patients were assessed cytofluorimetrically to assess proliferation levels (Figure 1E). Within our control samples, we observed an average proliferation increase of $81\%$ ± 11 in PBMCs activated with CD3s as compared to those maintained in simple medium; PBMCs activated and incubated with CpS-hUCMS showed a decrease in proliferation rate to around $51\%$ ± 12. In Figure 1E, the proliferative rates of PBMCs in complete medium and with CpS-hUCMS are also compared, with the latter showing no proliferation stimulation in control PBMCs.
PBMCs from SS patients also showed a behavior similar to that of healthy subjects; however, activation with CD3s induced a similar proliferative response (mean value 78 ± $5\%$) whereas the presence of CpS-hUCMS was associated with a weaker decrease in proliferation (72 ± $2\%$) (Figure 1F). Thus, we observed a greater inhibitory effect in the controls (37.1 ± $16.52\%$) than in the patient samples. In fact, the percentage of inhibition of PBMCs in SS patients was significantly lower than that of controls (9.79 ± $6.55\%$). The high reactivity and inflammatory nature of SS PBMCs could explain these readings but, nevertheless, CpS-hUCMS were shown to inhibit their proliferation.
## Treg characterization
We observed the presence of an increase in Treg by characterizing them as CD3+CD4+FOXP3+ cells and used the marker CD45RA in order to distinguish effector Treg (eTreg CD4+CD45RA-FOXP3+) from naive Treg (nTreg CD4+CD45+FOXP3LOW) and from non-Treg FOXP3+ cells. Both nTreg and eTreg were low in SS after 5 days of culture, as compared to controls. However, upon CpS-hUCMS exposure, eTreg in PBMCs from SS equaled controls (Figure 1G). Moreover, the FOXP3 messenger in these PBMCs was basally much lower as compared to controls, but its relative expression doubled after co-culture with CpS-hUCMS (Figure 2). However, the presence of CpSs did not affect nTreg.
**Figure 2:** *qPCR for indicated messengers expressed byPBMC of control and Sjogren patients after 5 days of culture with or without CpS-hUCMS (pretreated with IFNγ 2400 U/106 cells) and with or without CD3. The expression of each marker was calculated by assigning PBMC+CD3s with the arbitrary value 1. HPRT1 served for control. All results were expressed as mean ± SEM (*P<0.05).*
## Angiogenic T lymphocytes
Angiogenic T lymphocytes (Tang) constitute a recently discovered immune subset identified by the CD3+CD31+CD184+ phenotype [17] and involved in the repair of vascular damage and angiogenesis through release of soluble factors and the mobilization of endothelial progenitor cells. For this reason, characteristics of this T subset and its response to CpS-hUCMS were investigated.
Comparison of the two study groups after 5 days of incubation under the various experimental conditions, was associated with a significantly higher percentage of Tang lymphocytes in controls than in patients, under all conditions, except when PBMCs were exposed to CpS-hUCMS. This may suggest that hUCMSC exert the modulating effect equally, in healthy subjects and patients (Figure 3A). No significant differences in Tang lymphocyte levels were found between conditions in each group. This finding may suggest an impairment of the vascular repair mechanisms in patients, that is corrected by CpS-hUCMS.
**Figure 3:** *(A) Comparison of the percentages of CD3+CD31+CD184+ Tang lymphocytes in the PBMCs between healthy subjects (CTR) and SS patients (SS) under different conditions (mean ± S.D., Mann-Whitney T-Test, * p<0.05, ** p<0.005). No statistically significant differences were found in Tang lymphocyte percentages between conditions within the control and patient groups. (B) Percentage of CD28+ and CD28- Tang lymphocyte subsets within the total Tang lymphocyte population of healthy subjects and SS patients at the different conditions. (mean ± S.D., Mann-Whitney T-Test, p<0.05*, p<0.005*, n.s. not significant). No statistically significant differences were found in the percentages of CD28+ and CD28- Tang lymphocytes between conditions in the control group and in the patient group, with the exception of the comparison between control PBMCs treated with soluble anti-CD3 and PBMCs regarding CD28+ (p<0.05). (C) Representative cytofluorimetric dot-plots of total Tang lymphocytes belonging to a SS patient after five days of incubation in the absence (left) and presence (right) of CpS-hUCMS. The population in the red rectangle was called CD31H Tang; while that in the green rectangle is the Intermediate Tang. (D) Comparison of the percentages of Tang CD31H and Tang CD31Int lymphocytes in the total Tang CD3+CD31+CD184+ of healthy subjects (CTR) and SS patients (SS) in PBMCs and in PBMCs maintained in the presence of CpS-hUCMS. Significant was the increase in CD31H in PBMCs from SS in the presence of CpS-hUCMS. (mean ± S.D., Mann-Whitney T-Test & Wilcoxon T-Test, p<0.05*, p<0.005*, n.s. not significant). (E) Representative percentage of CD28 in the CD31H in selected SS patient. Almost all CD31H also expressed CD28. (F) Table with averages of CD31H in controls and patients with and without CpS-hUCMS.*
## CD28+ T angiogenic and CD28- T angiogenic
CD28 represents a key cell surface marker for sorting Tang lymphocytes into two different subsets [18, 19]: CD28+ tang and CD28-tang (Figure 3B). The former hallmarks younger cells that are actively involved in the process of angiogenesis through the release of cytokines such as IL8, IL17 and VEGF; the latter identifies a senescent phenotype. In the PBMCs of CTRs, there is an approximately equal percentage of CD28+ and CD28- Tang, and the presence of CD3s or CD3s with CpS-hUCMS results in a shift towards the CD28+ subset (PBMCs+CD3s: 41,3 ± 8,$1\%$; PBMCs+CD3s+CpS: 41,1 ± 15,$4\%$). In SS samples, the Tang after 5 days of culture are mostly CD28; however, the presence of CpS-hUCMS, CD3 with and without CPS-hUCMS, results in an increase of CD28+ Tang cells such that the percentages observed in the control samples are restored.
## Angiogenic T subset: Tang CD31H
Cytofluorimetric dot-plots of the Tang CD3+CD31+CD184+ T relative to the PBMCs of the controls and especially of the SS in the presence of the CpS-hUCMS (Figure 3C indicated the emergence of a particular angiogenic lymphocyte subset, to our knowledge never previously described in the literature, characterized by a high expression of CD31 and definable as Tang CD31H, and an angiogenic T subset encompassing Tang Intermediate (Tang CD31Int) cells, with intermediate expression levels of CD31). Under activation conditions (presence of CD3s), the Tang CD31H population is not detectable within the PBMCs in either group, as confirmed by the published literature [20]. Figure 3D shows the comparison between Tang CD31H and Tang CD31Int of controls vs. SS, with and without CPS-hUCMS. PBMCs from SSs showed a significant duplication of % Tang CD31H within PBMCs co-cultured with CpS-hUCMS, as compared to cells incubated in medium only. The Tang CD31Int of controls were higher in percentage than those of SS, and were not adversely affected by the presence of CpS-hUCMs, whereas, with respect to SS, percentage reduction of Tang CD31Int in the presence of hUCMSC was statistically significant. Hence in SS, CpS-hUCMS resulted in significant increase in Tang CD31H (Figure 3F) and this was due to the increase of CD31 expression by Tang CD31Int. Furthermore (Figure 3E) over $95\%$ of the Tang CD31H T were CD28+ both, in the presence and absence of the CpS-hUCMS in both groups. Collectively, these observations allowed us to define a new, previously undescribed T cells population, characterized by the high expression of CD31 and positive for CD28, which warrants further study.
## CD19+ B lymphocytes
B lymphocytes are crucial for the progression of SS: they intervene during the most advanced stages of the disease and directly or indirectly promote inflammation through the production of autoantibodies. Given their relevance in the disease and the lack of data on the effects of CpS-hUCMS on this cell population, we first examined CD19+ B lymphocytes. Obtained data (Figure 4A) showed that in SS PBMCs co-cultured with CpS-hUCMS, there was a significant higher increase in the percentage of CD19+ B lymphocytes than in controls (CTR: $14.67\%$ ± 0.33; SS: 19.97 ± 1.6; $$p \leq 0.0095$$**). In contrast, no clear variations between healthy versus unhealthy were found within the other conditions.
**Figure 4:** *(A) Proportion of CD19+ B lymphocytes in PBMCs of healthy subjects (CTRL) and SS patients (SS) in the various conditions (mean ± S.D.; Mann-Whitney T-Test; p<0.005**; ANOVA test in SS; p<0.005** between CpS-hUCMS vs Activated and CpS-hUCMS vs Act. + CpS-hUCMS). (B) Percentage of Breg lymphocytes in the PBMCs of healthy subjects (CTR) and SS patients (SS) in the various conditions (mean ± S.D.; Mann-Whitney T-Test; p<0.5*; ANOVA test in SS; p<0.005** between Medium vs CpS-hUCMS, CpS-hUCMS vs Activate and CpS-hUCMS vs Att. + CpS-hUCMS). (C) B/B-reg lymphocytes ratio in the two groups at various conditions.*
In the control group, there were no differences in the percentage of CD19+ B-cells between conditions, whereas in the SS group, a significant difference was found between the CD19+ number in CpS-hUCMS co-cultures and that of activated PBMCs both in the presence/absence of encapsulated mesenchymal cells. This finding is partly explained by the decrease of B-cells when an activating stimulus such as CD3s is introduced into the system. The qPCR (Figure 2) for transforming growth factor-beta 1 (TGFβ1) messenger confirmed the trend described for CD19+ B in the two groups. In fact, it is on average more highly expressed in the PBMCs of SS patients than in controls, both grown in basal medium and in the presence of CpS-hUCMS. The presence of CD3s downregulates its expression in both groups. PBMCs from SS grown in medium for 5 days showed lower levels of messenger for BACH2 (Figure 2) as compared to control PBMCs, whereas in the presence of CpS-hUCMS PBMCs from SS, showed higher levels for this messenger than control PBMCs. This messenger was also inhibited by the presence of CD3s, and CpS-hUCMS was irrelevant.
Furthermore, the expression of CD20+, a marker of B lymphocyte maturation progression, indicated that there were no significant differences between either study groups or conditions (data not shown). This finding seems to indicate that although CpS-hUCMS stimulated CD19+ B lymphocyte proliferation, it did not allow them to reach full maturity.
## B10 regulatory lymphocytes
We aimed to evaluate the effects of microencapsulated hUCMS on regulatory B10 lymphocytes, i.e., those expressing the anti-inflammatory cytokine IL10. In particular, we studied the ‘transient’ Breg subset, characterized by CD19+ cells predominantly positive for IL10 and expressing high levels of the surface markers CD24 and CD38. The co-culture experiments showed that there was a significant difference between the percentage of Breg B10 (Figure 4B) in healthy subjects and SS patients cultured in medium alone. In the SS group, the presence of the CPS-hUCMS led to significant increase in this subset as compared to all other conditions, with a restoration of the Breg cell subset to the levels found in healthy individuals. Activation with CD3s depressed this population and the concomitant presence of CPS-hUCMS increased their percentages in both controls and SS to pre-activation levels.
## Cytochines’ evaluation
The data obtained by measuring the concentration of certain cytokines and IFNγ (Figure 5) in the supernatant of the co-cultures and the expression of specific messengers (Figure 2) in the PBMCs helped us to better understand the action of stem cells on Tang. In particular, CPS-hUCMS in PBMCs from SS led to a lower concentration of IL17 and IFNγ in the culture medium as compared to controls. Activation with CD3s increased concentration of all cytokines leading, in controls, to maximum concentration which was negatively modulated by the presence of CpS-hUCMS, as far as IL6 and IL23 but not IL17 and IFNγ were concerned. In SS, CD3s increased their concentrations; for IL6 and IFNγ the simultaneous presence of CpSs was irrelevant; for IL23 the presence of CpSs and CD3 induced a marked decrease comparable to control, whereas IL17 doubled its concentration as compared to simple activation with CD3 and compared to the respective control points. qPCRs for the IL17A messenger (Figure 2) showed an increase in its relative amount in PBMCs from SS in the presence of CpS-hUCMS, which for control PBMCs was more evident but, as described above, it was not accompanied by a similar increase in the concentration of this cytokine in the culture medium.
**Figure 5:** *Amount of indicated cytokines (pg/mL) released in the supernatant from PBMC of control and Sjogren patients after 5 days of culture with or without CpS-hUCMS (pretreated with IFNγ 2400Y/106 cells) and with or without CD3s. All results were expressed as mean ± S.D. (*P<0.05, **P<0.005, ***P<0.0005).*
Further evidence confirming the modulatory activity of CpS-hUCMSs on Breg lymphocytes came from the amount of IL10 detected in the supernatant under various conditions (Figure 5). In SS patients, unlike healthy subjects, the presence of CpS-hUCMSs alone did not lead to an increase in the production of this cytokine, which instead reached high levels once upon CD3s introduction. This finding suggests that CpS-hUCMS stimulate the regulatory component of B lymphocytes, to which we attribute the increased expression of this cytokine, not only in terms of proliferation but also of production of IL10, which would be stored within the cytoplasm. However, whereas in healthy individuals there is a discrete increase of IL10 in the supernatant of the co-cultures, in SS patients the presence of an additional stimulus (in our case CD3s) is required for the complete release of IL10. The increase in Breg CD19+CD24+CD38+IL10+ induced by CpS-hUCMSs occured at expense of reduction of B CD19+, which brought the Bcell/Breg ratio up to that of controls (Figure 4C).
## Messangers’ evaluation
PD-L1 a receptor expressed on T and B cells, upon activation, is able to inhibit proliferation or activate the process of cell death of lymphocytes: in fact, in our samples after activation with CD3s the expression of its messenger declined in both controls and patients with SS (Figure 2). Its trend is similar to that of TGFβ1. The messenger for TIM1 on control PBMCs was never expressed; in some subjects it appeared after co-culture with CpS-hUCMS, resulting in wide variability in these samples; it is then well expressed after stimulation with CD3s with and without CpS-hUCMS. In contrast, in patients with SS it was present on PBMCs cultured in simple medium and in the presence of CpS-hUCMS and up-regulated after stimulation. TIM1 encodes for a glycoprotein expressed on the surface of Th2 lymphocytes that is involved in T-cell activation, being also present on Breg, induced by mesenchymal stem cells.
## Discussion
The most important finding of this work can be summarized as follows: enhancement of Tang CD28+ lymphocyte subset, definition of new CD31H angiogenic T lymphocyte subset, and increase of B10 regulatory subset (Breg CD19+CD24HCD38HIL10+), all the above described effects are attributable to the action of CpS-hUCMS.
We previously [9] demonstrated that CpS-hUCMS represents a functional biohybrid artificial system where cellular/molecular products induce powerful immunomodulatory in vitro effects on Treg cells of patients with SS. Instead now the aim of the present work was to investigate the effects on other Tc populations and also on B cell subsets with a particular focus on regulatory B cells. hUCMS are associated with important immunomodulatory effects through both, cell-to-cell contact and release of soluble factors (TGFβ1, IDO, HLAG5 IL6, PGE2, VEGF). Many studies in the literature confirm the beneficial effects of their application to various diseases, however some of them (3, 21–23) conducted in vitro, have also shown how autoimmune PBMCs can reduce the functionality of mesenchymal stem cells upon direct contact. As we have already pointed out [11], very useful is the use of a polymeric artificial matrix, e.g. endotoxin-free alginate, to prevent direct contact of hUCMS with the host’s immune system, although with no interference with secretion of immunomodulatory molecules. Most importantly, sodium alginate microcapsules, when properly formulated, allow hUCMS to acquire a three-dimensional structure [9] that allows them to survive longer in comparison with not aggregated cells, and enhances secretion of various immunomodulatory factors [24] after treatment with IFNγ. Data on viability, IDO1 production and ability to inhibit proliferation of stimulated PBMCs confirm the effects of CpS-hUCMS on patient PBMCs. In addition, hUCMS have been shown to affect preferentially eTreg, by increasing their rates, which enabled us to complete our previous observations [9].
## Enhancement of Tang CD28+ lymphocyte subset
The effects of Cps-hUCMS on *Tang is* remarkable [17]. This is a lymphocyte subset that reacts to vascular wall injuries and induces, in cooperation with endothelial cells (EPCs) and other immune populations (Treg), tissue repair, with the involvement of cytokines and pro-angiogenic factors (IL8, IL17, VEGF) [18]. They are also endowed with a high capacity of adhesion to ECs and trans-endothelial migration. Altogether, these properties give Tangs the ability to promote formation of new vessels in vitro and in vivo [17]. Recent literature shows that the percentage of Tang lymphocytes in the peripheral circulation is markedly reduced in subjects with SS, leading to defective vascular layer restoration and endothelial cells dysfunction. This data supports previous reports (17, 25–27). Tang can be classified according to the presence/absence of CD28 [18], one of the two known ligands for CD$\frac{80}{86}$, a cell surface molecule involved in T-cell activation, in the induction of cell proliferation, cytokine production and of T-cell survival promotion. It should be noted that CpS-hUCMS did not affect Tang cell proliferation but changed the ratio within the same population, thereby favoring the immature and more active cell phenotypes (Tang CD28+) [28] and restoring a situation similar to controls. Decrease in Tangs CD28- in our patients is desirable because this population correlates directly with serum levels of cytokines and autoantibodies associated with endothelial damage and poor SS prognosis [28].
## New CD31H angiogenic T lymphocyte subset
In addition, analysis of Tang lymphocytes, (CD3+CD31+CD184+), showed the presence in the co-cultures with PBMCs of both, controls, and SSs of a defined CD31H angiogenic T lymphocyte subset that was never previously described in the literature. The presence of CpS-hUCMS (probably through production of VEGF and IL6, two potent activators of angiogenesis) results in a significant increase in the percentage of CD31H Tang in SS, as does the reduction that occurs within Tang CD31Int. The fact that almost all CD31H Tang cells express CD28 makes this subset numerically not negligible. The enhanced action of Tang CD28+ expressing large amounts of CD31 by hUCMS could have very positive impact on patients. CD31 [29] is an efficient signaling molecule that plays several roles in vascular biology including angiogenesis, platelet function, and thrombosis, mechano-sensing of endothelial cells in response to fluid shear stress, and regulation of multiple stages of leukocyte migration through venular walls. Chronic autoimmune diseases are associated with increased risk of cardiovascular death, and endothelial dysfunction represents the first stage of subclinical atherosclerosis [25]. In SS, an increment of new blood vessel formation associated with an increased number of macrophages and histiocytes infiltrating in the stroma of the inflammatory lesions may occur [25] but it is a defective venous system that can be healed by young Tang with the higher expression of CD31 induced by CpS-hUCMS [30].
Based on our experimental evidence, we can hypothesize (Figure 6) that our findings occur as a consequence of certain soluble factors such as IL6, IL17 and IFNγ, which are present in high amounts in the supernatant of co-cultures. IL6, in addition to being produced by different lymphocyte types, is included in the secretome of hUCMS (31–34) and is responsible, together with VEGF, for maintaining the inflammatory and pro-angiogenic microenvironment. Once released, IL6 would act in our system on CD28+ Tang forcing them to produce IL8, IL17 and VEGF [35] hence creating a pro-inflammatory and angiogenic circuit in the vascular microenvironment aimed at restoring its functional integrity.
**Figure 6:** *Schematic representation of the hypothetical mechanisms of action of PBMC from Sjogren patients after 5 days of culture with CpS-hUCMS. Green arrows indicate an increase in the presence of CpS-hUCMS; red arrows indicate a decrease in the presence of CpS-hUCMS; whereas arrows in blue indicates an increase in the presence of both CpS-hUCMS and CD3s.*
## B10 regulatory subset (Breg CD19+CD24HCD38HIL10+)
The results obtained from the analysis of the B lymphocyte population subset after five-day incubation with CpS-hUCMS showed how the latter are able to condition this cell type in terms of both, proliferation, and function. CD19+ represents a characteristic maturation marker of the B lymphocyte lifespan: when these cells take over the plasma cell phase, they tend to lose it, as we saw after the addition of CD3s to the system. The various soluble stem cell factors (VEGF, IL6, HLAG5, IDO1) seem to prevent this process, by keeping the B cells in a state of functional immaturity and stimulating the Breg lymphocytes to produce IL10, PDL1, and TGFβ1 before spilling them into the surrounding environment. This event occurred with greater effects in SS samples than in CTRs.
Another evidence in support to the effects of CpS-hUCMS on B lymphocytes comes from the mRNA expression of BACH2, a transcription factor that, through Treg differentiation and maintenance, promotes B cell proliferation and differentiation into memory cells. This protein acts on two stages of B lymphocyte maturation: within the thymus, high expression of BACH2 guides the cells to various maturation stages, by promoting the expression of specific surface markers; in the periphery, however, BACH2 is gradually down-expressed until, having reached the plasma cell stage, it is no longer produced. If we consider the high levels observed in SS and CTRLs when PBMCs were co-cultured with CpS-hUCMSs and compared them with those when CD3s were added, it can be assumed that stem cells prevent B lymphocytes from continuing on their pathway to the plasma cell stage to keep them in an immature, quiescent state.
The action of CpS-hUCMS also seems to extend to the B10 regulatory subset (Breg CD19+CD24HCD38HIL10+), leading especially in SS to an important percentage increase of this population, as compared to that recorded for PBMCs alone. Molecular analysis showed reduced expression of the messenger for BACH2 in PBMCs, and increased expression of PD-L1 and TGFβ1, which are crucial for the Breg regulatory activity [36, 37]. Since, unlike what was observed in controls, the Breg of SS samples seem unable to release IL10 in the presence of CpS-hUCMS we might assume that the Breg of SS patients are characterized by functional defects [38], and that they need the joint stimuli of CpS-hUCMS, CD3s and the Tc in order for them to release the IL10 stored in the cytoplasm. This regulatory system that would be activated by CpS-hUCMS could contribute to modulate the pro-angiogenic circuitry thus preventing the inflammation to become uncontrolled and result in further damage.
We may then hypothesize that CpS-hUCMS result in a blockade of the maturation of B lymphocytes into plasma cells and their redirection toward the regulatory phenotype [39]. In particular, other soluble mediators abundantly produced by mesenchymal stem cells could be responsible for these effects: IDO1 and HLAG5 are important in inhibiting immune activation and promoting the differentiation of regulatory subsets of T and B lymphocytes [40]; IL6, possibly associated with IL1β, for induction of Breg lymphocytes [41]. Finally, VEGF, which not only stimulates vascular repair but also promotes the survival and proliferation of peripheral CD19+ B lymphocytes through blocking caspase 3-mediated apoptosis [42].
## Conclusion
In this work we provided preliminary evidence that CpS-hUCMS represent a functional biohybrid artificial system where cellular molecular products are able to exert powerful immunomodulatory effects in vitro on T cells and B cells in pSS. In particular, we described the strong induction of Breg lymphocytes and the emergence of a Tang phenotype greatly expressing CD31. Both will deserve to be studied in depth, by subtyping and function assays, in order to confirm the postulated beneficial action on patients.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Materials, further inquiries can be directed to the corresponding author/s.
## Ethics statement
The studies involving human participants were reviewed and approved by local ethical committee for clinical studies (CEAS). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
PM designed the research, performed experiments, analyzed data, write the article, arranged figures, OB designed the research, performed experiments, write the article, MA performed experiments, analyzed data, write the article, TP cultured hUCMS, performed experiments, AG performed experiments, GB performed microencapsulation, EB patients’ enrollment, RG edit the article, RC edit the article and supervised experiments. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1095768/full#supplementary-material
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|
---
title: 'Stress-associated weight gain, fibromyalgia symptoms, cardiometabolic markers,
and human growth hormone suppression respond to an amino acid supplement blend:
Results of a prospective, cohort study'
authors:
- Susan Pekarovics
- Adam Beres
- Colleen Kelly
- Sonja K. Billes
- Amy L. Heaton
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10043493
doi: 10.3389/fendo.2023.1053692
license: CC BY 4.0
---
# Stress-associated weight gain, fibromyalgia symptoms, cardiometabolic markers, and human growth hormone suppression respond to an amino acid supplement blend: Results of a prospective, cohort study
## Abstract
### Introduction
An orally administered amino acid-based test supplement was recently shown to increase human growth hormone (hGH) in healthy adults. This prospective, observational, single-center, single-arm cohort study investigated the effects of 24 weeks of daily oral administration of the test supplement in individuals with stress-related weight gain, fibromyalgia (FM) and stress-related low-normal hGH production (15-30th percentile for age-appropriate levels) on insulin-like growth factor 1 (IGF-1), an indicator of hGH levels caused by stress related stimulation of somatostatin.
### Methods
Participants continued to receive standard care. The primary endpoint was the change from baseline to endpoint (Week 24) in serum IGF-1. Additional endpoints included the change in body weight, clinical symptoms (assessed with the Revised Fibromyalgia Impact Questionnaire [FIQR], range 0-100, and Perceived Stress Scale [PSS], range 0-40), fasting cardiometabolic markers, tolerability, and safety. The study enrolled 84 fibromyalgia patients with low-normal age-adjusted IGF-1 serum levels. High mean ± Standard Deviation (SD) baseline FIQR and PSS scores of 76 ± 16 and 32 ± 5, respectively, indicated poor to moderate symptom management with standard care. All individuals completed 24 weeks.
### Results
Serum IGF-1 levels increased with a Week 24 mean± Standard Error (SE) change of 28.4 ± 3.0 ng/mL ($p \leq 0.001$). Body weight was reduced with a Week 24 mean ± SE change of -5.5 ± 0.3 kg ($p \leq 0.001$) (a $6.5\%$ weight loss from baseline). The change from baseline in FIQR and PSS scores were -29.1 ± 1.1 and -20.0 ± 0.8, respectively (both $p \leq 0.001$), indicating a substantial improvement. Statistically significant improvements from baseline to Week 24 were observed in systolic and diastolic blood pressure, HbA1c, LDL and HDL cholesterol, and triglycerides (all $p \leq 0.001$). The supplement was well tolerated; no adverse events were reported.
### Discussion
Sustained augmentation of IGF-1 with the test supplement may represent a novel method of improving clinical symptoms, including stress-related weight gain, in individuals with fibromyalgia and stress-associated low-normal hGH.
## Introduction
Fibromyalgia (FM) is a syndrome characterized primarily by chronic pain, fatigue, many times with significant weight gain, and sleep disturbance that affects approximately 3-$8\%$ of the general population (1–3). It is most prevalent in women [1]. Fibromyalgia is most commonly diagnosed in individuals aged 20-50 years but the incidence increases with age so that approximately $8\%$ of adults meet classification criteria for fibromyalgia by age 80 [4]. Other common concurrent symptoms include increased stress, disturbed sleep, impaired cognition, fatigue, mood disorders, other pain disorders, endocrine disorders, and obesity [1, 5, 6]. There is no cure for fibromyalgia; treatment is individualized based on patient symptoms and may include medications, behavioral approaches, and lifestyle modifications, including regular exercise. The benefits of consistent resistance and aerobic physical activity regime, as well as multimodality physical therapy have been demonstrated in the current literature (7–10).
Several studies indicate that reduced production of human growth hormone (hGH) is evident in approximately $30\%$ of individuals with fibromyalgia and may play a role in its pathophysiology (11–14). Impaired hGH production in these individuals is hypothesized to contribute to common fibromyalgia symptoms and comorbidities, such as fatigue, disordered sleep, impaired cognition, decreased lean body mass, increased adipose tissue, muscle weakness, and poor general health [11, 14]. Furthermore, preliminary studies have demonstrated that treatment with rhGH produces an improvement in tender points and fatigue in individuals with fibromyalgia [15, 16]. There are no Food and Drug Administration (FDA) approved treatments for increasing hGH levels in individuals with FM who do not have hGH deficiency (below the 15th percentile for age). Thus, rhGH therapy in individuals with low hGH levels (but higher than the cutoff for GHD) is expensive, requires dose monitoring and adjustment, and may result in adverse effects. Additionally, the incidence of FM and low hGH increases with age [4, 17]. However, treatment of fibromyalgia in elderly patients is complicated by greater prevalence of comorbidities and polypharmacy, as well as the increased risk of adverse drug reactions. There is a need for safe and effective methods to increase hGH in patients with fibromyalgia and low hGH, including the elderly.
An oral amino acid-based test supplement was recently shown to increase release of endogenous hGH in healthy individuals [18]. The test supplement is hypothesized to enhance the release of hGH by suppressing somatostatin [19] and preliminary observations indicate that continued administration of the supplement promotes sustained improvements in hGH levels [19]. This open-label, single-arm study investigated the effects of 24 weeks of daily oral administration of the amino acid-based test supplement in addition to standard care on serum IGF-1, a surrogate marker of hGH secretion in individuals with treatment-resistant fibromyalgia and low-normal hGH production (between the 15th and 50th percentile for age-appropriate levels of IGF-1) [13]. Impaired hGH production can be identified by reduced levels of insulin-like growth factor (IGF-1), a mediator of hGH action and a long-term indicator of hGH levels [17]. We hypothesized that oral administration of the supplement for 24 weeks would increase IGF-1 levels and improve clinical symptoms in individuals with treatment-resistant fibromyalgia and low-normal IGF-1.
## Study design
All studies were conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. Written informed consent was obtained from all participants in all studies. The study is registered at clinicaltrials.gov, identifier: NCT04510181.
This was a prospective, open-label, single-arm, observational, single-center study designed to assess the efficacy of the test supplement in individuals with low-normal hGH production over 24 weeks. The supplement was well tolerated. No participants withdrew from the study early and no adverse events were reported. There were no adverse changes in vital signs, laboratory markers, or physical examinations.
Participants were recruited and 84 fibromyalgia patients were enrolled with low-normal age-adjusted IGF-1 serum levels from the Study Site (Private Practice of Susan Pekarovics, MD, Inc., Los Angeles, CA) between 2018 and 2020. All enrolled patients completed the study. Eligible participants were males and females between the age of 18 and 80 years and had a clinical diagnosis of fibromyalgia (according to the 2010 American College of *Rheumatology criteria* [6]) for which they were receiving standard care at the Private Practice of Susan Pekarovics, MD, as well as fibromyalgia-related comorbidities, including low-normal hGH production (between the 15th and 50th percentile for age-appropriate levels of IGF-1) [11, 12]. Female participants of childbearing potential agreed to avoid pregnancy during the study. Participants were excluded from the study if they were diagnosed with human growth hormone deficiency (GHD), defined as IGF-1 levels below the 15th percentile for age appropriate levels) [11, 12], had previous treatment with rhGH, a history of substance abuse, or a history or presence of a severe mental illness, or at the discretion of the investigator.
After informed consent was obtained, demographics and baseline medical history were collected, and a baseline psychological examination was performed. Eligible participants returned for a baseline (Week 0 visit). Participants were instructed to self-administer the test supplement daily 30 minutes prior to bedtime on an empty stomach. The test supplement consists of 4 capsules containing 2.9 g of blended (listed in descending order) L-lysine, L-arginine, oxo-proline, N-acetyl-l-cysteine, L-glutamine, and Schizonepeta tenuifolia) and is commercially available (Basic Research, LLC). For participants who purchased the test supplement, the investigator verified that participants purchased the correct product. For participants who were financially limited and unable to purchase the amino acid-based blend, samples were provided by Sierra Research Group, LLC. No compensation was given for participation in the study.
All visits (Weeks 0, 6, 12, 18, and 24) were conducted in the morning and participants were instructed to be fasted (except for water) from 9:00pm the preceding evening. At each visit, compliance with the study protocol was assessed by the investigator by asking open ended questions about taking the test supplement, the assigned diet, an exercise program, blood draws were conducted for assessment of IGF-1, insulin-like growth factor binding protein-3 (IGFBP-3), fasting lipid panel (total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides), fasting glucose, and HbA1c and participants completed the Revised Fibromyalgia Impact Questionnaire (FIQR) and Perceived Stress Scale (PSS). Adverse events were collected at each visit.
Before entering the study, participants were receiving standard care for fibromyalgia at the Private Practice of Susan Pekarovics, MD. Standard care for fibromyalgia consists of symptom management and primarily includes medication, physical therapy, dietary advice (28 kcal/kg/day), exercise advice (walk 45 minutes/day). Study procedures were conducted during standard care visits. Evaluation of symptoms was conducted at each visit and changes to standard care were made at any time during the study at the discretion of the Investigator. Participants could withdraw from the study at any time for any reason and receive standard care for fibromyalgia at the Private Practice of Susan Pekarovics, MD.
## Outcomes
The primary endpoint was the change from baseline (Week 0) to endpoint (Week 24) in serum IGF-1 (Quest Diagnostics, Los Angeles, CA). Additional endpoints included the change from baseline to Week 24 in body weight, and fibromyalgia symptoms (assessed with the FIQR) and stress symptoms (assessed with the PSS). The FIQR is a 21-item self-report measure that estimates the severity and impact of FM [20]. Score ranges from 0-100 with higher scores indicating greater severity/impact of FM. The PSS is a 10-item self-report measure that assesses the perception of stress [21]. Total score ranges from 0-40 with higher scores indicating greater perceived stress. Other outcomes included the change from baseline to Week 24 in IGFBP-3, body weight, systolic and diastolic blood pressures, HbA1c, and fasting lipids (Quest Diagnostics, Los Angeles, CA). Adverse events and safety were monitored by the Investigator who evaluated medical history, exploring the patients for any new complaints, and performed physical exams to detect any pathological changes, asked open-ended questions related to tolerability, and reviewed vital signs and laboratory markers.
## Statistical analysis
A paired t-test was used to compare changes from baseline to endpoint (Week 24). Data are presented as mean ± SD or mean ± SE. $p \leq 0.05$ is considered statistically significant.
## Results
The study enrolled 84 participants (56 female, 28 male) and all completed 24 weeks of treatment. The mean ± SD baseline age was 67 ± 11 years. Consistent with entry criteria, mean ± SD serum IGF-1 was 107 ± 43 ng/mL. The relatively high mean ± SD baseline FIQR score of 76 ± 16 and PSS score of 32 ± 5 reflect poor to moderate control of fibromyalgia symptoms [20, 21]. Baseline cardiometabolic markers indicate slightly elevated blood pressure, moderate dyslipidemia, and impaired fasting glucose (Table 1).
**Table 1**
| Unnamed: 0 | Baseline | Week 24 | Change | Mean 95% CI |
| --- | --- | --- | --- | --- |
| Body weight (kg) | 85 ± 19 | 79 ± 19 | -5.5 ± 0.3*** | -6.1, -5.0 |
| IGF-1 (ng/mL) | 107 ± 43 | 135 ± 41 | 28.4 ± 3.0*** | 22.3, 34.6 |
| IGFBP-3 (ng/mL) | 3.8 ± 1.3 | 4.7 ± 6.9 | 0.9 ± 0.7 | -0.5, 2.4 |
| FIQR total score | 76 ± 16 | 46 ± 16 | -29.1 ± 1.1*** | -31.2, -27.0 |
| PSS total score | 32 ± 5 | 12 ± 7 | -20.0 ± 0.8*** | -21.6, -18.5 |
| Systolic blood pressure (mm Hg) | 133 ± 11 | 113 ± 15 | -20.6 ± 1.7*** | -23.9, -17.2 |
| Diastolic blood pressure (mm Hg) | 84 ± 11 | 73 ± 7 | -11.1 ± 1.2*** | -13.4, -8.7 |
| HbA1C (%) | 7.0 ± 1.8 | 6.2 ± 1.1 | -0.8 ± 0.1*** | -0.9, -0.6 |
| LDL cholesterol (mg/dL) | 132 ± 45 | 101 ± 30 | -32.2 ± 3.2*** | -38.5, -25.9 |
| HDL cholesterol (mg/dL) | 52 ± 19 | 60 ± 19 | 8.1 ± 0.6*** | 6.9, 9.2 |
| Triglycerides (mg/dL) | 203 ± 124 | 129 ± 42 | -73.7 ± 11.4*** | -96.3, -51.1 |
There was an increase from baseline in mean IGF-1 levels that peaked at Week 12 remained elevated for the remainder of the study (Figure 1). At Week 24, the mean ± SE increase from baseline in serum IGF-1 was 28.4 ± 3.9 ng/mL ($p \leq 0.001$). IGFBP-3 levels were unchanged from baseline (mean ± SE change: 0.9 ± 0.7, p=ns).
**Figure 1:** *Mean serum levels of IGF-1 by visit. Data are mean ± SE by visit for all participants (N=84). ***p<0.001 for baseline vs endpoint.*
There was a progressive reduction from baseline in mean body weight that continued for the duration of the study (Figure 2). At Week 24, the mean ± SE change from baseline in body weight was -5.5 ± 0.3 kg ($p \leq 0.001$). This was a mean weight loss of $6.5\%$, a clinically significant weight reduction.
**Figure 2:** *Mean body weight by visit. Data are mean ± SE by visit for all participants (N=84). ***p<0.001 for baseline vs endpoint.*
The mean change in FIQR score from baseline to endpoint was statistically significant (Table 1, Figure 3). At baseline, the mean ± SD PSS score was 32 ± 5 and the mean ± SE change from baseline in PSS score was -20.0 ± 0.8 ($p \leq 0.001$).
**Figure 3:** *Change from baseline to Week 24 in FIQR (A) and PSS Scores. (B) Data are mean ± SE by visit for all participants (N=84). FIQR, Revised Fibromyalgia Impact Questionnaire; PSS, Perceived Stress Scale. ***p<0.001 for baseline vs endpoint.*
Statistically significant improvements from baseline to Week 24 were observed in all cardiometabolic measures: systolic and diastolic blood pressure, HbA1c, triglycerides, LDL-cholesterol, and HDL-cholesterol (Table 1, Figure 4).
**Figure 4:** *Change from baseline to Week 24 in systolic BP (A), in diastolic BP (B), in triglycerides (C), in LDL cholesterol (D), and HDL cholesterol (E), as cardiometabolic markers. Data are mean ± SE by visit for all participants (N=84). BP, blood pressure. ***p<0.001 for baseline vs endpoint.*
The supplement was well tolerated. No participants withdrew from the study early and no adverse events were reported. There were no adverse changes in vital signs, laboratory markers, or physical examinations.
## Discussion
In individuals with suboptimal control of fibromyalgia and low-normal IGF-1 levels, the supplement resulted in an increase in IGF-1 levels from baseline. Additional improvements were observed in body weight, fibromyalgia and stress symptoms, blood pressure, A1C, and lipids. All individuals were receiving treatment for fibromyalgia that adhered to standard of care guidelines [1, 6]. There was a 2:1 ratio of female to male participants, which is consistent with the incidence of fibromyalgia [1].
Human growth hormone (hGH) is primarily known for its vital role in the tissue and bone growth but also influences sleep, stress, pain, mood, and quality of life (1–3, 22, 23). hGH production and release from the pituitary is primarily stimulated by growth hormone releasing hormone (GHRH) and inhibited by somatostatin. The majority of hGH release occurs at the onset of slow wave sleep when GHRH levels are elevated and somatostatin tone is low [22, 23]. hGH levels are highest during puberty and decline with age [24, 25]. Low levels of hGH and IGF-1 are well documented in individuals with fibromyalgia [12, 13, 15, 26, 27]. In the approximately one-third of patients with fibromyalgia and GHD, treatment with rhGH is appropriate and has been demonstrated to improve clinical symptoms [15, 26]. In women with fibromyalgia and GHD, improvements in fibromyalgia symptoms and tender point scores were observed approximately 6 months after beginning treatment with rhGH [15]. One study examined the effect of treatment with a low dose of rhGH on fibromyalgia outcomes in women with fibromyalgia and low to moderate hGH levels (defined as IGF-1 levels below 150 ng/mL at baseline) [16]. Treatment with a low dose of rhGH for up to 12 months resulted in an increase in IGF-1 and improvement in fibromyalgia impact scores. In our study, which included a similar patient population and baseline IGF-1 and fibromyalgia impact scores as reported by Cuatrecasas et al. [ 16], an increase in IGF-1 was observed, as well as improvements in the FIQR and FIQR subscales. Thus, our results are consistent with previous beneficial reports of the effects of hGH replacement in individuals with fibromyalgia and low IGF-1.
Fibromyalgia is often comorbid with obesity and metabolic perturbations, such as insulin resistance and elevated leptin [28, 29]. At baseline, the mean HbA1c of the population was $7.6\%$, indicating elevated fasting blood glucose. The population also exhibited dyslipidemia and slightly elevated blood pressure. After 24 weeks of administration of the supplement, there was weight loss of nearly 6 kg (approximately 1 kg per month), a reduction in mean HbA1c of $0.8\%$, and statistically significant improvements in systolic and diastolic blood pressure and lipids. The high baseline HbA1c levels and dyslipidemia observed in our study is consistent with the known inverse correlation between low IGF-1 levels and insulin resistance [30] and metabolic effects of IGF-1 [31]. Furthermore, improvements in cardiometabolic parameters such as HbA1c, blood pressure, and lipids, is known to be associated with restoring IGF-1 levels to physiological levels in individuals with low IGF-1 [32]. Although the cause of weight loss and improvement in cardiometabolic parameters requires further study, they are consistent with normalization of IGF-1 levels [31, 32], reduction in stress, and the known benefits of weight loss [33, 34] in this population.
Stress in among the most common psychological features of fibromyalgia. During chronic stress, elevated corticotropin-releasing hormone increases hypothalamic somatostatin tone [11, 23]. Increased somatostatin tone has been observed in fibromyalgia and modulates the stress-induced suppression of pituitary hGH release (11, 12, 27, 35–39). The PSS reflects dynamic changes in stress [40]. *In* general, individuals with fibromyalgia score higher on the PSS and reductions in PSS score are associated with an improvement in fibromyalgia symptoms and impact [21, 40, 41]. This is consistent with the high baseline PSS score and reduction in PSS score that was observed in the study. Thus, our results indicate that there was an improvement in perceived stress over the course of the study. The reductions in perceived stress observed in this study may be attributed to the known effects of some amino acids to reduce somatostatin tone under certain conditions (23, 42–44).
Interpretation of the results of this study is limited by the lack of a placebo comparator group. However, our study is unique in that all the individuals were already receiving treatment for fibromyalgia by the study investigator at the study site prior to enrolling in the study. There were no changes to the standardized care upon the start of the study except for the addition of the test supplement to their existing treatment. Standard care consisted of office visits every 6 weeks. At these visits, blood draws were performed for IGF-1, IGFBP-3, fasting lipid panels, fasting glucose, and HbA1c and participants completed the FIQR and PSS questionnaires according to the study protocol. The patients also were asked about any adverse events, new symptoms, and the list of their medications. Next, they received a physical exam, including measuring their vital signs. At the end of each visit the patients were evaluated for their compliance with the supplement, their assigned diet and exercise. Care and symptom management continued as usual upon entering the study. Notably, exercise and dietary advice were already a key component of the standard care being provided to all the patients at the study site prior to entry in this study and these recommendations were not changed upon entering or during the study. Whether the increase in IGF-1 and other benefits observed in this study cohort are sustained beyond 24 weeks remains to be determined. Further randomized clinical trials would be necessary with larger samples and using a control group in order to obtain a more robust inference from the results.
Due to the complicated etiology and advanced age of many individuals with fibromyalgia, and weight gain there is a need for therapies for fibromyalgia and obesity that are both safe and effective as an additional part of a diverse treatment plan. In addition, considering some similar features between fibromyalgia and prolonged critical illnesses, long-COVID, and post-ICU syndrome, a coordinated multidisciplinary effort to share insights by the research and clinical community could be more successful in tackling the challenges of fibromyalgia therapy. The hGH enhancing effects of the test supplement represents a potential low-risk and cost-effective treatment to amplify endogenous hGH and improve clinical symptoms, benefitting individuals with low-normal hGH such as fibromyalgia, especially as this population includes elderly patients where the risk/benefit ratio is of substantial concern. Future studies should be conducted for repurposing sustained augmentation of IGF-1 with the supplement in weight control, and to assess its benefits in otherwise healthy individuals with obesity and low-normal hGH.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Argus IRB. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SP was the Primary Investigator of this study and the primary author of this manuscript. AB was contributing to collecting and organizing the data, analyzing the results, and composing the manuscript. CK was assisting with statistical analysis of the results. SB and AH were consulting on literature review and background information, study design, interpretation of the results, and composing the discussion part of this manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
AH was employed by Sierra Research Group and some samples of the test supplement were provided by Sierra Research Group. Authors SP and AB were employed by Professional Medical Corporation. Author CK was employed by Kelly Statistical Consulting.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Investigating the change in gene expression profile of blood mononuclear cells
post-laparoscopic sleeve gastrectomy in Chinese obese patients
authors:
- Na Liu
- Xiaolei Chen
- Jianghua Ran
- Jianhui Yin
- Lijun Zhang
- Yuelin Yang
- Jianchang Cen
- Hongmei Dai
- Jiali Zhou
- Kui Gao
- Jihong Zhang
- Liyin Liu
- Zhiyuan Chen
- Haibin Wang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10043499
doi: 10.3389/fendo.2023.1049484
license: CC BY 4.0
---
# Investigating the change in gene expression profile of blood mononuclear cells post-laparoscopic sleeve gastrectomy in Chinese obese patients
## Abstract
### Background
Laparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.
### Methods
Peripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People’s Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-*Seq data* were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.
### Results
We identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.
### Conclusion
This study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.
## Introduction
Morbid obesity is a chronic disease threatening human health and life all over the world. Nowadays, obesity is increasing rapidly, leading to an outbreak trend [1, 2]. Over the past few decades, the overweight and obese population has grown significantly in most Asian countries [3]. The World Health Organization (WHO) report of 2016 stated that 650 million adults and more than 340 million children and adolescents were overweight or obese [4]. Following the current trend will lead to half of the world’s adult population becoming overweight or obese by 2030 [5]. Obesity has a global impact on public health and the economy, posing a burden. According to recent estimates, the suggested cost of treating obesity-related comorbidities has reached a staggering $2 trillion, equivalent to 2.8 percent of the global GROSS domestic product [6].
The first-line treatment for obesity includes managing body weight. Maintaining long-term body weight loss is challenging, especially for severely obese patients who still struggle with the process despite lifestyle adjustments and drug therapy. Bariatric surgery might be the most sustainable and effective quality management strategy for treating severe obesity and its related metabolic diseases [7]. However, weight loss mechanisms and the subsequent metabolic improvement after bariatric surgery remain unclear. An in-depth study to identify the underlying mechanisms might be instrumental to fully understanding the pathogenesis of obesity and deciding on weight-loss operations. Bariatric surgery was initially thought to merely reduce energy intake. Progressive evidence shows that the cause of metabolic improvement after weight loss is a culmination of mutual influence and common changes. This process potentially changes the body’s absorption and intake, affecting the gastrointestinal hormone levels, gastrointestinal flora, and adipokines and regulating the central feeding system. Studies have shown that bariatric surgery reduces patients’ weight and improves the treatment of diabetes and cardiovascular disease, and even reduces cancer incidences and related risks [8, 9]. However, the mechanism underlying weight loss and metabolic improvement after weight-loss surgery is still unclear [10].
Several studies also reported inflammatory changes in adipose tissue and blood, particularly at early stages during the first year after surgery. RNA sequencing (RNA-seq) technology is a powerful and reliable tool for profiling gene expression [11], which plays an instrumental role in identifying several biological pathways affected by weight loss. This study analyzed expression profiles from RNA-seq data in peripheral blood from 10 obese patients undergoing bariatric surgery, whose gene expression levels were assessed before and one month after surgery. This approach provided insights into significant changes in transcriptome profiles and underlying molecular mechanisms disrupting biological pathways. Our study indicated that improving the inflammatory state after bariatric surgery might be caused by a break in the coexpression between inflammatory signaling pathways and a few crucial molecules involved in chemotaxis and activation of immune cells. So, this study potentially provides novel insights and identifies indicators of weight loss and metabolic improvement after bariatric surgery. The results would significantly contribute to understanding the etiology of obesity, diabetes, and other diseases and search for potential biomarkers for better therapy.
## Patient recruitment and clinical characteristics
According to the Chinese Guidelines for Surgical Treatment of Obesity and Type 2 Diabetes (2019 Edition), inclusion criteria for this study included indications for bariatric surgery, BMI ≥32.5 kg/m2. The exclusion criteria included a history of previous bariatric surgery, gastrectomy, substance abuse, uncontrolled mental illness, end-stage organ disease, or advanced cancer. Ten obese patients who received bariatric surgery in The Department of General Surgery of Kunming First People’s Hospital were randomly selected from January 2020 to January 2021. The clinical information is shown in Table 1. The age of the patients was 35 ± 15 years, with or without abnormal glucose metabolism. There was no significant difference in drug application status since they were not taking any that affected inflammatory pathways, glucose homeostasis, or fatty acid metabolism. Follow-up was performed one month after surgery. The Ethics Committee of the hospital approved this study. The ethical clearance numbe is YLS2020-45. All patients signed an informed consent form.
**Table 1**
| Gender | Age | Weight(kg) | Weight(kg).1 | BMI(kg/m2) | BMI(kg/m2).1 | FPG(mmol/L) | FPG(mmol/L).1 | Cortisol(nmol/L) | Cortisol(nmol/L).1 | ACTH(pmol/L) | ACTH(pmol/L).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Gender | Age | pre-LSG | post-LSG | pre-LSG | post-LSG | pre-LSG | post-LSG | pre-LSG | post-LSG | pre-LSG | post-LSG |
| F | 37 | 109 | 93 | 44.22 | 37.73 | 5.1 | 5.31 | 227.1 | 171.4 | 1.31 | 1.77 |
| M | 20 | 120 | 99 | 44.08 | 36.36 | 4.96 | 5.00 | 335 | 173.9 | 7.44 | 5.18 |
| M | 38 | 101 | 88 | 33.75 | 29.40 | 5.84 | 5.95 | 453 | 320.5 | 5.53 | 4.96 |
| F | 27 | 90.2 | 82 | 33.95 | 30.86 | 5.6 | 4.79 | 103.1 | 273.9 | 1.1 | 1.33 |
| F | 34 | 95 | 86 | 35.76 | 32.37 | 5.7 | 5.16 | 237.2 | 160.6 | 3.22 | 1.33 |
| F | 34 | 135 | 113 | 51.44 | 43.06 | 5.23 | 5.6 | 168 | 475.2 | 2.11 | 4.86 |
| F | 49 | 102 | 89 | 34.48 | 30.08 | 8 | 7.26 | 273.4 | 234.2 | 3.85 | 2.3 |
| F | 39 | 104 | 89.2 | 38.67 | 33.16 | 6.2 | 6.46 | 258 | 228.1 | 3.18 | 3.02 |
| F | 29 | 115 | 102 | 41.73 | 37.02 | 4.34 | 4.5 | 312 | 283.4 | 5.25 | 5.2 |
| F | 36 | 121 | 92 | 46.11 | 35.06 | 4.61 | 5 | 319 | 211 | 6.43 | 4.07 |
## High-throughput bulk RNA-seq
Total RNA was isolated and purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s procedure. The amount and purity of each RNA sample were quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). The RNA integrity was assessed by Bioanalyzer 2100 (Agilent, CA, USA) (RIN values greater than 7.0 met the requirements RIN number >7.0) and confirmed by electrophoresis with denaturing agarose gel. Poly (A) RNA was purified from 1 μg total RNA using Dynabeads Oligo (dT)25-61005 (Thermo Fisher, CA, USA) using two rounds of purification.
The poly (A) RNA was fragmented into small pieces using Magnesium RNA Fragmentation Module (NEB, cat.e6150, USA) for 94°C 5-7min. Then, the cleaved RNA fragments were reverse-transcribed to create the cDNA by SuperScript™ II Reverse Transcriptase (Invitrogen, cat. 1896649, USA), which were then used to synthesize U-labeled second-stranded DNAs with E. coli DNA polymerase I (NEB, cat.m0209, USA), RNase H (NEB, cat.m0297, USA) and dUTP solution (Thermo Fisher, cat. R0133, USA). An A-base was then added to the blunt ends of each strand, preparing them for ligating to the indexed adapters. Each adapter contained a T-base overhang for ligating the adapter to the A-tailed fragmented DNA. Single- or dual-index adapters are ligated to the fragments, and size selection was performed with AMPureXP beads. After the heat-labile UDG enzyme (NEB, cat.m0280, USA) treatment of the U-labeled second-stranded DNAs, the ligated products were amplified with PCR by the following conditions: initial denaturation at 95°C for 3 min; 8 cycles of denaturation at 98°C for 15 sec, annealing at 60°C for 15 sec, and extension at 72°C for 30 sec; and then final extension at 72°C for 5 min. The average insert size for the last cDNA library was 300 ± 50 bp. Finally, we performed the 2×150 bp paired-end sequencing (PE150) on an Illumina Novaseq™ 6000 (LC-Bio Technology CO., Ltd., Hangzhou, China) following the vendor’s recommended protocol.
## Weighted gene coexpression network analysis
We used the R package WGCNA (Version 1.70-3) [11] to detect gene coexpression modules associated with a cohort of obese patients before and after receiving laparoscopic sleeve gastrectomy (LSG) treatment. Sample clustering helped to detect outliers and to match samples to their features. Module-trait correlation analysis was applied to detect coexpression modules with the highest correlation to clinical features. Correlation analysis of gene significance (GS) and module membership (MM) helped identify the hub genes; | MM| > 0.8 and |GS| > 0.2 were the thresholds. The higher the correlation between GS and MM, the greater the importance of the gene for the trait and the module [12].
## Differential analysis
Differential expression analysis was performed to identify the differentially expressed mRNAs (DE-mRNAs) between preoperative and postoperative samples using the R package limma (Version 3.46.0) [13]. The selection criteria for significantly differentially expressed mRNAs were |log2 fold change (FC)| > 0.5 and $P \leq 0.05.$
## Screening for signature genes
The common genes were identified via overlapping hub genes and DE-mRNAs. Subsequently, the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-REF) algorithms were conducted on these common genes, respectively. LASSO Cox regression using the R package glmnet (Version 4.0-2), with penalty parameters, was estimated by 10-fold cross-validation [14]. The SVM-REF [15] algorithm analysis was performed based on the R package el070 (Version 1.7-4). The LASSO and SVM-REF models were assessed using receiver operating characteristic (ROC) curves. The signature genes were screened by comparing and selecting the common output of the LASSO model and SVM-REF algorithms. ROC curves were plotted to evaluate diagnostic values of signature genes.
## Single-sample gene set enrichment analysis
Single-sample GSEA was performed on signature genes. The ‘c2.cp. Kegg.v7.2.symbols.gmt’ were downloaded from the Molecular Signature Database (MSigDB, http://www.gsea-msigdb.org/gsea/msigdb/) and used as the predefined gene set. We calculated the correlations of the signature genes with all other genes separately and ranked their correlations from highest to lowest. The KEGG (Kyoto Encyclopedia of Genes and Genomes) gene sets were deployed as a test set to detect the enrichment of signaling pathway in them. Pathways that met all three conditions simultaneously were identified as significant, |normalized enrichment score (NES)| ≥ 1, NOM p-val < 0.05, and FDR q-val < 0.25.
## Construction of competitive endogenous RNA network
We constructed a regulatory network of DE-lncRNAs-miRNAs-signature genes based on the identified signature genes. The reciprocal microRNAs (miRNAs) of the signature genes were predicted by the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/). Subsequently, lncRNAs interacting with the above miRNAs were retrieved from the starBase2.0 database (http://starbase.sysu.edu.cn/index.php). Then, the DE-lncRNAs were selected from the retrieved lncRNAs that were consistent with the expression trends of the signature genes to be consistent with the ceRNA mechanism. The ceRNA network was visualized by the R package Cytoscape.
## Detection of leptin and adiponectin levels
Around 5 ml venous blood was collected from patients after fasting and centrifuged at 1000 g for 10 min. The plasma was separated and kept at -80°C. A double antibody sandwich ELISA(Wuhan MSK Biotechnology Co. LTD) was used to detect the plasma levels of leptin and Adiponectin.
## Statistical analysis
Correlation coefficients of the signature genes with leptin and lipocalin were calculated by Pearson correlation analysis, where the Wilcoxon rank-s test compared the changes in leptin and lipocalin in patients before and after LSG. Ingenuity Pathway Analysis (IPA) revealed the diseases and functions involved in DE-mRNAs and demonstrated the interaction network of signature genes. Overlap analysis was performed using the online tool Jvenn (http://jvenn.toulouse.inra.fr/app/example.html). All analyses and statistics were performed in the R software. If not specified above, $p \leq 0.05$ was regarded as statistically significant.
## Identification of hub genes related to obese patients treated with LSG
We performed WGCNA using second-generation sequencing data from ten obese patients before and after LSG, after removing one obvious outlier (No. LR21E26DX40, obese sample before receiving LSG treatment; Supplementary Figure 1A). A soft threshold = 14 was chosen to construct a scale-free network (Supplementary Figures 1B, C). Similar modules, segmented by the dynamic tree-cutting algorithm, were subsequently merged according to MEDissThres=0.15 (Supplementary Figures 1D, E), resulting in 26 modules (Figures 1A, B). Our intention to annotate the phenotypes of the modules led us to jointly analyze the two features (pre- and postoperative) and all the genes with the modules in the heatmap (Figure 1C). The brown saddle module had the highest correlation with postoperative obese patients (cor = -0.58, $$P \leq 0.01$$). At the same time, this module was the most highly correlated with preoperative (cor = 0.58, $$P \leq 0.01$$) (Figure 1C). Subsequently, 91 out of 163 genes within this module with correlation cutoff values of |MM| > 0.8 and |GS| > 0.2 were selected as hub genes based on the analysis of MM of saddle brown modules for GS (Figure 1D; Supplementary Table 1).
**Figure 1:** *Identification of 91 hub genes related to obese patients treated with LSG by weighted gene coexpression network analysis (WGCNA). (A) Sample clustering with preoperative and postoperative external traits. (B) Dendrogram of the WGCNA modules. (C) The relationship between coexpression modules and external clinical traits and the saddle brown module was considered most relevant to clinical characteristics (cor = 0.58, p = 0.01). (D) Scatter plot of module eigengenes in the saddle brown module, the genes with |MM| > 0.8 and |GS| > 0.2 were selected as hub genes (cor = 0.23, p = 0.0.003). The horizontal axis represents the absolute value of the correlation between genes and modules, and the vertical axis represents the absolute value of the correlation between genes and traits.*
## Identification of DE-mRNAs after LSG in obese patients
We used the R package limma to identify 165 DE-mRNAs between nine preoperative and ten postoperative samples (Supplementary Table 2). Among them, 118 mRNAs were upregulated, and 47 were down-regulated (Figures 2A, B). Further, we performed a functional enrichment analysis to reveal the potential functions of these DE-mRNAs (Supplementary Table 3).
**Figure 2:** *Identification of 165 differentially expressed mRNAs (DE-mRNA) and functional enrichment analysis. (A) Volcano plot of DE-mRNAs with |log2 fold change (FC)| > 0.5 and p < 0.05. Red represented upregulated genes, and green indicated down-regulated genes. (B) Heat map of DE-mRNAs.*
## Identification and evaluation of key biological indicators in obese patients
Overlap analysis yielded eighteen common genes from the hub genes and DE-mRNAs (Figure 3A; Supplementary Table 4). Moreover, GO-BP revealed the eighteen common genes linked to migration, differentiation, and regulation of chemotaxis of immune cells. They were also involved in the inflammatory response, immune response, lipid storage, and localization. Moreover, these genes played essential roles in the molecular functions of chemokine and cytokine activity and receptor binding (Figure 3B; Supplementary Table 5). KEGG analysis indicated that the ‘NF-kappa B signaling pathway’, ‘C-type lectin receptor signaling pathway’, and ‘TNF signaling pathway’ were the three most relevant pathways (Figure 3C; Supplementary Table 6).
**Figure 3:** *Identification of 18 common 28 bariatric surgery-related genes and functional enrichment analysis of these genes. (A) The Venn diagram of 18 common genes 28 key biological indicators between 91 hub genes and 165 DE-mRNAs. (B) Gene Ontology (GO) enrichment analysis of bariatric surgery-related genes. (C) Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis of bariatric surgery-related genes.*
We then used two machine learning algorithms, LASSO and SVM-REF, respectively, to further narrow down the biomarkers. Eighteen common genes were subjected to LASSO Cox regression analysis to calculate the regression coefficients. The coefficients for each gene in obese patients are shown in Figures 4A, B. The LASSO analysis identified three signature genes (IRF1, NFKBIA, and YRDC) and incorporated them into the classifier, the efficacy of which was evaluated by ROC analysis. The AUC showed that the LASSO algorithm constructed a classifier with an accuracy of up to 0.950 (Figure 4C). Moreover, from the eighteen common genes, SVM-REF identified eight as representative feature genes with a minimum generalization error of 0.1 and a maximum accuracy of 0.9 (Figure 4D). Ten-fold cross-validation revealed these eight genes to be IRF1, YRDC, NLRP3, EGR3, FCAR_2, CCL4L1, ZFP36, and NFKBIA. Subsequently, we crossed the feature genes screened by both algorithms and obtained IRF1, NFKBIA, and YRDC as the final feature genes. The performance evaluation analysis of individual feature genes showed their capability to effectively distinguish between pre-LSG and post-LSG obese samples (Figure 4E). Moreover, the logistic regression model constructed based on the three final signature genes exhibited robust discriminatory validity (Figure 4F). Furthermore, we also predicted the interplay network of three biomarkers by IPA (Supplementary Figure 2).
**Figure 4:** *Identification of three signature genes. (A) LASSO Cox regression analysis to shrink estimated coefficients to zero. (B) Cross-validation was used to choose an optimal λ value in the LASSO selection diagram. The two dotted lines indicated two particular values of λ. The left side was λmin, and the right side was λ1se. The λmin was selected to build the model for accuracy in our study. (C) ROC analysis was adopted to assess the efficacy of the LASSO regression analysis results model (AUC = 0.950). (D) Plots of eight feature genes selection by SVM-REF. (E) ROC curve of three signature genes. (F) The ROC curve for L is the logistic regression model of three signature genes (AUC = 1).*
## Analysis of potential pathways regulated by the three biomarkers
We next performed ssGSEA in the R package GSVA to explore the relevant pathways regulated by the three biomarkers. For IRF1, a total of 94 KEGG pathways were enriched, of which 57 were enriched in genes positively associated with IRF1 (NES > 1) and 37 were enriched in genes negatively associated with it (NES < -1) (Figure 5A; Supplementary Table 7); NFKBIA was enriched in a total of 95 KEGG pathways, of which 55 were enriched in genes positively associated with NFKBIA and 40 in genes negatively associated with it (Figure 5B; Supplementary Table 8); A total of 68 KEGG pathways were enriched in YEDC, with 50 pathways enriched for genes positively associated with YEDC and eighteen for genes negatively associated it (Figure 5C; Supplementary Table 9).
**Figure 5:** *Single-sample Gene set enrichment analysis (ssGSEA) of three biomarkers. (A) Enrichment analysis of IRF1 single gene GSEA. (B) Enrichment analysis of NFKBIA ssGSEA. (C) Enrichment analysis of ssGSEA in YRDC. The screening criteria were set as |normalized enrichment score (NES)| ≥ 1, NOM p-val < 0.05, FDR q-val < 0.25.*
Comprehensive analysis showed all three biomarkers involved in viral infection-associated diseases such as ‘Herpes simplex virus 1 infection’, ‘Kaposi sarcoma-associated herpesvirus infection’, ‘*Human cytomegalovirus* infection’, ‘Influenza A’, and ‘Hepatitis C’. Besides, they were associated with immune and inflammatory responses (‘TNF signaling pathway’, ‘IL-17 signaling pathway’, ‘Th17 cell differentiation’, ‘Allograft rejection’, ‘Th1 and Th2 cell differentiation’, etc.). Three biomarkers were significantly enriched in fatty acid metabolism and degradation pathways. Therefore, we hypothesized that the three biomarkers might be involved in the successful weight loss outcome of LSG-treated obese patients by regulating fatty acid metabolism and degradation processes.
The above results led us to explore the changes in leptin and lipocalin in patients before and after LSG. The results showed that LSG significantly decreased leptin levels in patients upon receiving LSG treatment (Figure 6A), corroborating a previous study [16]. Next, Pearson correlation analysis indicated a significant negative correlation between NFKBIA and leptin (cor = -0.48, $$P \leq 0.038$$; Figure 6), suggesting that the up-regulation of NFKBIA might decrease leptin levels in patients treated with LSG.
**Figure 6:** *Pearson correlations analysis. (A) Box plot of Leptin and Adiponectin expression in patients before and after LSG treatment pre- and post-operation (Wilcoxon rank-sum test). *, p < 0.05. (B) Pearson correlation analysis of key characteristic genes with leptin and Adiponectin (cor = -0.48, P = 0.038).*
## A preliminary investigation of biomarker-based ceRNA mechanisms
We identified 20 upregulated, and two downregulated lncRNAs from obese samples before and after receiving LSG treatment (Supplementary Table 9). A ceRNA network based on three biomarkers was subsequently constructed using miRWalk and starBase databases (Figure 7; Supplementary Table 10). The network included 14 nodes (1 lncRNA, ten miRNAs, and three biomarkers) and 17 edges. The network revealed the regulation of IRF1 by lncRNA ATP2B1-AS1 through the competitive binding of 4 miRNAs (hsa-miR-6509-5P, hsa-miR-330-5p, hsa-miR-154-5p, and hsa-miR145-5P). lncRNA ATP2B1-AS1 modulated NFKBIA expression through the competitive binding to hsa-miR4726-5P, since YRDC regulation was achieved by competitive binding of lncRNA ATP2B1-AS1 to hsa-miR4726-5P and/or hsa-miR-134-5p.
**Figure 7:** *The ceRNA regulatory network of three biomarkers CeRNA network. Red rectangles represent three biomarkers, the yellow diamond represents the lncRNA, and blue circles represent the miRNAs.*
## Discussion
Morbid obesity is a severe threat to human health, causing debilitating outcomes. Recently, bariatric surgery has emerged as the most effective treatment for morbid obesity [8]. Laparoscopic sleeve gastrectomy (LSG) is now the most-performed bariatric procedure worldwide within academic centers [17]. However, the critical variables associated with weight loss and metabolic improvement after bariatric surgery remain unclear.
This study detected the differential expressions of the critical biomarkers, IRF1, NFKBIA, and YRDC genes in obese patients after LSG. The enriched pathways associated with these genes included the metabolism and degradation of fatty acids. Our results suggested IRF1, NFKBIA, and YRDC could potentially help in successful weight loss in obese patients treated with LSG. Interferon regulatory factor (IRF) is a transcription factor that regulates the expression of interferon genes. The IRF1 expression has been implicated in the adipocyte inflammatory processes [18]. The YRDC is widely expressed in humans, but its biological function is unknown in obese patients. NFKBIA is an inhibitor regulating the movement of the transcription factor NF-κB into and out of the nucleus. It also improves glucose and lipid metabolism in postoperative obese patients which has regulatory effect on diabetes [19, 20]. Moreover, the NFKBIA gene might be closely related to postoperative weight loss and improved glucose and lipid metabolism in obese patients.
Our enrichment analyses revealed that the immune cells were closely linked to three characteristic genes and eighteen DE-mRNAs common genes. Changes in the immune micro-environment might play an essential role in obese patients post-LSG. Whole blood enrichment analysis showed that the gene expression had also changed early at one-month post-LSG. Transcriptomic changes occur in inflammatory cytokines and the metabolic pathways associated with energy levels [21]. Obesity regulates immune cells in adipose tissue [22]. Adipose tissue is a vital immune organ that stores fat and provides energy [23]. Obesity changes the immune cell landscape in adipose tissues, causing the pro-inflammatory cells and CD8+T cells to gradually dominate [24, 25]. This phenomenon results in insulin resistance and other related metabolic diseases [26]. Obesity also leads to chronic low-grade inflammation and changes in the immune environment of multiple organs [27]. Bariatric surgery leads to improvement in obesity-associated comorbidities by decreasing adipose tissue inflammation. The improved inflammatory state following surgery might be explained by the disruption of immuno-inflammatory cascades involving a few crucial molecules which could serve as potential therapeutic targets [28]. Obesity is related to cancer risk, with weight loss having a protective function. Cancer causes chronic low-grade inflammation and the disorder of the immune environment [29]. A study on obese women undergoing bariatric surgery or a medically supervised low-calorie diet showed how weight loss reduced systemic inflammation and recruitment of protective immune cell types to the endometrium. The study also highlighted how weight loss critically prevented endometrial cancer [30]. LSG improves patients’ obesity and related complications by altering the immune microenvironment and energy metabolism pathway.
Competitive endogenous RNA (ceRNA) networks reveal the mechanism of interaction between RNAs and play crucial roles in multiple biological processes. Mounting evidence shows that long non-coding RNAs (lncRNAs) essentially modulate the biological process of diabetic retinopathy (DR). Moreover, it has been demonstrated that ATP2B1-AS1 acts as a miR-4729 sponge to regulate the expression of IQGAP2, reducing high-glucose-induced endothelial dysfunction in DR. This phenomenon has tumor suppressive effects in many cancers, controlling endothelial cells dysfunction [31]. Our study identified 20 upregulated, and two downregulated lncRNAs from obese samples before and after receiving LSG treatment; a lncRNA–miRNA–mRNA ceRNA network was constructed with one lncRNA, ten miRNAs, and three biomarkers. In the ceRNA network, both the three signature genes were regulated by lncRNA ATP2B1-AS1. Silencing ATP2B1-AS1 protects mice from myocardial infarction by blocking the NFKBIA mediated NF-κB signaling pathway [19]. The IKKβ/NF-κB pathway essentially regulates the inflammatory response and has also recently been implicated in insulin resistance. Alternatively, a reduction in IκBα might cause an increased translocation of NF-κB into the nucleus, increasing the transcription of several inflammatory cytokines, such as tumor necrosis factor-α, associated with insulin resistance. These results confirm that the NF-kappaB/IKKbeta pathway may mediate human obesity-induced insulin resistance. They might serve as diagnostic and prognosis markers as well as therapeutic targets.
Leptin and Adiponectin are two important adipose-regulatory factors in obesity, regulating sugar, fat, and energy metabolism. Leptin inhibits the synthesis of adipose cells, inhibiting appetite and reducing weight. Leptin resistance is a characteristic of human obesity [32]. Pathologically, leptin resistance and decreased sensitivity to leptin receptors depolarize islet β cells and promote insulin secretion, resulting in hyperinsulinemia and type 2 diabetes. Moreover, plasma leptin levels are increased in obese type 2 diabetes patients, significantly post-LSG, which indicates the potential of LSG in modifying the conditions of type 2 diabetes by reducing the level of leptin [33]. Adiponectin increases insulin sensitivity. Hypoadiponectin was closely related to insulin resistance in obese people; decreasing weight was accompanied by increased adiponectin levels [34], which were negatively correlated with BMI, body fat percentage, waist-to-hip ratio, fasting insulin level, and two hours postprandial blood glucose level, and positively correlated with insulin sensitivity [35].
Our results showed leptin concentration significantly decreased in postoperative patients, while NFKBIA expression level was significantly correlated with leptin. This observation suggested that LSG could reduce leptin levels to improve the postoperative metabolic level of obese patients, leading to increased NFKBIA expression, lowering insulin resistance, and improving insulin sensitivity to improve blood glucose. However, the regulation mechanism of leptin and NFKBIA is still unknown, warranting further systematic and in-depth studies.
Metabolic surgery causes mutual influences and changes in treating obesity [36], reducing the body’s absorption and intake and regulating the level of gastrointestinal hormones, gastrointestinal flora, adipokines, and the central feeding system. Importantly, candidate prognostic biomarkers involved in the ceRNA network were screened out. These biomarkers exhibited essential roles as therapeutic targets and in prognosis analysis in obese patients. Our results also showed that leptin levels decreased significantly after surgery, which may be related to the up-regulation of NFKBIA. However, the regulation mechanism of these two factors needs to be explored in future studies.
## Limitations
There are several limitations of this study. First, the small sample size, further validation in a large number of samples is required. Second, this was based on peripheral blood mononuclear cells which were readily available. Further work on actual adipose tissues is needed to verify expression of characteristic genes. Finally, animal studies would be useful to confirm function of the identified genes.
## Data availability statement
The datasets have been deposited under BioProject ID PRJNA861382.
## Ethics statement
The studies involving human participants were reviewed and approved by Medical Ethics Committee of the First People’s Hospital of Kunming. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
All authors contributed toward data analysis, drafting, and critically revising the paper and agreed to be accountable for all aspects of the work.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1049484/full#supplementary-material
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|
---
title: 'Emotion regulation and virtual nature: cognitive reappraisal as an individual-level
moderator for impacts on subjective vitality'
authors:
- Annalisa Theodorou
- Giuseppina Spano
- Gregory N. Bratman
- Kevin Monneron
- Giovanni Sanesi
- Giuseppe Carrus
- Claudio Imperatori
- Angelo Panno
journal: Scientific Reports
year: 2023
pmcid: PMC10043509
doi: 10.1038/s41598-023-30287-7
license: CC BY 4.0
---
# Emotion regulation and virtual nature: cognitive reappraisal as an individual-level moderator for impacts on subjective vitality
## Abstract
People who make habitual use of an emotion regulation strategy such as cognitive reappraisal may be more sensitive to the emotion cues coming from a surrounding natural environment and, thus, get more benefits from virtual nature exposure such as enhanced subjective vitality. However, no previous study investigated the moderating role of cognitive reappraisal in the relationship between exposure to different types of natural environments (a national park, a lacustrine environment, and an arctic environment vs. an urban environment) and subjective vitality. We designed a between-subject design (four conditions, one per type of environment) with a sample of 187 university students (Mage = 21.17, SD = 2.55). Participants were exposed to four 360° panoramic photos of the environment for one minute each with a virtual reality head-mounted display. The results of a multicategorical moderation analysis attested that there were two significant interactions, respectively between lacustrine and arctic environments and cognitive reappraisal. More specifically, for participants with low levels of habitual use of cognitive reappraisal, the effects of virtual nature (vs. urban) exposure on subjective vitality were not significant, while for participants with high levels, the effects were significant and positive. Findings show how the potential of virtual nature may be boosted with training aimed at increasing the general use of cognitive reappraisal, supports enhancing the applications of virtual nature, and demonstrates the need to take individual differences into account when determining the benefits of these applications.
## Introduction
Nature exposure through virtual reality (VR) is considered to be particularly useful for individuals facing barriers or other difficulties in accessing in-vivo natural environments, such as populations with special needs, hospitalized patients, prisoners, and individuals in forced confinement1–3. Increasing evidence demonstrates the benefits of virtual green and blue environments for a variety of psychological outcomes, including state affect, self-regulation, reduced stress, increased nature connectedness, and enhanced perceived restorativeness4–9. This includes some emerging work on other virtual natural environments other than greenspace, including the stress-reducing effects of exposure to deserts10 in healthy adult volunteers from El Paso, TX, and the impacts of an arctic environment on depressive symptoms in a clinical sample of patients with spinal cord injury11.
Among the psychological outcomes investigated in this new research topic, very little is known about subjective vitality, defined as a positive feeling of aliveness and energy12. This is an important aspect of emotional well-being to consider in this research, as it represents the presence or increase of a positive affective outcome in contrast to the absence or decrease of a negative one (e.g., anxiety). Specifically, as a positive emotion characterized by high activation and the perception that this energy emanates from the self, it has been extensively studied in the realm of motivation12,13. Subjective vitality has been shown to be associated with several physical, behavioral, and health outcomes, such as weight loss, attention, productivity, and other aspects of affective well-being12,14–16. Thus, it seems relevant to investigate if a relatively accessible medium such as VR can sustain subjective vitality. A recent study17 explored the change in subjective vitality in a sample of participants exposed to a VR application. Participants were administered a scale on subjective vitality before and after being exposed to a detailed 3D virtual forest through a head-mounted display (HMD) and headphones for the reproduction of sounds. Results showed a significant improvement in the participants’ vitality after a stay of just 5 min in the virtual environment. In line with this, Reese, Stahlberg, and Menzel18 found a slight increase in this outcome after an immersive VR experience of approximately 7 min duration of a forest scene.
Despite the encouraging findings on the effect of virtual green environments, more evidence is needed on the potential effects of other virtual natural environments, e.g., blue and arctic environments, on subjective vitality. Some results have been found with respect to blue environments. One study suggests that an outdoor environment characterized by liquid water (i.e., a river) can enhance subjective vitality19 but studies on virtual blue spaces are needed. Second, results on the virtual arctic environment are scarce and have not tested its specific characteristics in comparison to other environments11, though it was recently suggested that environments characterized by solid water, such as arctic ones, may increase restoration through fascination and the experience of being away20. Restorativeness is associated with subjective vitality21,22 and is presumably one of its antecedents23. Given this, we believe that taking into account all of the environments in the same study might be important for comparatively evaluating the size of their effects.
Another under-investigated aspect to consider is the potential moderating role of individual characteristics (e.g., personality traits or disposition) on the relationship between different types of VR natural environments and subjective vitality6. One relevant characteristic may be individual-level differences in emotion regulation1,24–26. In particular, the emotion regulation strategy of cognitive reappraisal may be relevant in decoding environmental cues and taking advantage of the opportunities offered by the surrounding environment27, including those that derive from natural environments28,29.
Cognitive reappraisal is defined as an emotion regulation strategy that aims at reconstructing the meaning of a situation, thereby modifying its emotional impact27,30–32. Habitual use of cognitive reappraisal has been found to be positively associated with affective well-being outcomes, including increased positive emotions and subjective vitality27,33. A recent study on a sample of park visitors found that a higher level of habitual use of cognitive reappraisal was significantly and directly associated with pro-environmental behavior, and indirectly associated with this outcome through the experience of “being away”29. These results suggest that cognitive reappraisal can make individuals more sensitive to the natural environment, and thus potentially receive more affective benefits. Although virtual nature experiences have been shown to generate benefits that are smaller than in situ experiences in nature8, we predicted that VR nature exposures would increase subjective vitality more for participants with higher tendencies to cognitive reappraise in general.
## The present study
To the best of our knowledge, no previous study has investigated the moderating role of participants’ habitual use of cognitive reappraisal on the efficacy of virtual nature in enhancing subjective vitality. In this study, we hypothesize that virtual natural environments, namely a national park, a lacustrine environment, and an arctic environment would be significantly more efficient than a virtual urban environment in enhancing subjective vitality (hypothesis 1). Moreover, we hypothesize that these effects would be particularly high for those with high (vs. low) levels of habitual use of cognitive reappraisal (hypothesis 2).
To test our hypotheses, we conducted an experiment with four conditions (one per environment), measuring pre- and post-exposure levels of subjective vitality as well as the habitual use of cognitive reappraisal measured pre-exposure. Moreover, we included some control variables to assess if participants were similar among conditions. In particular, we measured: [1] sociodemographic variables (age, gender, educational qualification, marital status, and employment status), [2] personal conditions and individual differences that can interfere with virtual nature appreciation and subjective vitality levels (environmental identity and perceived stress in the previous month)34,35, [3] type of environment the participant lives in that may influence their perceptions or preference of nature, and hence the effects36, and [4] variables that may impact the VR experience itself (previous VR experience, the brightness of the images shown, sense of presence, and motion sickness4,6).
## Preliminary analysis
First, we compared the level of each control variable per condition to check whether the distribution of the participants was the same between groups. To this end, we performed a series of one-way ANOVAs and chi-squared tests. Regarding the main model variables, results of two one-way ANOVAs showed that participants did not differ per level of subjective vitality pre-exposure F[3, 183] = 1.05, $$p \leq 0.370$$, η2p = 0.017, nor per level of habitual use of cognitive reappraisal F[3, 183] = 0.94, $$p \leq 0.423$$, η2p = 0.015. Moreover, participants did not differ in other important characteristics such as sociodemographic variables. Indeed, a one-way ANOVA indicated that participants did not differ by age F[3, 181] = 1.60, $$p \leq 0.192$$, η2p = 0.026, while a series of chi-square tests revealed that the distribution of participants was balanced for gender χ2 [3, 185] = 0.47, $$p \leq 0.925$$, educational qualification χ2 [3, 187] = 5.95, $$p \leq 0.114$$, marital status χ2 [3, 187] = 3.53, $$p \leq 0.317$$, and employment status χ2 [3, 187] = 1.91, $$p \leq 0.591.$$ Participants were similar also in their levels of environmental identity F[3, 183] = 0.54, $$p \leq 0.653$$, η2p = 0.009, perceived stress F[3, 183] = 1.35, $$p \leq 0.259$$, η2p = 0.022, and equally distributed per type of environment they live in χ2 [3, 187] = 1.14, $$p \leq 0.767$$, and previous VR experience χ2 [3, 187] = 0.619, $$p \leq 0.892.$$ Lastly, our analysis revealed that participants reported similar experiences regarding some specifics of the VR exposures across conditions, as they rated similarly across conditions the brightness of the images shown F[3, 183] = 0.65, $$p \leq 0.587$$, η2p = 0.010, the sense of presence F[3, 183] = 0.67, $$p \leq 0.574$$, η2p = 0.011, and the motion sickness F[3, 183] = 1.57, $$p \leq 0.198$$, η2p = 0.025 experienced.
## Moderation analysis
We then proceeded to test our hypotheses. To this end, we conducted a moderation analysis with a multicategorical independent variable (i.e., the condition) using the IBM SPSS macro PROCESS37. In particular, we added the condition as the independent variable, the post-exposure subjective vitality as the outcome, and the general propensity to use cognitive reappraisal as the moderator. Following the recommendations for pre-post experimental designs38, we included the pre-exposure subjective vitality as a covariate. The estimated $95\%$ (percentile bootstrap) confidence intervals were based on a bootstrapping procedure with 5000 bootstrap samples.
According to the indications by Hayes and Montoya39 for a moderation analysis with a multicategorical independent variable, we set the condition variable as an indicator in PROCESS. Thus, the park condition, the lacustrine condition, and the arctic condition were coded as dummy variables with a value of 1 if a case was in that condition and 0 if otherwise. We set the urban condition as the reference (i.e., control) group. In this way, the urban condition received a code of 0 on the park condition, the lacustrine condition, and the arctic condition (see Table 1).Table 1The indicator coding system used for the multicategorical independent variable "condition". Urban condition (control)Park conditionLacustrine conditionArctic conditionPark condition0100Lacustrine condition0010Arctic condition0001 *As a* multicategorical moderation analysis, the results include estimated effects for each natural condition as compared to the reference group (i.e., the urban condition) on the outcome as well as their interaction effects with the moderator cognitive reappraisal. Because of our coding system, the estimated effects are the adjusted mean differences in the outcome between each natural condition and the reference group (i.e., urban), holding the covariate constant (i.e., at mean levels39).
The model explained a significant portion of the variance of the outcome R2 = 0.43, $F = 16.85$, $p \leq 0.001.$ As can be seen in Table 2, all three natural environments resulted in increases in subjective vitality vs. the urban environment. Additionally, two out of three interactions were significant, namely those of lacustrine and arctic environments with cognitive reappraisal. Simple slope analysis revealed that at low levels (− 1 SD) of cognitive reappraisal, both effects were nonsignificant (for the lacustrine condition: $b = 0.04$, SE = 0.26, $$p \leq 0.888$$, for the arctic condition: $b = 0.25$, SE = 0.24, $$p \leq 0.311$$), while, at high levels (+ 1 SD) they were both positive and significant (for the lacustrine condition: $b = 0.92$, SE = 0.23, $p \leq 0.001$, for the arctic condition: $b = 0.98$, SE = 0.26, $p \leq 0.001$). See Fig. 1 for a graphical representation of the simple slopes. Table 2Results of the moderation analysis including the main effects of each predictor, the interaction effects, and the effect of the covariate on subjective vitality.bSEb [$95\%$ CI]pPark condition0.380.18[0.04, 0.73]0.029Lacustrine condition0.480.17[0.14, 0.82]0.006Arctic condition0.610.18[0.27, 0.96]0.001Cognitive reappraisal− 0.160.10[− 0.35, 0.03]0.096Park condition × Cognitive reappraisal0.240.14[− 0.02, 0.51]0.074Lacustrine condition × Cognitive reappraisal0.350.14[0.08, 0.63]0.013Arctic condition × Cognitive reappraisal0.290.14[0.01, 0.57]0.041Subjective vitality (pre-exposure)0.600.06[0.48, 0.72]< 0.001Significant values are in bold. Figure 1Graphical representation of the simple slope analysis. CR cognitive reappraisal.
See Fig. 2 for a graphical representation of summary statistics and the density of subjective vitality post-exposure by condition. Figure 2Violin and box plots with data points that graphically represent summary statistics and the density of subjective vitality post-exposure by condition.
## Discussion
The psychological benefits of virtual nature exposure are drawing increasing attention from the research community and health professionals. Nevertheless, although growing evidence points to the use of the VR medium as an effective substitute for outdoor nature exposure when access to the latter is hindered or impossible, little is known about the cases in which virtual nature could be more vs. less effective, or even not effective at all. These effects may vary depending upon different types of virtual nature to which people are exposed, as well as individual difference in the people themselves. In this study, we aimed at investigating the role of a possible moderator, namely general tendencies to engage in cognitive reappraisal, as it may play a role in the ways in which participants experience natural environments, including the engagement of aspects of involuntary attention such as “being away” that lead to restoration29,40. To investigate this, we designed an experiment in which we exposed participants to different natural environments as well as an urban one (as a control). We measured pre- and post-exposure levels of subjective vitality and general levels of habitual use of cognitive reappraisal.
Our first hypothesis was that exposure to virtual natural (vs. urban) environments would increase subjective vitality. Our results confirmed this hypothesis. Specifically, all three types of natural environments presented, namely the park, the lacustrine, and the arctic, were all significantly more effective than the urban environment in increasing levels of subjective vitality. The potential of virtual nature to increase subjective vitality has been found by other researchers as well17,18,23,41. A contribution that our findings offer here is the demonstration of how different types of nature may have different magnitudes of effects. Other types of natural environments such as deserts, high mountains, and other landscapes should be incorporated into future studies, as they may show different results as well.
Our second hypothesis was that individuals who make habitual use of cognitive reappraisal would experience greater increases in subjective vitality from the virtual natural (vs. urban) environments than those who do not. This hypothesis was partially confirmed. We found this “boosting effect” of the habitual use of cognitive reappraisal only in the lacustrine and arctic (and not the park) environments. In these conditions, findings demonstrated that for those who make no habitual use of cognitive reappraisal, the effect of virtual nature exposure on subjective vitality disappeared, while for those with high levels of habitual use of cognitive reappraisal, virtual nature exposure exerted a positive effect on subjective vitality. These findings confirm that cognitive reappraisal can facilitate increases in subjective vitality from at least some types of nature exposure—results that have practical implications for applications of VR in improving well-being. Future research should extend this investigation to include other affective and cognitive outcomes.
As we state above, we did not find the interaction effect for the park. This result should also be investigated further to understand the reasons behind it. It may be the case that cognitive reappraisal plays less of a moderating role when presented with more familiar natural environments. We can speculate that participants were less acquainted with a lacustrine or arctic environment than a park one. The sensitivity to surroundings that is associated with the habitual use of cognitive reappraisal27,29 may play a more significant role during exposure to unfamiliar natural environments, helping to enhance awareness of the unusual experience and thereby lead to increased subjective vitality. In contrast, paying less attention and having less control over the meaning of a situation during virtual nature exposure to unfamiliar environments could lead to missing out on these benefits42. Of course, these are speculations that need further consideration and future studies should investigate these possibilities.
Lastly, although beyond the scope of our research, it is interesting to note that in our study the main effect of cognitive reappraisal on subjective vitality was nonsignificant ($$p \leq 0.096$$), suggesting that, in our experiment, cognitive reappraisal alone did not determine changes in subjective vitality. Instead it was the interaction of cognitive reappraisal with the type of environment that was significantly associated with observed changes in this outcome. This result suggests that the habitual use of cognitive reappraisal is not enough to determine higher subjective vitality after VR exposure in general, but that it is the cognitive reappraisal interaction with the type of environment (i.e., lacustrine and arctic) that makes the difference. Future studies may extend this study to different types of non-natural VR environments to test whether cognitive reappraisal may also have a role in VR exposure to other kinds of environments apart from the natural ones, as well as the potential mechanisms underlying these differences (e.g., familiar vs. unfamiliar environments as suggested previously).
This study is not free of limitations. First, we used a sample of social psychology students to test our hypotheses, with a gender imbalance (i.e., the majority of participants were women). Future efforts should be directed to replicate these results in other populations and more balanced samples by gender to allow the generalizability of the findings. Second, as recently highlighted6, short-term outcomes of VR are promising but future research should also address whether the changes observed hold in the long term, to enlarge the potential implications for interventions. In this regard, it could be important to examine not just the role of cognitive reappraisal in boosting post-exposure subjective vitality but also in sustaining it over time (e.g., after hours, days, weeks). Third, we choose to use 360° panoramic photos to expose participants to the different environments. Future research may replicate our results with 360° videos. Fourth, some studies suggest that auditory and olfactory stimuli can amplify the effects of VR exposure5,43. Future studies could integrate these additional stimuli into our methodology and test whether the habitual use of cognitive reappraisal may enhance the benefit associated with multisensory exposures. Fifth, in our study, we focused on the adaptive emotion regulation strategy of cognitive reappraisal. Future research may investigate the role of other emotion regulation strategies, both adaptive and maladaptive, in post-exposure subjective vitality. Lastly, in the future, it could be interesting to study whether adaptive emotion regulation strategies such as cognitive reappraisal may diminish post-exposure negative emotions (characterized by both high and low activation44,45) to certain virtual environments (e.g., stressful environments) or for certain individuals (e.g., with high resistance to new technologies), as well as the impact on positive emotions characterized by low activation (e.g., contentment44).
Despite these limitations, compared to previous studies on virtual nature6, our study has some important strengths such as a relatively large sample size, a methodological approach that controlled for different possible confounding variables found in the literature, the comparison between different natural environments that includes an under-investigated environment such as the arctic environment, and a focus on the moderators behind virtual nature benefits. To our knowledge, this is the first study to integrate all these features in one study. Importantly, if future studies will confirm our findings there could be important practical implications. Indeed, subjective vitality has been demonstrated to be important in sustaining changes in unhealthy lifestyles such as smoking cessation46, promoting motivation to change during psychotherapeutic programs47, coping during stressful home confinement48, and encouraging adjustment at work15,49,50. In all these cases, an intervention that integrates virtual nature exposure could be effective.
However, caution should be paid when designing those interventions. Indeed, our study highlights how virtual nature may be ineffective for those with low levels of habitual use of cognitive reappraisal. A variety of different strategies are employable to address this. For instance, levels of cognitive reappraisal could be measured before the beginning of an intervention. For those with low levels of habitual use of this emotion regulation strategy, training could be designed and implemented to increase its general use, in order to improve the efficacy of the VR nature exposures.
## Conclusion
An increasing number of studies have shown that exposure to nature throughVR can provide health benefits, especially in those cases when outdoor exposure is difficult or not possible6,11,51. This study is the first to have investigated the moderating role of habitual use of cognitive reappraisal in relation to these effects. Research on the benefits associated with virtual nature is not new; however, it has drawn growing attention during the last years, especially after the pandemic and the lowering of prices of VR equipment that has extended its usage to more people. Nevertheless, our study points out how this extension in the usage of virtual nature should not automatically lead to an assumption regarding the extension of its benefits. People are different and some individuals, more than others, may experience relatively fewer of these effects. Our study is one of the first to highlight that. Following this line of research, other studies are needed to enhance the possible applications of virtual nature and to extend them to more people.
## Participants and procedure
To test our hypotheses, we conducted an experiment with a between-subject design and four conditions. An initial sample of 204 students agreed to take part in the study by giving their informed consent, of which 9 did not meet the eligibility criteria or lost contact (see Fig. 3 for a representation of participant recruitment). Anonymity was assured to all participants. They obtained extra course credit for participating. The experimental procedure was structured as follows. There were two measurement times: pre-exposure and post-exposure. To verify participants’ attention, we used two attention checks52, one in the pre-exposure questionnaire (i.e., «Please select the answer 5») and one in the post-exposure questionnaire (i.e., «This question is to check the attention of the respondent, if you are attentive please answer 4»). Of the total sample, 7 ($3.6\%$) did not answer correctly to the first attention check and 1 ($0.5\%$) failed the second attention check; thus, they were excluded from further analysis (see Fig. 3).Figure 3Participants recruitment process.
The final sample was composed of 187 participants (Mage = 21.17, SD = 2.55), of whom 150 ($80.2\%$) were women, 35 ($18.7\%$) were men, and 2 ($1.1\%$) were missing. The majority of participants held a high school diploma namely 148 ($79.1\%$), while 39 ($20.9\%$) held a degree or higher qualification. Approximately half of the sample, namely 101 ($54.0\%$), were in a relationship whereas 86 ($46.0\%$) were single. Lastly, 133 ($71.1\%$) participants were nonworking students and 54 ($28.9\%$) were working students.
During a social psychology class, the study was presented to the students. Students who voluntarily agreed to participate were then contacted by research assistants to book an appointment and were informed about the Covid-19 related procedures to be adopted during the experiment as well as the study’s eligibility criteria. In particular, they were discouraged to participate in the experiment if any of the VR contraindications were present (e.g., epilepsy, pacemaker usage).
Each participant was randomly assigned to one of the four experimental conditions, one for each type of virtual environment presented: [1] an urban environment (urban condition); [2] a national park (park condition); [3] a natural area with a lake (lacustrine condition); and [4] an arctic environment (arctic condition). The exposure took place through an HMD for VR, namely the Oculus Quest 2. Through the HMD, participants were asked to watch four 360° panoramic photos for each environment. The photos were taken by the experimenters (see 10.17605/osf.io/puhrj for an example of a photo per environment). Based on research conducted on previous experiments with VR that were similar to ours5,53,54, the time for each photo exposure was established at one minute per image, four minutes in total. The final numbers of participants for each group were: 47 for the urban condition, 46 for the park condition, 48 for the lacustrine condition, and 46 for the arctic condition (see Fig. 3).
At the laboratory, participants were invited to sign the informed consent for the experiment. Then, they were invited to fill out the first questionnaire. After this phase, students were presented with the immersive photos through the HMD. Participants wore protective eye masks, a protective cap, and surgical masks for the duration of exposure. They were asked to stand still, watch the images, and turn their head around to explore the environment. Research assistants were present throughout the VR exposure to assist participants if needed. After this phase, participants were asked to complete the second questionnaire. Then they were thanked, debriefed, and dismissed. See Fig. 4 for a graphical representation of the experimental procedure. Figure 4The experimental procedure.
## Measures
In the pre-exposure questionnaire, we measured cognitive reappraisal, the baseline level of subjective vitality along with environmental identity and perceived stress. In the post-exposure questionnaire, we measured the post-exposure level of subjective vitality and the remaining control variables (i.e., sociodemographic variables, type of environment the participant lives in, and variables related to participants' experience during the VR exposure).
## Subjective vitality
Pre- and post-exposure subjective vitality was measured using the state subjective vitality scale12,55, composed of 7 items. An example of an item is "*At this* moment, I feel alive and vital". The response scale ranged from 1 (not true at all) to 7 (very true). Cronbach’s alpha was 0.83 for both the pre- and post-exposure questionnaires.
## Cognitive reappraisal
Cognitive reappraisal was assessed using the Emotion Regulation Questionnaire in its Italian version56. The scale is composed of 6 items (e.g., “When I’m faced with a stressful situation, I make myself think about it in a way that helps me stay calm”). The response scale ranged from 1 (strongly disagree) to 7 (strongly agree). Cronbach’s alpha was 0.94.
## Sociodemographic variables
We collected information on age, gender (non-binary, women, men), education qualification (high school diploma, degree or higher qualification), marital status (single, in a relationship), and employment status (i.e., “Are you a working student?”: no, yes).
## Environmental identity
Environmental identity was assessed using the one-item measure called Inclusion of Nature in Self (INS)57 (i.e., "Below, please choose the pictures which best describe your relationship with the natural environment"). The measure provides seven possible answers (from 1 to 7) each displaying two circles progressively overlapping, representing nature and the self respectively. Higher scores indicate higher environmental identity.
## Perceived stress
To measure perceived stress in the previous month, we used four items of the original Perceived Stress Scale (PSS)58. An example of an item is "In the last month, how often have you felt that you were unable to control the important things in your life?". The response scale ranged from 0 (never) to 4 (very often). Cronbach’s alpha was 0.82.
## Type of environment the participant lives in
One question was used to assess the type of environment the participant lives in. The item was “Where do you live most of the time?”, with possible answers “House in an urban context” and “House in a natural environment”.
## Previous VR experience
Based on previous studies, we used one item namely “*Is this* your first time experimenting with immersive virtual reality (with a headset or goggles)?", with possible answers "no" and "yes".
## Brightness of the images
To control for possible differences in the brightness of the images we used one item, i.e., "How would you rate the brightness of the images shown?". The response scale ranged from 1 (very bad brightness) to 10 (excellent brightness).
## Sense of presence
To measure the sense of presence we used the 14-item Igroup Presence Questionnaire (IPQ)59. The items were adapted to refer to 360° panoramic photos and two items (applicable only to computer-generated worlds) were excluded. Cronbach’s alpha was 0.83.
## Motion sickness
To assess motion sickness, we used the 16-item Motion Sickness Assessment Questionnaire (MSAQ)60. In this study, we used the total score computed as suggested by Gianaros and colleagues. Cronbach’s alpha was 0.86.
## Ethics declarations
The study was approved by the institutional ethics committee of the European University of Rome (protocol n. $\frac{11}{2021}$). All methods were performed in accordance with the Declaration of Helsinki for studies involving human participants.
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|
---
title: 'Rugby Fans in Training New Zealand (RUFIT NZ): a randomized controlled trial
to assess the effectiveness of a healthy lifestyle program for overweight men delivered
through professional rugby clubs'
authors:
- Ralph Maddison
- Elaine Anne Hargreaves
- Yannan Jiang
- Amanda Jane Calder
- Sally Wyke
- Cindy M. Gray
- Kate Hunt
- David Revalds Lubans
- Helen Eyles
- Nick Draper
- Ihirangi Heke
- Stephen Kara
- Gerhard Sundborn
- Claire Arandjus
- Lan Gao
- Peter Lee
- Megumi Lim
- Samantha Marsh
journal: The International Journal of Behavioral Nutrition and Physical Activity
year: 2023
pmcid: PMC10043512
doi: 10.1186/s12966-022-01395-w
license: CC BY 4.0
---
# Rugby Fans in Training New Zealand (RUFIT NZ): a randomized controlled trial to assess the effectiveness of a healthy lifestyle program for overweight men delivered through professional rugby clubs
## Abstract
### Background
A healthy lifestyle program that appeals to, and supports, overweight and obese New Zealand (NZ) European, Māori (indigenous) and Pasifika men to achieve weight loss is urgently needed. A pilot program inspired by the successful Football Fans in Training program but delivered via professional rugby clubs in NZ ($$n = 96$$) was shown to be effective in weight loss, adherence to healthy lifestyle behaviors, and cardiorespiratory fitness in overweight and obese men. A full effectiveness trial is now needed.
### Aims
To determine the effectiveness and cost effectiveness of Rugby Fans In Training-NZ (RUFIT-NZ) on weight loss, fitness, blood pressure, lifestyle change, and health related quality of life (HRQoL) at 12- and 52-weeks.
### Methods
We conducted a pragmatic, two-arm, multi-center, randomized controlled trial in NZ with 378 (target 308) overweight and obese men aged 30–65 years, randomized to an intervention group or wait-list control group. The 12-week RUFIT-NZ program was a gender-sensitised, healthy lifestyle intervention delivered through professional rugby clubs. Each intervention session included: i) a 1-h workshop-based education component focused on nutrition, physical activity, sleep, sedentary behavior, and learning evidence-based behavior change strategies for sustaining a healthier lifestyle; and 2) a 1-h group-based, but individually tailored, exercise training session. The control group were offered RUFIT-NZ after 52-weeks. The primary outcome was change in body weight from baseline to 52-weeks. Secondary outcomes included change in body weight at 12-weeks, waist circumference, blood pressure, fitness (cardiorespiratory and musculoskeletal), lifestyle behaviors (leisure-time physical activity, sleep, smoking status, and alcohol and dietary quality), and health-related quality of life at 12- and 52-weeks.
### Results
Our final analysis included 200 participants (intervention $$n = 103$$; control $$n = 97$$) who were able to complete the RUFIT-NZ intervention prior to COVID-19 restrictions. At 52-weeks, the adjusted mean group difference in weight change (primary outcome) was -2.77 kg ($95\%$ CI -4.92 to -0.61), which favored the intervention group. The intervention also resulted in favorable significant differences in weight change and fruit and vegetable consumption at 12-weeks; and waist circumference, fitness outcomes, physical activity levels, and health-related quality of life at both 12 and 52 weeks. No significant intervention effects were observed for blood pressure, or sleep. Incremental cost-effective ratios estimated were $259 per kg lost, or $40,269 per quality adjusted life year (QALY) gained.
### Conclusion
RUFIT-NZ resulted in sustained positive changes in weight, waist circumference, physical fitness, self-reported physical activity, selected dietary outcomes, and health-related quality of life in overweight/obese men. As such, the program should be recommended for sustained delivery beyond this trial, involving other rugby clubs across NZ.
### Trial registration
Australia New Zealand Clinical Trials Registry, ACTRN12619000069156. Registered 18 January 2019, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376740 Universal Trial Number, U1111-1245–0645.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12966-022-01395-w.
## Background
In New Zealand (NZ), $31\%$ of adults are obese (BMI > 30 kg/m2) and a further $35\%$ are overweight, with key sex and ethnic disparities. Compared with women, the prevalence of overweight is greater in NZ European ($41\%$ vs $32\%$), Māori (the indigenous peoples of NZ; $33\%$ vs $27\%$), and Pasifika ($26\%$ vs $16\%$, respectively) men [1]. Further, Māori and Pasifika men are 1.7 and 2.2 times more likely to be obese when compared with non-Māori and non-Pasifika men, respectively. It has been estimated that excess weight costs NZ at least $NZ 2 Billion per year [2]. Therefore, effective, sustainable and appealing healthy lifestyle programs are needed to meet the needs of overweight and obese NZ men, and in particular Māori and Pasifika men [3, 4].
Men are underrepresented in obesity services, suggesting current weight loss services are suboptimal for this group [5]. A systematic review and meta-analysis of men-only weight loss and weight maintenance programs, involving 14 randomized controlled trials (RCTs) showed that the most effective interventions combined dietary, exercise, and behavior change techniques (mean difference in weight at 1 year compared with no intervention was -4.9 kg, $95\%$ confidence interval -5.9 to -4.0, $p \leq 0.0001$). Group-based interventions also produced favorable weight loss results [6].
Building on the successful Football Fans In Training (FFIT) program, a weight management and healthy lifestyle program in Scotland [7, 8], we previously developed and piloted a similar program to support overweight/obese men to lose weight, delivered via professional rugby clubs in NZ. In our pilot RCT ($$n = 96$$) of Rugby Fans In Training-NZ (RUFIT-NZ) we found a -2.5 kg ($95\%$ CI -0.4 to 5.4) difference in body weight in favor of participants in the intervention group at 12-weeks. In addition, participants who received the program had significant reductions in waist circumference, resting heart rate, and diastolic blood pressure, as well as improved fitness and adherence to lifestyle behaviors, including physical activity and not smoking [9]. Furthermore, $100\%$ of those who completed the program said that they would recommend it to their friends, and qualitative data from a subset of men found that the factors incorporated into the design and delivery of the program created engagement [10]. Therefore the feasibility and acceptability of RUFIT-NZ was demonstrated, supporting the need for a larger scale RCT to evaluate its sustained effect [9]. The aim of the current study was to determine the effectiveness and cost effectiveness of RUFIT-NZ on weight loss, fitness, blood pressure, lifestyle change, and health related quality of life (HRQoL) at 12- and 52-weeks.
## Study design
A pragmatic multi-center, two-arm, parallel RCT was conducted in NZ between 21 Jan 2019 and 22 Oct 2020. The study received ethical approval from the University of Auckland Human Ethics Committee [021,888]. The study protocol was registered and published [11] before the conclusion of recruitment (Australian New Zealand Clinical Trials Registry, ID: ACTRN12619000069156 Registered, 18 Jan 2019). The trial was designed and reported according to the Consolidated Standards of Reporting Trials (CONSORT) checklist [12, 13] (Additional file 1). Some changes to study methods were implemented as a result of COVID-19 (see details below).
## Study setting
RUFIT-NZ was delivered via professional rugby franchises, which participate in the Super Rugby competition across NZ, Australia and South Africa. The five NZ-based Super Rugby franchises were approached and three agreed to participate (the Blues based in Auckland [North Island], Crusaders based in Christchurch, and Highlanders based in Dunedin [both South Island]).
## Participants and recruitment
Eligible participants were overweight men (defined as a BMI ≥ 28 kg/m2) aged 30–65 years, who were able to safely undertake physical activity, understand and read English, and provide written informed consent. Interested and eligible participants were pre-screened using the Physical Activity Readiness Questionnaire (PAR-Q) [14, 15], and required consent from their general practitioner if any PAR-Q items were endorsed. Exclusion criteria included participation in any other healthy lifestyle program, or if participants knew in advance they could not complete the 52-week follow-up. Participants were recruited via the respective rugby club’s fan base registries, including Facebook pages, supporter mailing lists, and newspaper advertisements/articles. Participants were also recruited via Māori-specific networks (e.g., Marae [Māori meeting house], word-of-mouth) and media (e.g., Māori television and radio). We linked all advertisements with the University of Auckland’s Faculty of Medical and Health Science’s research study recruitment page where participants could access information about the study, including the Participant Information Sheet and Consent Form. There was also the option to contact the research team for additional information as required, and potential participants could link directly to an online registration form. Our multifaceted recruitment strategy was informed by the RUFIT-NZ pilot study, which suggested that club-based and social media strategies were likely to be most effective. An example of a recruitment flyer is provided (see supplementary file).
## Sample size
A total of 308 participants (154 per arm) was estimated to provide $90\%$ power at $5\%$ significance level (two-sided) to detect a clinically significant 5 kg difference [16] on the primary outcome (change in weight) between the two groups at 52-weeks, assuming a standard deviation (SD) of 12 kg and allowing for $20\%$ loss to follow up. Our SD was conservative and derived from other weight management trials for men [9, 17]. Māori are the indigenous population of NZ, therefore we wanted to ensure sufficient power to detect effects for this group. We therefore aimed to recruit a total of 150 Māori participants (~ $50\%$ of the total sample size), which was estimated to provide $80\%$ power to detect a 6 kg difference between ethnic groups under the same assumptions.
## Randomization
Following baseline data collection, eligible and consented participants were randomized on a 1:1 ratio to either the RUFIT-NZ intervention or the control group using a computerized randomization process that ensured allocation concealment. Randomization was stratified by baseline BMI category (< 35 kg/m2 versus ≥ 35 kg/m2), self-reported ethnicity (Māori, Pasifika, non-Māori/non-Pasifika), and study center, using stratified block randomization with variable block sizes of two or four. The randomization sequence was generated by our biostatistician (YJ). Participants were informed by email of their eligibility and allocation group within 2–3 days of their baseline assessment at the club. Due to the nature of the study, participants and research assistants were aware of the treatment allocation post-randomization. Study investigators and the trial statistician were blinded during analysis. To reduce assessment bias, objective measures of height and weight were collected by trained researchers at 12- and 52-weeks.
## Control group
We used a wait-list control approach— those randomized to the control group were asked to continue with their usual lifestyle for 52 weeks during the trial period but were offered the RUFIT-NZ intervention at the end of the 12-month follow-up period.
## Intervention
Full details of the development of the RUFIT-NZ intervention are published in our study protocol [11]. The overall aim of the intervention was to support men to engage in healthy lifestyle behaviors to reduce weight and develop the necessary skills to maintain these behaviors in the long-term. RUFIT-NZ involved a 12-week healthy lifestyle program, consisting of 12 × weekly 2-h sessions. Each intervention session included a 1-h workshop-based education component (See Appendix) and 1-h group-based, but individually tailored, exercise training session. During the education component, participants were introduced to a range of topics relating to physical activity, nutrition, sleep, and alcohol consumption, as well as to key theory-based behavior change techniques.
Education sessions were delivered predominantly by RUFIT-NZ-trained trainers, however nutrition-based components were delivered by the clubs’ nutritionists or qualified dieticians, supported by the study nutritionist (HE). This approach differed from the original FFIT program but was consistent with the RUFIT-NZ pilot, which indicated a preference for expert advice on diet and nutrition. All RUFIT-NZ trainers were qualified strength and conditioning trainers involved with the respective rugby clubs. Registered dietitians involved in delivering RUFIT-NZ had a previous connection to the club. For the purpose of this trial, the trainers and nutritionists were employed by the respective clubs and agreed to deliver RUFIT-NZ. Classroom content was standardized, so that all participants received the same education information, but the individual trainers could tailor the format of delivery and level of detail as required. RUFIT-NZ did not engage professional team players in the delivery of the intervention. That decision was based on previous experience with FFIT [7] and our previous pilot trial [9]. The education sessions and the overall delivery of the program was interactive, with RUFIT-NZ trainers and dieticians enabling interactive learning and encouraging camaraderie and a sense of team to facilitate discussion of key topics.
Group-based in-stadia physical activity sessions were delivered by the trainers who were given basic guidance to deliver sessions (e.g., start low and build slow), but were also given freedom to structure each session as they chose. This approach allowed trainers to best meet the needs of individuals attending their RUFIT-NZ sessions. Activity sessions were tailored to individual fitness levels and ability. They included aerobic (e.g., stationary rowing and cycling, walking and jogging), muscle strengthening (e.g., weight/circuit training) and flexibility (e.g., warm-up/cool-down activities) exercises [18]. Participants were instructed to use the rating of perceived exertion (RPE) scale to ensure their activity was appropriate for their own fitness level. The difficulty (intensity) of each physical activity session increased over the 12 weeks, accounting for each participant’s level of fitness. Throughout the intervention men were encouraged to consider what types of activity they could continue to engage with in community settings. Sessions were varied and utilized the supportive group involvement to foster the sense of being in a ‘team’. Group size ranged from approximately 15–20 men per trainer. Roll calls were taken at the beginning of each session to record attendance.
To inspire habitual physical activity, men were encouraged to follow a daily step-based walking program over the course of the 12-week intervention period and beyond [19–21] and to use a step counter (pedometer or smartphone app) to track their daily and weekly progress. Trainers encouraged men to engage in other forms of physical activity and with a focus on integrating walking and other forms of incidental activity into daily life (e.g., walking up stairs). RUFIT-NZ trainers also provided physical activity ‘homework’ that participants could undertake outside of the structured sessions (e.g., researching places in their community to be physically active). Participants’ lifestyle behaviors in terms of alcohol, sleep, sedentary behavior, and nutrition were guided by individual goals, which men set for themselves during the group education sessions and recorded in a workbook.
Nutrition content for RUFIT-NZ was developed by our investigator nutritionist (HE), and was consistent with the NZ guidelines approach for weight management using a Family, Activity, Behavior (FAB) approach [22]. Our aim was to ensure all nutrition sessions were positively framed (e.g., ‘what are some good examples of healthy snacks?’ and ‘where can I find quick easy recipes?’), and involved the delivery of simple messages focused on practical elements of improving diet. Messages aligned with the NZ Eating and Activity Guidelines for Adults [23]. To facilitate an understanding of what men were eating and to help them record their diet, we provided men with a food diary to use as they wished. RUFIT-NZ nutrition sessions targeted the following biggest healthy eating ‘wins’:Eating as many fruit and vegetables as possible. Cooking and preparing food and snacks at home as much as possible. Eating mostly whole foods (as opposed to packaged/processed foods and takeaways).Drinking sugar-free beverages. Conscious eating (screen-free, mindful eating, ideally in the company of others).
## Behavior change techniques
A key focus of RUFIT-NZ was to provide men with a range of skills and strategies they could use to develop and maintain a healthy lifestyle, which included managing their weight. To that end, a range of evidence-based behavior change techniques shown to be effective in improving diet and physical activity were used throughout the education and exercise sessions [24]. Key techniques included: i) identifying autonomous reasons for lifestyle change, ii) goal setting for, and self-monitoring of, weight, physical activity, and healthy diet; iii) intention formation with action plans; iv) experiencing exercise sessions with increased challenges as well as positive feedback on exercise achievements and change reinforcement from trainers to build self-efficacy; and v) identification of barriers and coping planning to help avoid relapse during, and on completion of, the program (see Appendix, for details).
## Training
Prior to delivering RUFIT-NZ, trainers underwent a standardized training session, delivered by a member of the RUFIT-NZ investigator team. Training was supplemented with a standardized trainer’s manual, which outlined key principles to be promoted via RUFIT-NZ, and the nutrition topics to be covered in each session. We also provided PowerPoint presentation templates and participant worksheets, and support sessions and resources were offered to nutritionists to use in group-based sessions. Trainers were provided a nominal fee (paid to the club) to deliver RUFIT-NZ.
For RUFIT-NZ we used the Supportive, Active, Autonomous, Fair, and Enjoyable (SAAFE) delivery principles, an evidence-based approach for the planning, delivery, and evaluation of organized physical activity sessions [25]. Consistent with this approach, trainers were encouraged to ensure a SAAFE environment by: i) creating a Supportive social environment, enabling learning from each other; ii) maximizing participants’ opportunities to be physically Active during the sessions; iii) satisfying participants’ need for Autonomy by including elements of choice and providing a rationale for activities; iv) designing and delivering activities that are Fair by allowing all participants to experience success regardless of their physical abilities; and v) promoting an Enjoyable experience by focusing on fun and variety and incorporating games where possible.
## Fidelity
To assess intervention fidelity, we undertook direct observation by trained research assistants, using a standardized checklist at Weeks 4 and 10 of the 12-week intervention. Components of the classroom sessions (e.g., attendance, correct slides, weight check, and delivery) and the physical activity sessions (e.g., individualized exercises, adherence to SAAFE principles, and general delivery) were assessed on whether they were delivered or not; the research team provided verbal and written feedback to the trainers to address any gaps in intervention delivery. All coaches passed these checks.
## Outcomes
All outcomes were assessed at 12- and 52-weeks post-randomization. The primary outcome was change in body weight from baseline to 52-weeks.
Secondary outcomes included change in body weight at 12-weeks, waist circumference, blood pressure, fitness, lifestyle behaviors and other nutrition outcomes, health-related quality of life (HRQoL) and cost-effectiveness.
Anthropometric data were collected using standard practices [26]. Height was measured to the nearest 0.1 cm with a stadiometer (Seca, 214, Hamburg, Germany) and weight was measured to the nearest 0.1 kg with a digital scale (Tanita, UM-070, Illinois, US). For both height and weight, two measures were taken. A third measurement was taken if differences of 0.1 cm and 0.1 kg respectively were observed between the first and second measurements. The mean of two measurements or the median of three was used for analysis.
Resting systolic and diastolic blood pressure were measured via standard procedures using an automated sphygmomanometer (OMRON T9P Intellisense Blood Pressure Monitor) and/or a manual Blood Pressure Monitor (D1537-Reister Shock Proof Sphygmomanometer). Participants were asked to rest for a period of 5-min before measurements were taken.
Cardiorespiratory fitness was assessed using the time to complete a 6 km cycle test using a stationary bike [27].
Musculoskeletal fitness (endurance) was assessed using the timed sit-to-stand test [28], and a timed push up test [27].
## Lifestyle behaviors
Participants self-reported the following lifestyle behaviors, [1] leisure-time physical activity (assessed by the Godin Leisure Time Physical Activity Questionnaire) [29]; [2] cigarette smoking (assessed by a smoking history questionnaire) [30]; [3] alcohol intake (assessed by the Alcohol Use Disorders Identification test consumption [AUDIT C]) [31]; [4] sleep (self-reported average number of hours slept over a 24 h period); and nutrition habits over the past seven days, including average fruit and vegetable intake over the past week (with options from ‘I don’t eat fruit or vegetables’ to ‘4 or more servings per day), fast food or takeaway consumption (number of times in the past seven days), and sugar-sweetened beverage consumption (powdered and fizzy drinks; number of times over the past seven days). All nutrition habits questions were assessed using existing questions from the NZ Health Survey [32]. The Godin Leisure Time Questionnaire has been shown to correlate with maximal oxygen consumption ($r = 0.24$ -0.34) [33], and accelerometry ($r = 0.32$–0.45) (cf., [ 34]). Cigarette smoking questions were adapted from the Fagerström Test for Nicotine Dependence, which has demonstrated significant mean differences in the number of cigarettes smoked and Carbon Monoxide measures [35]. The AUDIT-C is an abbreviated version of the Alcohol Use Disorders Identification test (AUDIT) that has been advocated for use in both research and practice settings where there is insufficient time to administer the full AUDIT [36]. The AUDIT-C has similar accuracy to the full AUDIT for providing cut-off scores for units of alcohol consumed [37]; cut-off scores are based on expert opinion rather than validation data. No validation data were available for sleep or the fruit and vegetable questions, however we utilized the standardized questions outlined in the $\frac{2006}{07}$ NZ Health Survey.
## Other dietary factors
In addition to fruit and vegetable consumption, we collected data on meals consumed or prepared at home (number of times for breakfast, lunch and dinner over the past week), and conscious eating (noticing when eating and not hungry, and stopping eating when full (scale from one to four, with four ‘agree’); these questions were sourced from the Framson mindful eating questionnaire and the Clementi abbreviated mindful eating questionnaire, adapted to seven days of measurement [38, 39].
Baseline demographics: Participants completed a web-based questionnaire for demographic information including age, date of birth, ethnicity, employment status, highest level of education, marital status, and household income.
## Economic evaluation
A trial-based economic evaluation was undertaken using data from the RUFIT-NZ trial. The main outcomes for the economic evaluation were the incremental cost-effectiveness ratio (ICER) in terms of cost per body weight loss (kg), and cost per quality-adjusted life year (QALY) gained, for participants enrolled in the program compared with those in the control arm over 52 weeks. Participant HRQoL was measured using the EQ-5D-5L at both 12 and 52 weeks; a utility score was derived using NZ population-specific weights to estimate QALYs gained at 52 weeks for participants enrolled in both treatment and control arms [40, 41]. A minor protocol violation meant that the EQ5-D was not administered at baseline.
## Changes in response to the COVID-19 Pandemic
The present study was severely affected by the COVID-19 pandemic and changes to our original trial are detailed below. For logistical reasons, participants were recruited for the RUFIT-NZ trial in three separate waves. Prior to COVID-19 we successfully recruited and randomized 200 participants over two waves [2019]. Participants for wave three were recruited and randomized in February 2020, but due to strict COVID-19 restrictions and lockdowns across NZ, as well as changes in health and safety requirements at the respective rugby clubs, we were unable to continue with wave three. As a result, none of the participants randomized (intervention or control) in wave three ($$n = 178$$) took part in the RUFIT-NZ program. As a result, the steering group committee made the decision to only analyze data from the 200 participants randomized to waves 1 and 2. A post-hoc power evaluation on the study sample suggested that we had > $90\%$ power to detect a group difference of 3 kg on weight change at 52 weeks. Therefore, in the results section we only report findings on those 200 participants; baseline data from all participants are presented in the Appendices.
## Analysis
Trial data from all randomized participants were collected via secure web-based case record forms and stored using REDCap. Baseline characteristics and outcome data were first summarized descriptively: continuous variables were summarized as mean and standard deviation (SD), and categorical variables as frequency and percentage.
Analysis was conducted according to a modified intention-to-treat (ITT) principle, to include all randomized participants in the first two waves. For the main ITT analysis, we used multiple imputations for missing primary outcome data. Sensitivity analysis was also considered using the baseline value carried forward (BVCF) approach on missing data to test the robustness of the main results. Per protocol (PP) analysis was also performed on randomized participants who provided primary outcome data with no major protocol violations. Linear regression models were used to evaluate the effect of the intervention on change in body weight at 52 weeks (primary outcome), adjusting for baseline body weight, age, study wave, and stratification factors (BMI category, self-reported ethnicity, study center). Given the COVID-19 related issues, we were unable to undertake sub-group analysis by ethnicity.
Similar regression models were used on continuous secondary outcomes at 12- and 52-weeks. Model-adjusted mean differences were reported with $95\%$ confidence intervals (CIs). Logistic regression models were used on the adherence to healthy lifestyle behaviors outcomes at 12 and 52 weeks. Adjusted odds ratios (ORs) were reported with $95\%$ CIs. For secondary outcomes, no imputation was considered on missing outcome data. Confidence interval widths have not been adjusted for multiplicity and may not be used in place of hypothesis testing. Statistical analysis was performed using SAS version 9.4 (SAS Institute Inc.). All statistical tests were two-sided with a $5\%$ significance level.
## Economic analysis
Multiple imputations were applied to missing primary outcome data (change in body weight, and quality of life at 52 weeks) assuming missing at random. Patterns in missing data were explored with the use of logistic regression and t-tests to investigate if any covariates would predict if an outcome variable was missing (logistic regression), and whether there were differences in patient characteristics between patient groups who had a missing outcome variable, and patient groups with a non-missing outcome variable (t-tests). To address the negatively skewed distribution for the estimated utilities, univariable generalized linear regression modelling with a Poisson distribution and log link was used to explore differences in participant QALYs following the modified Park test [41]. As with the main analysis, this model was adjusted for baseline weight, age in years, study wave, and stratification factors (BMI category, self-reported ethnicity, and study centre).
Nonparametric bootstrapping with 2,000 simulations was performed around each ICER to address uncertainty around the cost-effectiveness of RUFIT-NZ. Additionally, scenario analyses exploring the cost-effectiveness of RUFIT-NZ were performed for participants with non-missing outcomes data without imputation. The cost of providing RUFIT-NZ across the three rugby clubs was estimated through micro costing (see Appendices). Although no official willingness-to-pay threshold has been established for NZ, contemporary economic analyses have considered a gross domestic product per capita expenditure of $45,000 per QALY to be cost-effective [42, 43]. All costs were expressed in $NZD in 2021 using the consumer price index (CPI) [44].
## Overview
Figure 1 presents the flow diagram of participant progress through the phases of the trial. A total of 1,186 people were screened between January 2019 and February 2020, and 378 eligible participants were randomized (Intervention $$n = 192$$; Control $$n = 186$$). The final analysis included a total of 200 trial participants (Intervention $$n = 103$$; Control $$n = 97$$) who were able to complete the study in waves one and two, prior to COVID-19 restrictions. No baseline differences were found between the participants of different waves (see Appendices).Fig. 1CONSORT 2010 Flow Diagram. Note: ITT – Intention to treat; PP – Per protocol. * One participant was randomized to the wrong group but also had missing primary outcome as well and was therefore reported in the missing outcome numbers Two participants had a major protocol violation after randomization and were excluded from analysis. One participant had a baseline BMI below the < 28 kg/m2 cutoff in the inclusion criteria. Another participant was randomized to the control group but allocated to the intervention group in error. The participant was informed of the mistake but continued with the intervention. As highlighted above, a separate minor protocol violation was recorded as the EQ5-D was not administered at baseline.
Participants were predominantly New Zealand European, with a mean age of 45.7 years (SD 8.7) years. The two groups were comparable at baseline (Table 1). Table 1Participant baseline demographic and study site data ($$n = 200$$)Baseline CharacteristicsControl ($$n = 97$$)Intervention ($$n = 103$$)Demographics Age (years), mean (SD)46.3 (8.7)45.1 (8.7)Ethnicity, n (%) Māori16 (16.5)21 (20.4) Pasifika16 (16.5)16 (15.5) New Zealand European or Other65 (67.0)66 (64.1)BMI category, n (%) 28–34 kg/m253 (54.6)56 (54.4) ≥ 35 kg/m244 (45.4)47 (45.6)Annual household income, n (%) Less than $NZD15,0001 (1.0)1 (1.0) $NZD15,000—$29,9990 (0.0)4 (3.9) $30,000—$59,9999 (9.3)14 (13.6) $60,000—$99,99932 (33.0)30 (29.1) $100,000 or more49 (50.5)46 (44.7) Did not know / chose not to answer6 (6.2)8 (7.8)Marital status, n (%) Married64 (66.0)72 (69.9) Civil union or living with partner22 (22.7)14 (13.6) Separated, divorced, or widowed9 (9.3)7 (6.8) Never married (single)2 (2.1)5 (4.9) Refuse to answer0 (0.0)5 (4.9)Study wave, n (%) Wave 1 (Feb 2019 – Feb 2020)56 (57.7)55 (53.4) Wave 2 (May 2019 – Jun 2020)41 (42.3)48 (46.6)Study site, n (%) Blues48 (49.5)52 (50.5) Crusaders21 (21.6)23 (22.3) Highlanders28 (28.9)28 (27.2)Note: Data presented from participants recruited in waves one and two only
## Effect on body weight and secondary outcomes
Participants in the control group showed little change in weight from baseline to 12-weeks (mean = -0.38, SD 4.82 kg) and 52-weeks (mean = 0.10, SD 5.73 kg), while the intervention group demonstrated weight losses from baseline at both time points (12-weeks: mean -2.65, SD 3.42 kg; 52-weeks: mean -2.59, SD 6.95 kg). In the primary ITT analysis with multiple imputations on missing outcome, the adjusted mean difference in weight change between the two groups was -2.77 kg ($95\%$CI -4.92 to -0.61) in favour of the intervention group (Table 2). In the per-protocol analysis, the adjusted mean difference in weight change between the two groups was -2.82 kg ($95\%$CI -4.99 to -0.65), also in favour of the intervention group. The sensitivity analysis using the BVCF approach on missing outcome gave an estimated group difference of -1.95 kg ($95\%$CI -3.34 to -0.56). Table 2Primary outcome analysis on change in body weight at 52 weeksControlInterventionIntervention vs Control*N, Mean (SD)N, Mean (SD)Adjusted Mean difference ($95\%$ CI)p-valueChange from baseline64, 0.10 (5.73)63, -2.59 (6.95)--ITT with MI (primary)---2.77 (-4.92, -0.61)0.015ITT with BVCF---1.95 (-3.34, -0.56)0.006PP---2.82 (-4.99, -0.65)0.011N numbers observed, SD standard deviation, ITT intention to treat, MI multiple imputations, BVCF baseline value carry forward, PP per protocol*Linear regression model adjusted for baseline weight, age in years, study wave, and stratification factors (BMI category, self-reported ethnicity, study centre) For secondary outcomes reported in Table 3, a similar mean difference in weight change was observed at 12-weeks (-2.65 kg; $95\%$CI -3.90 to -1.41) in favour of the intervention group. There was a significant difference in waist circumference at both time points in favour of the intervention. There were no between group differences in blood pressure at either time point. There were significant differences in cardiorespiratory fitness, sit to stand times, and press-ups completed, all favouring the intervention group. In terms of lifestyle change, there were positive differences in self-reported physical activity at 12- and 52-weeks, and fruit and vegetable consumption at 12 weeks (with some attenuation at 52 weeks), but no significant differences in number of alcoholic drinks consumed per week, or hours of sleep per day at both time points. Significant differences were observed in HRQoL at both 12- and 52 weeks in favour of the intervention group (Table 3). Table 3Secondary outcome analysis on anthropometric data, blood pressure, fitness, lifestyle behaviors and HRQoL at 12- and 52-weeksOutcomesControlInterventionIntervention vs Control*NMean (SD)NMean (SD)Mean difference$95\%$ CIP valueWeight (kg)Baseline97111.46 (17.25)103112.13 (19.35)12-weeks69112.52 (17.93)81107.85 (18.26)-2.65(-3.90, -1.41) <.000152-weeks64111.34 (19.76)63108.47 (19.86)-2.82(-4.99, -0.65)0.011BMI (kg/m2)Baseline9735.30 (4.87)10335.55 (5.65)12-weeks6735.46 (4.96)8034.38 (5.09)-0.88(-1.27, -0.48) <.000152-weeks6135.47 (5.48)5834.50 (5.52)-0.69(-1.42, 0.04)0.062Waist circumference (cm)Baseline97116.93 (11.39)103118.14 (13.60)12-weeks67115.55 (11.40)80112.48 (12.91)-2.51(-4.27, -0.75)0.00652-weeks61116.29 (12.79)58113.00 (13.65)-2.91(-5.14, -0.67)0.011Systolic BP (mmHg)Baseline97147.28 (14.19)103145.99 (15.91)12-weeks67145.20 (15.71)80145.81 (17.54)1.94(-1.92, 5.80)0.32252-weeks61147.70 (15.28)58143.47 (15.22)-2.06(-6.13, 2.01)0.318Diastolic BP (mmHg)Baseline9793.36 (9.23)10391.59 (8.66)12-weeks6790.01 (10.18)8090.09 (9.74)1.57(-0.87, 4.00)0.20552-weeks6193.08 (9.77)5889.94 (10.48)-1.63(-4.54, 1.28)0.270Sit to stand testBaseline9715.37 (2.84)10315.69 (3.23)12-weeks6716.37 (3.64)8020.23 (4.36)3.26(2.15, 4.36) <.000152-weeks5515.64 (3.26)5119.33 (4.79)2.73(1.55, 3.92) <.0001Press up test (count)Baseline9719.10 (9.37)10319.77 (9.53)12-weeks6719.83 (11.39)8026.43 (10.40)5.05(2.56, 7.54)0.000152-weeks5419.20 (11.33)5024.48 (12.64)5.09(2.08, 8.09)0.0011Fitness test (mins)Baseline9710.17 (1.00)10210.19 (0.95)12-weeks6710.09 (0.92)779.71 (0.76)-0.37(-0.53, -0.21) <.000152-weeks5510.15(1.08)509.74 (0.99)-0.48(-0.77, -0.18)0.0017Sleep (hours/day)Baseline977.00 (0.92)1036.96 (1.00)12-weeks677.19 (0.94)817.14 (1.20)0.09(-0.17, 0.34)0.50852-weeks616.90 (0.89)546.91 (1.00)0.09(-0.17, 0.35)0.489Physical activity scoreBaseline9729.03 (25.18)10328.51 (31.46)12-weeks6936.36 (32.25)8150.79 (25.52)17.10(8.29, 25.91)0.000252-weeks6233.65 (19.35)5447.24 (25.94)15.03(6.98, 23.07)0.0003Alcohol drinks per weekBaseline976.77 (8.36)1036.63 (7.25)12-weeks695.91 (7.66)815.19 (6.91)0.04(-1.51, 1.59)0.95752-weeks626.05 (6.09)545.52 (5.69)-0.32(-1.81—1.17)0.669Fruit and vegetable servings per dayBaseline963.22 (1.64)1033.29 (1.54)12-weeks663.45 (1.71)804.04 (1.53)0.63(0.19, 1.07)0.00552-weeks613.23 (1.56)543.75 (1.66)0.41(-0.07, 0.90)0.093HRQoLBaseline (not collected)12-weeks2963.24 (19.33)3774.35 (13.60)11.73(3.65, 19.82)0.00552-weeks6163.48 (18.34)5466.87 (19.42)6.55(0.05, 13.05)0.048Note: BP Blood pressure, HRQoL Health related quality of life scale measured via EQ5D (0–100, higher is better)*Secondary analysis* with no imputation for missing outcome data; two participants with major protocol violations were excluded* Linear regression model adjusted for baseline outcome, age in years, study wave, and stratification factors (BMI category, self-reported ethnicity, study centre) In terms of additional dietary outcomes, we observed favourable differences for the intervention group in the number of fizzy drinks consumed at 12 weeks, number of powdered drinks at 52 weeks, and number of fast-food occasions at 12 weeks only (Table 4). There were positive effects on the ‘stop eating when full’ score at 12 and 52 weeks, but not on the ‘noticed when eating and hunger’ score. Table 4Secondary analysis on other dietary outcomes measured at 12- and 52-weeksOutcomesControlInterventionIntervention vs Control*NMean (SD)NMean (SD)Mean difference$95\%$ CIP valueNumber of fizzy drinks in past weekBaseline973.79 (4.04)1034.0 (6.04)Post program672.72 (3.37)811.90 (3.12)-0.81(-1.66, 0.04)0.0652-weeks612.26 (3.61)542.65 (4.48)0.22(-0.72, 1.16)0.64Number of powdered drinks in past weekBaseline971.12 (2.32)1030.65 (1.68)Post program670.60 (1.97)810.25 (0.68)-0.16(-0.57, 0.25)0.4352-weeks611.07 (2.48)540.33 (1.03)-0.69(-1.42, 0.04)0.06Number of fast food occasionsBaseline972.64 (2.19)1032.77 (2.20)Post program672.19 (2.15)811.32 (1.16)-1.05(-1.51, -0.59) < 0.000152-weeks611.74 (1.70)541.70 (1.78)-0.18(-0.75, 0.40)0.54Noticed when eating and hunger scoreBaseline971.99 (0.73)1032.10 (0.71)Post program671.91 (0.57)811.94 (0.81)-0.01(-0.24, 0.22)0.9152-weeks611.97 (0.68)541.87 (0.85)-0.13(-0.42, 0.16)0.36Stop eating when full scoreBaseline971.89 (0.81)1032.02 (0.91)Post program672.19 (1.00)812.67 (0.88)0.39(0.11, 0.67)0.00752-weeks612.02 (0.85)542.50 (0.82)0.38(0.10, 0.67)0.009Note: Noticed when eating and hunger score – higher score is better. Stope eating when full score – higher is betterSecondary analysis with no imputation on missing outcome data; two participants with major protocol violations were excluded*Linear regression model adjusted for baseline outcome, age in years, study wave, and stratification factors (BMI category, self-reported ethnicity, study centre) Cost-effectiveness: Based on a total intervention cost of $77,469, the cost per participant was estimated to be $ NZD 752. Control participants were assumed to incur no costs (derivation of costs can be found in the Appendices).
Based on the results of non-parametric bootstrapping following multiple imputation, slight QALY gains were observed for participants in the intervention arm in bootstrapped analysis (mean QALY gains: 0.02, $95\%$ CI: 0.01 – 0.03, $P \leq 0.001$). ICERs estimated for participants in the RUFIT-NZ intervention were $259 per kg lost, or $40,269 per QALY gained, and RUFIT-NZ was cost-effective in $78\%$ of 2,000 iterations if a willingness-to-pay threshold of $45,000 NZD per QALY was used. In a scenario analysis of non-imputed data, the cost-effectiveness of RUFIT-NZ was maintained for the primary outcome of body weight reduction at 52 weeks, but not for QALYs gained (ICER: $231,895 per QALY gained) (Table 5).Table 5Results of the economic evaluationParameterAnalysisDifferenceP-valueCost cMean ($95\%$ CI) a,bQALYs gainedBase-case0.02 (0.01 – 0.03) < 0.001$752Non-imputed0.01 (-0.03 – 0.06)0.623$752Cost-effectiveness analysisICERProbability of cost-effectiveness (%) aCost per QALY gainedBase-case$40,269 per QALY gained78.0Non-imputed$231,895 per QALY gained42.9Cost per kg body weight lossBase-case$259 per KG weight loss-Non-imputed$252 per KG weight loss-ICER incremental cost-effectiveness ratio, KG kilogram, QALY quality-adjusted life yeara Based on 2,000 bootstrapped iterations following multiple imputationb Generalised linear regression model adjusted for baseline weight, age in years, study wave, and stratification factors (BMI category, self-reported ethnicity, study centre)c The cost of RUFIT-NZ was $752 per participant; no costs were incurred for participants in the control arm
## Discussion
The aim of our study was to determine the effectiveness of the 12-session, healthy lifestyle RUFIT-NZ program on weight loss, fitness, blood pressure, and lifestyle change at 12- and 52-weeks. Overall, our findings showed the program resulted in sustained changes in weight, waist circumference, physical fitness, self-reported physical activity, alcohol consumption, and HRQoL in overweight men. The RUFIT-NZ intervention, as assessed through cost per kg body weight loss and QALYs gained, was broadly comparable with other economic evaluations of weight loss interventions and was highly likely to be cost-effective at a willingness-to-pay threshold of $45,000 NZD per QALY [45, 46]. The modest gain in QALYs is likely attributed to the short time period (one year) of the evaluation, as the key benefits attributed to weight loss interventions lie in the prevention of downstream events of morbidity and mortality associated with obesity [45, 46]. As such, a separate cost-effectiveness analysis exploring the impact of ongoing weight reduction through RUFIT-NZ on lifetime morbidity and mortality risk is planned. The cost-effectiveness of RUFIT-NZ, in terms of cost per QALYs, was not maintained in a sensitivity analysis of non-imputed data; this is likely attributed to the high proportion of missing HRQoL data.
Strength and limitations: We conducted a pragmatic RCT based on extensive feasibility and pilot work [9]. The inclusion of three Super Rugby franchises located in different parts of the country with substantially distinct ethnic compositions (Auckland has a larger proportion of Māori and Pacific peoples compared with Dunedin) [43], enhanced the generalizability of the findings. Of the various adaptations and wider implementation of FFIT [47], RUFIT-NZ included a wider inclusion of a country’s major ethnic minority populations. Specifically, we recruited a large proportion of Māori ($18\%$) and Pacific ($16\%$) participants, which is representative of the respective groups’ population in NZ, and highly relevant to the NZ context given the burden of overweight and obesity in these groups.
The major limitations of this trial were related to COVID-19 disruptions. A recent paper highlighted the effect of the COVID-19 pandemic and the need to acknowledge COVID–related changes to studies [48]. Due to COVID-19 enforced lockdowns and restrictions we were unable to include the third and final wave of participants in the analyses. While we had recruited and randomized those participants, we were unable to deliver the intervention, and thus, in consultation with our trial steering committee, we made a pragmatic decision to analyze only those participants from the first two waves. Notwithstanding those issues, we had sufficient power to detect differences in the primary outcome between groups. This was a reflection of the estimated SD (12.0) used for the sample size calculation, which was larger than the observed SD (5.0) in the present study. Other limitations of our trial include the lack of blinded outcome assessments, and potential contamination, however we were unaware of any participants who were randomized to RUFIT-NZ but engaged with control participants at the same club. Finally, while we undertook extensive efforts to follow-up participants, there remained a large proportion of missing data. Findings from this trial should be interpreted with those collective limitations in mind.
The sustained weight loss (2.7 kg) at 52 weeks was slightly larger than observed in our RUFIT-NZ pilot study at 12-weeks [9], and was similar to that observed in the trial of the EuroFIT healthy lifestyle program [49] across four countries (England, Norway, Netherlands and Portugal) (mean between groups difference in weight at 12-months -2.4 kg, $95\%$CI -3,1 to -1.7). However, it was less than the 12-month weight loss in FFIT (mean difference in weight loss at 12-months -4.94 kg, $95\%$ CI 3.95–5.94) [7]. Reasons for these differences are unclear. While RUFIT-NZ was inspired by FFIT, there were some differences in intervention delivery. Specifically, while trainers largely delivered education content and training sessions in RUFIT-NZ, a nutritionist delivered the nutrition content. The program was also modified to meet the cultural needs of NZ men. Despite these differences, compared with previous studies, RUFIT-NZ produced similarly positive changes in physical activity levels, fruit and vegetable consumption (but attenuated at 52-weeks), and HRQoL. However, in contrast to FFIT we did not find an effect on blood pressure.
Given the considerable burden of overweight and obesity on Māori and Pasifika men, we aimed to recruit $50\%$ Māori. While we did not reach this target, our study was successful in recruiting large numbers of both Māori and Pasifika men. Results from our present study highlight the potential for our RUFIT-NZ to be implemented at-scale to engage these populations and to support positive lifestyle changes in a culturally acceptable way. Findings from our pilot trial demonstrated NZ men found RUFIT-NZ was acceptable [9] and features within the program (creation of a team environment, motivating coach, knowledge gained from education sessions) and those men brought to the program (motivation, support of others) created the engagement with it [10]. Combined with experience from FFIT that has shown that word-of-mouth recommendations by past participants to be one way of sustaining delivery [47], we might feasibly expect that more Māori and Pasifika men may be attracted to the program in future on the basis of the positive experience of their peers who took part in RUFIT-NZ.
## Conclusion
RUFIT-NZ resulted in sustained positive changes in weight, waist circumference, physical fitness, self-reported physical activity, selected dietary outcomes, and HRQoL in overweight men. As such, the program should be recommended for sustained delivery beyond this trial and might include other rugby clubs across NZ.
## Supplementary Information
Additional file 1. CONSORT checklist. Additional file 2. The TIDieR checklist.
## Appendices
Table 6RUFIT-NZ workshop contentWeekTopics/Core Messages to CoverTarget behaviorBehavior change technique addressedFacilitatorWeek 1• Welcome & getting to know each other• Focus on lifestyle behaviors vs weight• SMART Goal setting• Team photoExercise/ physical activityGoal setting and intention formationSelf-monitoringSocial support and encouragementTrainerWeek 2• Whole food philosophy including social aspects• Big wins for healthy diets• Incorporating fruit and veg• Healthy drink options• Healthy serving sizes• Reading food labelsNutritionIdentifying autonomous reasons for lifestyle changeGoal setting for, and self-monitoring of healthy dietNutritionistWeek 3• Menu planning• Budgeting• Shopping• Being organized/importance of routineNutritionGoal setting for, and self-monitoring of healthy dietIntention formation with action plansNutritionistWeek 4• Discussion of important behavior change techniques: Autonomous motivation, building confidence, goal setting, action & coping planning, self-monitoring, social supportExercise and physical activityIdentifying autonomous reasons for lifestyle changeGoal settingSelf-monitoringSocial support and encouragementTrainerWeek 5• Focusing on your ‘circle of influence’• Eating out Mindful eatingNutritionSelf-monitoring, social support, goal setting with intention formation with action plans Nutritionist Week 6• Informal session designed by trainer to meet individual needs of men in teamPhysical activityIdentification of barriers and coping planning, self-monitoring and review of progressTrainerWeek 7• Question and Answers sessionNutritionIdentification of barriers and coping planningNutritionistWeek 8• Alcohol weight-related facts• Standard drink sizes• Planning your drinkingNutritionAlcoholGoal setting for behavior and outcome; discrepancy between current behavior and goal; health consequencesTrainerWeek 9• Questions and Answers sessionPhysical activityIdentification of barriers and coping planning, review of past progress and goal setting, social support and encouragementTrainerWeek 10• How sleep affects weight• How much sleep we need• Signs of sleep deprivation• Sleep hygiene tips• *What is* sedentary behavior• How sedentary behavior affects weight• Tips for reducing SBSleep and sedentary behaviorSelf-monitoring of behavior and outcome of behavior, goal setting for behavior and goal setting for outcome; problem-solvingTrainerWeek 11• Importance of enjoying physical activity for long-term maintenance• Long-term behavior change & overcoming obstacles• Planning for lifestyle change• Relapse preventionPhysical activity Social support/encouragement; goal setting for behavior and outcomeTrainerWeek 12• Wrap-up• Motivational talk• Team photo & CertificatesPhysical activity and nutritionFocus on past success; verbal persuasion to boost self-efficacy; goal setting for behavior and outcome; problem-solvingTrainer Table 7Participant baseline demographic data comparing all randomized participants ($$n = 378$$) with those who completed study ($$n = 200$$) Total cohort Wave 3 No Yes N % N % N % 378100.0200100.0178100.0 Study site Blues19651.910050.09653.9 Crusaders7419.64422.03016.9 Highlanders10828.65628.05229.2 BMI category < 35 kg/m221155.810954.510257.3 > = 35 kg/m216744.29145.57642.7 Ethnicity Maori5614.83718.51910.7 Pacific6316.73216.03117.4 New Zealand European/other25968.513165.512871.9 Marital Status Married24765.313668.011162.4 Civil union/living with partner6216.43618.02614.6 Divorced, separated or widowed359.3168.01910.7 Never married (single)256.673.51810.1 Choose not to answer92.452.542.2 Annual Household Income Less than $15,00051.321.031.7 $15,000—$29,99982.142.042.2 $30,000—$59,9994311.42311.52011.2 $60,000—$99,99911831.26231.05631.5 Table 8Derivation of RUFIT-NZ program costsCost parameterValueCoaching$40,293Printing$3,327Phone$120Travel$1,365T-shirts$4,825Advertising$2,714Staff costs$26,109 Total intervention cost $77,469 Cost per participant $752
## Study
materials
Study materials are not publically available. People interested in delivering RUFIT-NZ can contact the research team to discuss.
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|
---
title: 'Association between depression and dysmenorrhea among adolescent girls: multiple
mediating effects of binge eating and sleep quality'
authors:
- Yingzhen Li
- Baixue Kang
- Xueyan Zhao
- Xuena Cui
- Jie Chen
- Lijie Wang
journal: BMC Women's Health
year: 2023
pmcid: PMC10043526
doi: 10.1186/s12905-023-02283-6
license: CC BY 4.0
---
# Association between depression and dysmenorrhea among adolescent girls: multiple mediating effects of binge eating and sleep quality
## Abstract
### Background
Dysmenorrhea has a significant negative impact on teenagers’ quality of life, and its prevalence is increasing annually. Although studies have explored the factors affecting dysmenorrhea, it remains unclear how these factors interact with one another. This study aimed to explore the mediating role of binge eating and sleep quality between depression and dysmenorrhea.
### Methods
This cross-sectional study recruited adolescent girls from the Health Status Survey of adolescents in Jinan, Shandong Province, and used multistage stratified cluster random sampling. Data was collected using an electronic questionnaire between March 9, 2022, and June 20, 2022. The Numerical Rating Scale and Cox Menstrual Symptom Scale were used to assess dysmenorrhea and the Patient Health Questionnaire-9 to assess depression. The mediation model was tested by Mplus 8.0, and the mediating effect was analyzed using the Product of Coefficients approach and the Bootstrap method.
### Results
Among the total of 7818 adolescent girls included in this study, the prevalence of dysmenorrhea is $60.5\%$. A significant positive association was found between dysmenorrhea and depression. Binge eating and sleep quality seemingly mediate this association. The mediating effect of sleep quality ($21.31\%$) was greater than that of binge eating ($6.18\%$).
### Conclusions
The findings of this study point in the right direction for preventing and treating dysmenorrhea in adolescents. For adolescent dysmenorrhea, mental health should be considered and proactive steps taken for educating adolescents on healthy lifestyles to reduce negative consequences of dysmenorrhea. Longitudinal studies on the causal link and influence mechanisms between depression and dysmenorrhea should be conducted in the future.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12905-023-02283-6.
## Introduction
Dysmenorrhea is a type of pain that includes symptoms such as lower abdominal cramps and other discomfort before and/or during menstruation [1]. It can be divided into primary and secondary dysmenorrhea [2]. Primary dysmenorrhea is characterized by lower abdominal pain without organic lesions, whereas secondary dysmenorrhea is caused by organic lesions in the pelvis [3]. A series of physiological-psychological-nerve-endocrine changes occur in adolescence [4], and menstrual health specifically is affected by complex physiological and psychological changes [5]. According to a previous study, dysmenorrhea has the highest prevalence ($89.7\%$) of menstrual disorders [6]. The prevalence of dysmenorrhea in adolescents varies greatly across the globe, with previous studies indicating a prevalence ranging from $20\%$-$90\%$ [7]. For example, the prevalence of dysmenorrhea in adolescents is $77.8\%$ in Osogbo [8], $38.1\%$ in Lebanon [9], and $89\%$ in Sweden [10]. In recent years, $16\%$–$93\%$ of adolescent girls reported dysmenorrhea [11]. Dysmenorrhea has a direct adverse impact on the quality of life [11]; for instance, dysmenorrhea affects women’s social lives and students’ academic performance due to issues including absenteeism and inability to participate in physical activities [12].
Studies have shown that negative emotions may aggravate dysmenorrhea [13], but the mechanism is not completely clear. Depression is a common negative emotion. During the epidemic period of the coronavirus disease 2019 (COVID-19), the incidence of depression increased and was much higher in girls ($14.65\%$) than in boys ($9.04\%$) [14–16]. Furthermore, studies have shown that the frequency of depressive symptoms after SARS-CoV-2 infection ranges from 11 to $28\%$ [17]. Therefore, it is necessary to further explore the possible mechanisms between depression and dysmenorrhea for more comprehensive dysmenorrhea management and treatment.
According to the psychodynamic hypothesis, behavior is primarily influenced by psychological forces, leading to the development of physical symptoms [18, 19]. Behavioral psychology theory points out that implicit psychology predominates over explicit actions because the main objective of psychology is to predict and control behavior [20]. Therefore, we hypothesized that depression may trigger dysmenorrhea through certain behavioral factors. Previous research has shown that eating behavior can mediate the association between depression and other diseases such as obesity and cardiovascular disease [21, 22]. Moreover, a school-based study in Australia found that $12.4\%$ of adolescents reported binge eating at least once per week [23]. Another study in Bahrain showed that $21.2\%$ of young people engaged in binge eating [24]. However, no study has examined whether dietary behaviors mediate the association between depression and dysmenorrhea. In addition, a study has shown that $52.7\%$ of adolescents have poor sleep quality [25]. During adolescence, the prevalence of sleep disorders is higher in girls than in boys [26]. Sleep quality has a partially mediated effect on depression and pain [27]; however, it is unclear whether this relationship holds true for dysmenorrhea.
Individuals with binge eating behaviors reportedly have hypersensitivity to certain interoceptive signals [28]. Therefore, binge eating may aggravate the discomfort associated with dysmenorrhea. Depression is related to eating disorders [29], and even predicts eating disorders during adolescence [30]; therefore, we hypothesized that depression can indirectly affect dysmenorrhea through binge eating behavior. To date, studies have found no significant association between dysmenorrhea and factors including sleep work patterns and sleep time, but there is a significant association between dysmenorrhea and sleep quality [31]. Students with depression were 2.47 times more likely to develop sleep disorders than other students [32]. Therefore, we hypothesized that depression could indirectly affect dysmenorrhea by affecting sleep quality. Additionally, excessive carbohydrate intake is harmful to sleep quality [33], therefore, based on psychodynamic and behavioral psychology theories, we hypothesized that depression can indirectly affect sleep quality through binge eating behavior.
Depression, binge eating behavior, and poor sleep quality are all risk factors for dysmenorrhea; however, no study has identified the interactional mechanisms between these risk factors. In previous studies, the diagnostic criteria for dysmenorrhea were different [34], and there are few studies with multicenter, large samples reporting dysmenorrhea in adolescents since the COVID-19 outbreak. Therefore, our research is devoted to addressing the following problems:The current prevalence of dysmenorrhea among adolescent girls;The correlation between depression and dysmenorrhea; andThe mediating effects of binge eating and sleep quality on depression and dysmenorrhea.
## Participants
Data were collected from a cross-sectional study conducted in Jinan, Shandong Province, from March 9, 2022, to June 20, 2022, that aimed to explore the health status of adolescents. The respondents were junior and senior high school students. Information on sociodemographic characteristics, mental health, lifestyle, and menstruation-related variables were included. The menstruation-related questions were only asked for girls to answer. We use the stratified multistage random sampling to select participants from all districts and counties in Jinan (10 districts and 2 counties). In the first stage, according to the probability proportionate-to-size sampling method (PPS) [35], 1 junior high school and 1 senior high school were randomly selected from 10 districts, 3 junior high schools and 1 senior high school from 2 counties, and a total of 6 vocational high schools from all districts and counties of Jinan. A total of 16 junior, 12 senior, and 6 vocational high schools were selected. In the second stage, based on the PPS, 3–5 classes were selected separately from each grade of each school, and a total of 443 classes were selected. Lastly, all students in the selected class completed an electronic questionnaire. In the Health Status Survey of Adolescents in Jinan, Shandong Province, a total of 17,703 questionnaires were collected, of which 8,685 were girls and 9018 were boys, for a response rate of $86.7\%$. In this study, the 8685 girls from the Health Status Survey of Adolescents were included. Information about the Cox Menstrual Symptom Scale (CMSS), the Patient Health Questionnaire-9 (PHQ-9), binge eating, sleep quality, and sociodemographic characteristics from the questionnaire were all incorporated into this study. The inclusion criterion for this study was middle and high school girls aged 10 to 20 years; the exclusion criterion was students who had not yet reached menarche. In addition, questionnaires with incomplete answers were discarded during the data analysis process. In total, 7818 eligible female respondents were included in this study. The effectiveness rate of the questionnaire was $90.02\%$.
The Public Health Ethics Committee of Shandong University reviewed and approved the study protocol (approval number: LL20211116). All participants provided informed consent.
## Dysmenorrhea
The severity and duration of dysmenorrhea symptoms were evaluated using the CMSS (see Supplementary Table 1). The scale was developed by Professor Daniel J. Cox in 1978 to comprehensively evaluate the severity and duration of dysmenorrhea symptoms [36]. The Cronbach's alpha coefficient of the Cox Menstrual Symptom Scale of the Chinese version was 0.833 and the KMO was 0.811 [37]. This scale contains 18 items, each with two assessment dimensions: severity and duration of dysmenorrhea symptoms. The 18 items include: “general aching, headaches, stomachache, backaches, cramps, leg aches, dizziness, facial blemishes, flushing, nausea, vomiting, loss of appetite, diarrhea, weakness, insomnia, gloomy, irritability, and nervousness.” In the severity level assessment, each symptom was scored on a 5-point scale: 0 = no discomfort, 1 = mild discomfort, 2 = moderate discomfort, 3 = severe discomfort, and 4 = very severe discomfort. In the duration level assessment, each symptom was also scored on a 5-point scale: 0 = none, 1 = lasting 0–3 h, 2 = lasting 3–7 h, 3 = lasting 7–24 h, and 4 = lasting > 24 h. The total score of the two dimensions can be calculated to comprehensively evaluate dysmenorrhea symptoms. The total score ranges from 0 to 144. The higher the score, the more severe the dysmenorrhea. In this study, the Cronbach's alpha coefficient of the Cox Menstrual Symptom Scale was 0.963 and the KMO was 0.897.
Using the Numerical Rating Scale (NRS) to classify the severity of dysmenorrhea. According to previous research, the NRS has been widely used in various studies to assess the degree of pain, and it has high reliability and validity [38]. Dysmenorrhea was categorized into 11 levels: 0 = no dysmenorrhea, 1–3 = mild pain, 4–6 = moderate pain, 7–9 = severe pain, and 10 = very severe pain.
## Depression
The depression symptoms in the past 2 weeks were assessed using the PHQ-9 (see Supplementary Table 2), a study has confirmed that the PHQ-9 is a reliable and valid measure of depression [39]. The questionnaire consisted of nine items: “1. Little interest or pleasure in doing things.” “ 2. Feeling down, depressed, or hopeless.” “ 3. Trouble falling or staying asleep, or sleeping too much.” “ 4. Feeling tired or having little energy.” “ 5. Poor appetite or overeating.” “ 6. Feeling bad about yourself—or that you are a failure or have let yourself or your family down.” “ 7. Trouble concentrating on things, such as reading the newspaper or watching television.” “ 8. Moving or speaking so slowly that other people could have noticed, or the opposite—being so fidgety or restless that you have been moving around a lot more than usual.” “ 9. Thoughts that you would be better off dead or of hurting yourself in some way.” Each item rated on a 4-point scale (0 = never, 1 = for a few days, 2 = just over half of the days, and 3 = almost daily). The total score ranged from 0 to 27, with a total score of 0–4 indicating no depression, 5–9 indicating mild depression possibly, 10–14 indicating moderate depression possibly, 15–19 indicating moderate-to-severe depression possibly; and 20–27 indicating severe depression possibly. In this study, Cronbach’s alpha coefficient of the PHQ-9 was 0.925 and the KMO was 0.945.
## Mediating variables
Mediating variables included binge eating and sleep quality. Binge eating is when you eat more food than the majority of people do in comparable circumstances and you are unable to stop, regulate what you eat, or regulate how much you eat [40]. Sleep quality refers to how satisfied you are with your sleeping experience. Good sleep quality means that you feel rested and refreshed. You enjoy your sleep and have fulfilling familial and social interactions [41]. Binge eating was classified as frequent, occasional, or never, and sleep quality was classified as good, general, or poor.
## Covariates
We controlled for age (10–13 years, 14–17 years, 18–20 years), school type (junior high school, senior high school, vocational high school), parental education level (junior high school and below, senior high school/technical secondary school, bachelor’s/college degree and above), boarding at school (yes = 1, no = 0), maternal history of dysmenorrhea (unclear, yes, no), and average sleep duration per night (excessive, normal, inadequate).
## Statistical analysis
Statistical analysis was performed using SPSS version 26.0 and Mplus version 8.0. We tested whether the data followed a normal distribution before analysis. We present sample characteristics as frequency (percentage). Nonparametric tests (Kruskal–Wallis test and Mann–Whitney U test) were performed to compare the total CMSS scale scores in subgroups of different categorical variables. In addition, the main study variables (depression level, binge eating, sleep quality, and dysmenorrhea symptoms) were tested using Spearman’s rank correlation.
Finally, Mplus version 8.0 was used to test the mediating effect of binge eating and sleep quality on depression and dysmenorrhea. A significant mediation effect was demonstrated if the $95\%$ confidence intervals (CIs) of the interaction did not contain 0 (effect test $p \leq 0.05$), and $95\%$ bias-corrected CIs was yielded by 5,000 bootstrap estimates. Statistical significance was defined as a two-tailed p-value of < 0.05.
## Sample characteristics
The mean age of the 7,818 adolescent girls included in this study was 15.71 ± 1.617 years. The prevalence of dysmenorrhea is $60.5\%$ ($$n = 4732$$), and that of mild, moderate, severe, and very severe dysmenorrhea was $12.8\%$, $33.7\%$, $12.4\%$, and $1.6\%$, respectively. In addition, information about the frequency of CMSS and PHQ-9 are shown in Supplementary Table 3 and 4.
Table 1 summarizes the sociodemographic information of the 4,732 girls with dysmenorrhea and the univariate analysis of the total CMSS scale score in the subgroups of different category variables. Univariate analysis showed that age, type of school, boarding at school, maternal history of dysmenorrhea, sleep duration, sleep quality, and binge eating were significantly associated with dysmenorrhea. Table 1Characteristics of the sample ($$n = 4732$$)VariablesN(%)CMSS ScoreH/ZPM(P25-P75)Observation variable4732(100.0)27.0(14.00–45.00)Age59.259a0.000 10–13394(8.3)19.0(8.75–34.00) 14–173611(76.3)27.0(14.00–46.00) 18–20727(15.4)30.0(15.00–48.00)Type of school173.956a0.000 Junior high school1776(37.5)21.0(10.00–37.00) Vocational high school844(17.8)33.0(18.00–56.00) Senior high school2112(44.6)30.0(15.25–48.00)Father's education0.586a0.746 Junior high school and below2371(50.1)27.0(14.00–45.00) Senior high school1242(26.2)26.0(13.00–45.00) Bachelor’s/college degree and above1119(23.6)27.0(14.00–45.00)Mother’s education3.582a0.167 Junior high school and below2677(56.6)26.0(13.00–45.00) Senior high school1101(23.2)26.0(13.00–45.00) Bachelor’s/college degree and above954(20.2)29.0(14.00–46.00)Boarding at school2.198b0.028 Yes2130(45.0)28.0(14.00–47.00) No2602(55.0)26.0(14.00–44.00)Mother's history of dysmenorrhea49.071a0.000 Yes2305(48.7)29.0(16.00–48.00) No1050(22.2)23.0(10.00–41.00) No clear1377(29.1)27.0(14.00–45.00)Sleep duration61.692a0.000 Inadequate3348(70.8)29.0(15.00–47.00) Normal1351(28.6)23.0(10.00–40.00) Excessive33(0.7)30.0(21.00–59.50)Sleep quality906.332a0.000 Good1953(41.3)17.0(8.00–32.00) General2195(46.4)31.0(18.00–46.00) Poor584(12.3)55.0(36.00–76.00)Binge eating265.551a0.000 Often126(2.7)43.0(19.75–69.00) Sometimes1223(25.8)36.0(21.00–56.00) Never3383(71.5)23.0(11.00–40.00)CMSS the Cox Menstrual Symptom ScaleaKruskal-wallis testbMann-Whitney U test
## Preliminary analyses
Table 2 presents the Spearman’s rank correlation coefficients for the main study variables. Depression was positively associated with dysmenorrhea ($r = 0.543$, $p \leq 0.01$), binge eating behavior ($r = 0.262$, $p \leq 0.01$), and poor sleep quality ($r = 0.334$, $p \leq 0.01$). Binge eating behavior was positively associated with dysmenorrhea ($r = 0.237$, $p \leq 0.01$) and poor sleep quality ($r = 0.112$, $p \leq 0.01$). Poor sleep quality was positively associated with dysmenorrhea ($r = 0.428$, $p \leq 0.01$).Table 2The correlations among the main study variablesVariables1234Depression score1CMSS score0.543**1Binge eating0.262**0.237**1Sleep quality0.334**0.428**0.112**1CMSS the Cox Menstrual Symptom Scale** $p \leq 0.01$
## Testing for the mediating effect
The mediator model showed an acceptable fit: χ2/df = 1.705, CFI = 0.994, TLI = 0.990, RMSEA = 0.012, and SRMR = 0.059.
As shown in Table 3 and Fig. 1, binge eating and sleep quality had partial mediating effects on depression and dysmenorrhea, respectively. The difference in the mediating effects between binge eating and sleep quality was statistically significant (Table 4), and the mediating effect of sleep quality ($21.31\%$) was greater than that of binge eating ($6.18\%$); however, the binge eating to sleep quality pathway was not statistically significant ($$P \leq 0.213$$).Table 3The chained mediation model and $95\%$ CIsModel pathwaysPointEstimateProduct of CoefficientsBootstrap 5000 Times$95\%$CIS.EEst./S.EP ValueLowerUpperPath A0.1070.00813.6440.0000.0920.122Path B0.0310.0056.2170.0000.0220.042Path C0.0020.0021.2450.213-0.0020.006Path D0.3620.01820.4750.0000.3230.391Total indirect0.1400.00915.5180.0000.1230.158Total0.5020.01534.5420.0000.4670.565Adjusted for age, school type, educational level of parents, boarding at school, mother's history of dysmenorrhea, and sleep duration. The values listed in the table are all standardized values. S.E Standard errorPath A: Depression- > Sleep Quality- > DysmenorrheaPath B: Depression- > Binge Eating- > DysmenorrheaPath C: Depression- > Binge Eating- > Sleep quality- > DysmenorrheaPath D: Depression- > DysmenorrheaFig. 1Mediating effects of binge eating and sleep quality on depression and dysmenorrhea. Note: adjusted for age, school type, educational level of parents, boarding at school, mother's history of dysmenorrhea, and sleep duration. Standardized pathway coefficients are shown outside in parentheses, and standard errors are shown in parentheses, * $p \leq 0.01$Table 4The parallel mediation, $95\%$ CIs and comparison between the mediation effectsModel pathwaysPointEstimateProduct of CoefficientsBootstrap 5000 Times$95\%$CIS.EEst./S.EP ValueLowerUpperIndirect Effects Path AB10.1530.0236.6330.0000.1120.205 Path AB20.5000.03514.2050.0000.4360.575 Total indirect0.6530.04315.0630.0000.5780.747 Direct1.6260.08020.2250.0001.4641.775 Total2.2790.04732.1350.0002.1372.418Contrast Path AB1 vs. Path AB2-0.3470.041-8.4910.000-0.428-0.270Note: adjusted for age, school type, educational level of parents, boarding at school, mother's history of dysmenorrhea, and sleep duration. The values listed in the table are all non-standardized values. S.E Standard errorPath AB1: Depression- > Binge eating- > DysmenorrheaPath AB2: Depression- > Sleep Quality- > Dysmenorrhea
## Discussion
More than half of the adolescent girls in this study experienced dysmenorrhea, with more than one-third of those experiencing moderate dysmenorrhea. This is inconsistent with the results of the prior study, which found that the prevalence of mild dysmenorrhea had the highest proportion [31]. Although the overall prevalence in our study is high, it is lower than the results of said previous study [31]. A possible reason for this is that there are no uniform diagnostic criteria for dysmenorrhea. Additionally, it might be because the COVID-19 pandemic has affected the prevalence of dysmenorrhea in teenagers in recent years.
The current study focused on the relationship between depression and dysmenorrhea in adolescent girls and revealed the possible mechanisms underlying the relationship between depression and dysmenorrhea through a mediation model. We found a positive association between depression and dysmenorrhea. Binge eating and sleep quality had a partial mediating effect on depression and dysmenorrhea, respectively. The mediating effect of sleep quality ($21.31\%$) was greater than that of binge eating ($6.18\%$).
Three possible pathways may explain the association between depression and dysmenorrhea. In the physiological pathway, we hypothesize that because depression and other bad emotions can result in psychological imbalance and neuroendocrine abnormalities, which in turn stimulate the uterus, increased uterine isthmus tension can either cause or worsen dysmenorrhea. Studies have shown that the monoamine hypothesis of depression states that the neurotransmitter-serotonin release function is weakened in the brains of patients with depression [42], and serotonin release can participate in the inhibition of some types of pain [43]. Therefore, patients with depression may be more sensitive to dysmenorrhea. In the behavioral pathway, we mainly considered dietary behavior, which was represented by binge eating. In the sleep pathway, we primarily considered sleep quality.
Our results show that the association between depression and dysmenorrhea is partially mediated by binge eating, probably revealing one of the dietary behavioral mechanisms regarding how depressive symptoms indirectly affect dysmenorrhea symptoms. We think that one of the ways that depression indirectly influences dysmenorrhea through binge eating is that depressed people may prefer to eat to soothe an unpleasant mood, and that both binge eating and depression may lead to endocrine abnormalities that cause or worsen dysmenorrhea. A previous study has shown that the gray matter volume increases in the left anterior abdominal insula of people with binge eating behavior, causing hypersensitivity [44], which may aggravate the uncomfortable feeling of dysmenorrhea. Depressed people have elevated cortisol levels [45], elevated cortisol levels increase food intake [46], which may contribute to the significant positive relationship between depression and binge eating behavior.
Our results also showed that the association between depression and dysmenorrhea is partially mediated by sleep quality, probably revealing one of the sleep mechanisms regarding how depressive symptoms indirectly affect dysmenorrhea symptoms. We believe that sleep problems are almost always a common sign of depression and that poor sleep quality is likely to interfere with the production of prostaglandins, which can cause dysmenorrhea. Experiments have shown that prefrontal density declines in the brains of individuals with depression. In this region, genes that control circadian rhythms regulate sleep dysregulation [47]. The effect of sleep quality on dysmenorrhea may be due to overlapping central nervous system mechanisms [48]. In this study, the path coefficient for binge eating-sleep quality was not significant. However, previous research found a link between dietary habits and sleep problems [33]. It is possible that other mechanisms influence the relationship between dietary behaviors and sleep disorders, and further research is required to clarify this relationship.
Our study has several public health implications. Government can implement measures to prevent and relieve dysmenorrhea in adolescents by focusing on the prevention and treatment of depression and educating teenagers to develop good living habits. Firstly, relevant personnel should pay attention to the psychological problems of teenagers, especially in the context of the normalization of the COVID-19 epidemic because they frequently arise in this context [49]. Teenagers are an especially vulnerable group, relevant departments should pay more attention to their mental health and provide necessary psychological support services. Secondly, educators need to correct students' living habits, to maintain good eating behaviors, especially educate young people in developing good sleep habits.
To the best of our knowledge, few studies have the sample size included in this study. For the first time, our study examined the interaction mechanisms of psychological factors (depressive symptoms), dietary behavioral factors (binge eating behavior), and sleep problems (sleep quality) on dysmenorrhea in adolescents. Most previous studies of dysmenorrhea focused only on the unidirectional dimension of the severity of lower abdominal pain, whereas the CMSS covers a broader range of dysmenorrhea symptoms and dimensions.
The limitations of our study can provide directions for further research. Firstly, the data collection with a structured self-report questionnaire could not exclude the recall bias. Secondly, the cross-sectional study examined only the relationship between depression and dysmenorrhea, making it difficult to conclude causal inferences between depression and dysmenorrhea. Furthermore, using a self-report questionnaire rather than a clinical diagnosis, the judgment of dysmenorrhea may not be completely correct, and there is no way to distinguish between primary dysmenorrhea and secondary dysmenorrhea. In the future, a combination of clinical diagnostic techniques will be needed to distinguish between primary and secondary dysmenorrhea for further research. Finally, this study can only represent the situation of adolescent girls in Jinan City, Shandong Province; whether the conclusions can be generalized to other regions is unknown. Further longitudinal studies should be conducted on the causal relationship and mechanism of influence between depression and dysmenorrhea. Multicenter, large-sample research should be conducted in the future. Simultaneously, investigators should combine public health technology with clinical diagnostic techniques.
## Conclusion
Sleep quality and binge eating partially mediate the positive association between depression and dysmenorrhea. The mediating effect of sleep quality ($21.31\%$) was greater than that of binge eating ($6.18\%$). Our results encourage educators to concentrate on the mental health problems of adolescents while educating them to develop good life habits to prevent and relieve dysmenorrhea symptoms, further improving the quality of life of adolescents. In the future, longitudinal studies and multicenter, large-sample research should be conducted. Simultaneously, we should combine public health technology with clinical diagnostic techniques.
## Supplementary Information
Additional file 1: Supplementary Table 1. The Cox Menstrual Symptom Scale. Supplementary Table 2. The Patient Health Questionnaire-9. Supplementary Table 3. Distribution of frequency of the CMSS. Supplementary Table 4. Distribution of frequency of the Questions in PHQ-9.
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|
---
title: Caspase 6/NR4A1/SOX9 signaling axis regulates hepatic inflammation and pyroptosis
in ischemia-stressed fatty liver
authors:
- Mingwei Sheng
- Yiqi Weng
- Yingli Cao
- Chen Zhang
- Yuanbang Lin
- Wenli Yu
journal: Cell Death Discovery
year: 2023
pmcid: PMC10043527
doi: 10.1038/s41420-023-01396-z
license: CC BY 4.0
---
# Caspase 6/NR4A1/SOX9 signaling axis regulates hepatic inflammation and pyroptosis in ischemia-stressed fatty liver
## Abstract
The mechanism of nonalcoholic fatty liver susceptibility to ischemia/reperfusion (IR) injury has not been fully clarified. Caspase 6 is a critical regulator in innate immunity and host defense. We aimed to characterize the specific role of Caspase 6 in IR-induced inflammatory responses in fatty livers. Human fatty liver samples were harvested from patients undergoing ischemia-related hepatectomy to evaluate Caspase 6 expression. in mice model, we generated Caspase 6-knockout (Caspase 6KO) mice to investigate cellular and molecular mechanisms of macrophage Caspase 6 in IR-stimulated fatty livers. In human liver biopsies, Caspase 6 expression was upregulated combined with enhanced serum ALT level and severe histopathological injury in ischemic fatty livers. Moreover, Caspase 6 was mainly accumulated in macrophages but not hepatocytes. Unlike in controls, the Caspase 6-deficiency attenuated liver damage and inflammation activation. Activation of macrophage NR4A1 or SOX9 in Caspase 6-deficient livers aggravated liver inflammation. Mechanistically, macrophage NR4A1 co-localized with SOX9 in the nuclear under inflammatory conditions. Specifically, SOX9 acts as a coactivator of NR4A1 to directly target S100A9 transcription. Furthermore, macrophage S100A9 ablation dampened NEK7/NLRP3-driven inflammatory response and pyroptosis in macrophages. In conclusion, our findings identify a novel role of Caspase 6 in regulating NR4A1/SOX9 interaction in response to IR-stimulated fatty liver inflammation, and provide potential therapeutic targets for the prevention of fatty liver IR injury.
## Introduction
Improving the utilization rate of marginal donor liver (e.g., from donors with fatty liver) is an important strategy to overcome the clinical problem of donor shortage [1, 2]. Compared with normal liver, steatotic donor liver is more sensitive to ischemia /reperfusion (IR) injury, and the risk of early postoperative graft dysfunction or even nonfunction is doubled [3]. However, despite the obvious clinical importance, there are no effective intervention strategies to prevent this condition in humans. Thus, it is urgently needed to explore the underlying molecular mechanism and corresponding therapeutics for IR injury in steatotic donor livers.
Caspase 6, a member of the caspase family, has been reported as a non-essential apoptotic executor protein and involved in many biological processes, such as cell apoptosis and proliferation [4]. Recent findings suggest that Caspase 6 can act as a key regulatory molecule to regulate immune responses and cell death [5]. In response to influenza A virus infection, Caspase 6 participated in regulation of inflammasome formation in a non-protease-dependent manner [6]. Zhao et al. [ 7] found that an AMPK activator inhibited hepatocyte apoptosis by blocking Caspase 6 activity, thereby preventing the progression from fatty liver to nonalcoholic steatohepatitis (NASH) and subsequent hepatocyte death. Moreover, administration of the chemical inhibitor of Caspase 6 greatly restricted adaptive MERS-CoV replication in mice and increased their survival rate from 33.3 to $80\%$ [8]. However, the specific role of macrophage-derived Caspase 6 in IR injury of fatty liver remains unclear.
Nuclear receptor subfamily 4 group A (NR4A) family members include Nur77/NR4A1, Nurr1/NR4A2, and Nor1/NR4A3. As a master factor of stress response, NR4A1 acts as a transcriptional activator or repressor by binding to DNA or proteins and plays critical roles in various pathophysiological processes such as immune regulation, energy metabolism, and tumorigenesis [9, 10]. Genome-wide analysis identified NR4A1 as a key inducer and general mediator of T cell dysfunction, making it a potential target for development of tumor immunotherapy [11]. NR4A1 is lowly or not expressed in macrophages under physiological conditions. However, in response to harmful stimuli, NR4A1 can be activated in various types of macrophages [12]. Previous studies found that the mutation of small ubiquitin-like modifier (SUMO) modification sites in NR4A1 enhanced its stability and the release of inflammatory cytokines from macrophages [13]. Yet, this is no report exploring the relation between NR4A1 and Caspase 6 to affect the inflammatory response of fatty liver IR.
Sex-determining Y box (SOX) genes are widely conserved in mammals and have been involved in biological processes including embryonic development, cell differentiation, and inflammatory responses [14]. Thus far, more than 20 SOX genes have been discovered in vertebrates, with SOX9 being the most extensively studied [15]. Previous study demonstrated that SOX9 attenuated acute kidney injury by regulating Wnt/β-catenin signaling [16]. In contrast, a Tomo-sequencing assay of ischemic myocardium identified collagen I-positive fibroblast-derived SOX9 as the key regulator of IR-induced myocardial fibrosis [17]. Accordingly, the functions of SOX9 in various cell types and cellular activities are still controversial.
Herein, we hypothesized that Caspase 6 is a key component in activating the innate immune response in IR-induced fatty liver. First, liver structure, function, and Caspase 6 expression in patients undergoing partial hepatectomy of fatty livers were evaluated. Second, Caspase 6-knockout (Caspase 6KO) mice were employed to investigate the specific role of Caspase 6-mediated innate immune signaling in IR-induced inflammatory responses in fatty livers. Finally, the exact mechanisms by which Caspase 6 regulated inflammasome activation and pyroptosis in macrophages were explored using in vivo and in vitro models.
## Caspase 6 expression is positively associated with hepatic IR injury in fatty livers
To verify the severity of the effect of IR on fatty liver, liver structural and functional damage indexes were detected in both liver biopsies and serum samples from patients who underwent partial hepatectomy with or without NASH. Compared with healthy livers, IR-stressed fatty livers showed a significant increase in serum ALT in patients with NASH (Fig. 1A), accompanied by greater histopathological damage, proinflammatory mediator release, and macrophage activation (Fig. 1B). Next, the upregulated Caspase 6 expression was observed in patients‘ fatty liver samples compared with samples without fatty livers (Fig. 1B). Similarly, Caspase 6 mRNA expression was also enhanced in the fatty livers subjected to IR insult (Fig. 1C). Double-immunofluorescence staining showed that increased Caspase 6 was mainly concentrated in liver macrophages (Kupffer cells) (Fig. 1D), which was further confirmed by detecting Caspase 6 protein levels in isolated liver macrophages (Kupffer cells) using western blotting assay (Fig. 1E). These data suggest that Caspase 6 plays a critical role in IR injury of fatty livers. Fig. 1Caspase 6 expression is positively associated with hepatic IR injury in fatty livers. Liver samples were harvested from patients during hepatectomy (after hepatic portal vein occlusion). A sALT was detected at the first day postoperatively after hepatectomy, $$n = 7$$/group; (B) H&E staining, IL-1β IHC staining, CD11b IF staining, Caspase 6 IHC staining in human liver biopsies, scale bar: 200 µm, 100 µm, 50 µm; (C) qRT-PCR analysis of Caspase 6 in IR-stressed livers with HFD or ND diets; (D) IF analysis of AlexaFluor488-labeled Caspase 6 and Cy5-labeled CD68 positive macrophages in ischemic livers, Scale bars, 40 µm; (E) WB-assisted Caspase 6 expression profile in Kupffer cells isolated from mice ischemic livers. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Caspase 6 deletion alleviates liver damage and inflammatory response during fatty liver IR injury
On the basis of the expression of Caspase 6 being upregulated in IR-stressed fatty livers, we hypothesized a functional association of Caspase 6 during fatty liver IR injury. Conventional Caspase 6KO mice were generated using the CRISPR/Cas9 system. As expected, the expression of Caspase 6 was not detectable in Caspase 6KO mice compared with WT mice (Fig. 2A). After 60 min of ischemia and 6 h of reperfusion, Caspase 6 deletion significantly reduced the elevated levels of serum ALT after fatty liver IR insult compared with the levels in WT mice (Fig. 2B). Assessment of liver morphology after IR surgery revealed that Caspase 6KO fatty livers exhibited reduced edema, sinusoidal congestion, vacuolization and local necrosis (Fig. 2C, D). What’s more, CD11b + macrophage accumulation (Fig. 2E) and neutrophil activation (Fig. 2F) were also relatively suppressed in IR-induced Caspase 6KO fatty liver. The mRNA expression of proinflammatory factors IL-1β, TNF-α, and CXCL-2 (Fig. 2G), as well as serum IL-1β concentrations (Fig. 2H) were decreased in ischemic fatty livers from Caspase 6KO mice. Western blot analysis also showed that NEK7, NLRP3 and C-Caspase 1 protein expressions were downregulated in IR-induced Caspase 6KO fatty liver (Fig. 2I). Given that Caspase 6 deficiency ameliorated fatty liver damage subjected to IR surgery, we tested whether the liver protection of Caspase 6 deletion come from alleviating IR stress or liver steatosis in advance. Caspase 6 siRNA was injected in NASH mice 24 h before establishing liver IR model. As expected, serum ALT levels, histopathological damage, and release of liver proinflammatory factors were simultaneously inhibited after Caspase 6 siRNA injection 24 h before establishing the fatty liver IR model (Supplementary Fig. 1). Together, these results demonstrated that Caspase 6 deletion ameliorated liver damage and inflammatory induced by fatty liver IR treatment. Fig. 2Caspase 6 deletion alleviates liver damage and inflammatory response during fatty liver IR injury. WT and Caspase 6KO mice were used to establish fatty liver IR (HIR) models. Samples were harvested after 90 min ischemia and 6 h of reperfusion. A Liver Caspase 6 expression was evaluated by Western blot assay. B sALT level was detected in ischemic livers; (C) Representative images of livers after IR surgery, scale bar: 1 cm; (D) Representative H&E staining in ischemic liver tissue. scale bars: 100 μm, 40 μm; (E) IF staining and quantification of CD11b + macrophages in ischemia livers, scale bars: 40 μm, 20 μm; (F) IHC staining and quantification of Ly6G+ neutrophiles in ischemia livers, scale bars: 40 μm, 20 μm; (G) Detection of cytokines IL-1β, TNF-α and CXCL-2 by qRT-PCR in ischemic livers; (H) ELISA analysis of serum IL-1β levels; (G) Western blotting analysis and relative intensity of NEK7, NLRP3 and C-Caspase 1. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Caspase 6 activates NR4A1/SOX9 signaling and induces S100A9 expression in IR-stressed fatty liver
To investigate the underlying mechanism of the Caspase 6-dependent immune regulation during fatty liver IR, RNA-Sequencing with IR-challenged fatty livers from WT and Caspase 6KO mice was performed. Interestingly, we detected changes in 1054 genes, of which 440 were downregulated and 614 were upregulated (Fig. 3A). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed “Immune system”, “Signal transduction” and “Cellular community” as the top pathways regulated by Caspase 6 (Fig. 3B). Among the top 80 differentially expressed genes in terms of fold change (Fig. 3C), changes of SOX9, NR4A1, and S100A9 aroused our attention. We found that IR triggered the upregulation of SOX9, NR4A1 and S100A9 in fatty liver (Fig. 3D), which could be suppressed by Caspase 6 deficiency (Fig. 3E) in line with the RNA-sequencing results. IF staining suggested that Caspase 6 deficiency reduced the localization of both NR4A1 and SOX9 in CD11b-positive macrophages (Fig. 3F), which was further confirmed by western blot assay of NR4A1 and SOX9 in Kupffer cells isolated in ischemic fatty livers from WT and Caspase 6KO mice (Fig. 3G).Fig. 3Caspase 6 activates NR4A1/SOX9 signaling and induces S100A9 expression in IR-stressed fatty liver. WT and Caspase 6KO mice subjected to fatty liver IR (HIR) were collected to perform RNA-sequencing analysis. A Volcano Plot displaying 615 genes to be upregulated and 440 genes downregulated. B KEGG pathway enrichment analysis of major biological pathways contributing to Caspase 6 function. C Heatmap showing the expression of Top 80 different genes. D, E Western-assisted analysis of NR4A1, SOX9 and S100A9; (F, G) IF analysis of AlexaFluor488-labeled NR4A1 or SOX9 and Cy5-labeled CD68 positive macrophages in ischemic livers, Scale bars, 40 µm, 20 µm; (H) WB-assisted NR4A1 and SOX9 expression profile in Kupffer cells isolated from mice ischemic fatty livers of WT and Caspase 6KO mice. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## NR4A1/SOX9 signaling controls proinflammatory responses in IR-stressed fatty liver
Given that Caspase 6 deficiency inhibited the NR4A1/SOX9 signaling, we next explored whether NR4A1/SOX9 signaling affected the downstream proinflammatory signaling cascade in IR-induced fatty liver. As previously reported, mannose-coupled vectors can be delivered to macrophages/Kupffer cells by expressing a mannose-specific membrane receptor. The transfection efficiency of NR4A1/SOX9 activation plasmids was verified by IF staining and western blotting analysis (Supplementary Fig. 2). Compared with Caspase 6KO + CTRL group, NR4A1-activation aggravated liver histopathology and functional damage, as evidenced by elevated ALT and greater necrotic area in ischemic fatty liver from Caspase 6KO mice (Fig. 4A, B). Moreover, mRNA expression of proinflammatory factors coding for IL-1β, TNF-α, and CXCL-2 (Fig. 4C), serum concentrations of IL-1β (Fig. 4D), the accumulation of CD11b-postive macrophages (Fig. 4E), and Ly-6G-positive neutrophils (Fig. 4F) were decreased in Caspase 6KO + NR4A1-Activation group compared with those in Caspase 6KO + CTRL group. Similarly, macrophages overexpressing SOX9 antagonized the liver protective effect of Caspase 6 deletion by promoting the inflammatory response and pathological damage (Fig. 4G–L).Fig. 4NR4A1/SOX9 signaling controls proinflammatory responses in IR-stressed fatty liver. NR4A1 or SOX9 activation (Act) plasmids or control (CTRL) vectors mixed with mannose-conjugated polymers were injected into Caspase 6KO mice 24 h before establishing HIR models. A, G Serum ALT levels in ischemic fatty livers; (B, H) H&E staining of ischemic fatty livers, Scale bar: 200 μm; (C, I) Detection of cytokines IL-1β, TNF-α and CXCL-2 by qRT-PCR in ischemic fatty livers; (D, J) ELISA analysis of serum IL-1β levels; (E, K) IF staining and quantification of CD11b+ macrophages in ischemic fatty livers, Scale bar: 40 μm; (F, L) IHC staining and quantification of Ly6G+ neutrophils in ischemic fatty livers, Scale bar: 40 μm; $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## NR4A1 interacts with SOX9 in nuclear to target S100A9 transcription in macrophages
Having demonstrated that Caspase 6 activated NR4A1/SOX9 signaling in IR-induced fatty liver inflammation, we analyzed the interaction between NR4A1 and SOX9 in macrophages. As expected, IF staining revealed that Caspase 6 deletion markedly inhibited nuclear localization of both NR4A1 (Fig. 5A) and SOX9 (Fig. 5B) in LPS-induced macrophages compared with WT group. Western blotting assay further confirmed that the nuclear expressions of NR4A1 and SOX9 were reduced in Caspase 6KO macrophages (Fig. 5C). Strikingly, co-immunoprecipitation results revealed that SOX9 bound endogenous NR4A1 in LPS-challenged BMMs (Fig. 5D). IF staining confirmed that NR4A1 could colocalize with SOX9 in the nuclear (Fig. 5E). To decipher the potential mechanism by which Caspase 6 regulates NR4A1/SOX9-mediated inflammatory responses, we performed ChIP with LPS-stimulated BMMs. The data showed that NR4A1 localized to the S100A9 promoter region (Fig. 5F), suggesting that the transcription of S100A9 is directly regulated by NR4A1. In line with the ChIP results, both RNA-ISH (Fig. 5G) and qRT-PCR assay (Fig. 5H) proved that Caspase 6 deficiency decreased the mRNA expression of S100A9 in LPS-stimulated macrophages. In conclusion, these results confirm that NR4A1 targeting the transcription of S100A9 through interaction with SOX9 is critical for Caspase 6-controlled immune regulation in macrophages. Fig. 5NR4A1 interacts with SOX9 in nuclear to target S100A9 transcription in macrophages. Bone marrow-derived macrophages (BMMs) derived from WT or Caspase 6KO mice were treated with LPS for 6 h. IF staining of NR4A1 (A) and SOX9 (B) in LPS-stimulated macrophages. DAPI was used to visualize nuclei (blue). Scale bars: 20 μm. C Western-assisted analysis of NR4A1 and SOX9 in nuclear extracts; (D) *Immunoprecipitation analysis* of NR4A1 and SOX9 in LPS-stimulated macrophages from WT and Caspase 6KO mice. E IF staining for macrophage NR4A1 (red) and SOX9 (green) co-localization in the nuclear after LPS stimulation, scale bar: 10 μm; (F) ChIP assay with macrophages using anti-NR4A1 antibody in LPS-treated BMMs; (G) RNA-ISH detecting S100A9 mRNA in macrophages by using specific probe; (H) Detection of S100A9 mRNA by qRT-PCR in macrophages. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## SOX9 is necessary for NR4A1 to mediate S100A9 transcription in macrophages during Caspase 6-mediated inflammation
To clarify the role of SOX9 in regulating S100A9 transcription, BMMs were isolated from Caspase 6KO mice and transfected with CRISPR-mediated SOX9 activation plasmids. RNA-ISH assays confirmed that Caspase 6 deficiency inhibited S100A9 mRNA, whereas CRISPR-mediated SOX9 activation reduced S100A9 transcription in Caspase 6KO macrophages (Fig. 6A). Western blot results further confirmed that SOX9 activation increased S100A9 protein expression (Fig. 6B). Moreover, compared with CRISPR-Ctrl group, the CRISPR-mediated SOX9 activation plasmids increased expression of NEK7, NLRP3, and C-caspase 1 (Fig. 6C). IF staining was further confirmed the enhanced co-localization of NEK7 and NLRP3 in SOX9-activated macrophages induced by LPS (Fig. 6D). mRNA levels of proinflammatory factors IL-1β, TNF-α, and CXCL-2 (Fig. 6E), and secretion of IL-1β were also relatively increased (Fig. 6F), suggesting that SOX9 is a key mediator for NRA1 to induce S100A9 activation and inflammation in macrophages. Fig. 6SOX9 is necessary for NR4A1 to mediate S100A9 transcription in macrophages during Caspase 6-mediated inflammation. BMMs were isolated from Caspase 6KO and WT mice and transfected with the p-CRISPR-SOX9 activation or control vector followed by LPS stimulation. A RNA-ISH detecting S100A9 mRNA in macrophages by using specific probe; (B) Western blotting and relative intensity of SOX9 and S100A9; (C) Western blotting and relative intensity of NEK7, NLRP3 and C-caspase 1; (D) IF staining for NEK7 and NLRP3 expression in macrophages. DAPI was used to visualize nuclei. Scale bars: 40 μm, 10 μm; (E) Detection of cytokines IL-1β, TNF-α and CXCL-2 by qRT-PCR in macrophages; (F) ELISA analysis of IL-1β levels in culture medium. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## S100A9 is essential for NLRP3 activation and pyroptosis in Caspase 6-mediated inflammation induced by IR in fatty liver
Having determined that NR4A1/SOX9 axis targeted S100A9 transcription in macrophages during Caspase 6-mediated immune regulation, we next examined whether S100A9 could affect NLRP3 inflammasome activation and macrophage pyroptosis. BMMs isolated from WT and Caspase 6KO mice were subsequently transfected with CRISPR-mediated S100A9 activation vector or S100A9-KO vector. CRISPR-mediated activation of S100A9 promoted expression of NEK7, NLRP3, and C-caspase 1 in LPS-stimulated macrophages extracted from Caspase 6KO mice (Fig. 7A). IF staining further revealed that S100A9 activation aggravated NLRP3 expression (Fig. 7B) and IL-1β release in macrophages (Fig. 7C). In contrast, the CRISPR-S100A9-KO vector inhibited NEK7/NLRP3 function, C-caspase 1 expression (Fig. 7D, E), and IL-1β release from macrophages (Fig. 7F). Collectively, these in vitro results strongly suggest that Caspase 6-mediated NEK7/NLRP3 inflammasome activation and pyroptosis are dependent on S100A9 function. Fig. 7S100A9 is essential for NLRP3 activation and pyroptosis in Caspase 6-mediated inflammation induced by IR in fatty liver. BMMs from WT or Caspase 6KO mice were transfected with the CRISPR-S100A9 Act, CRISPR-S100A9 KO or control vector followed by LPS stimulation; (A, D) Western blotting and relative intensity of S100A9, NEK7, NLRP3 and C-caspase 1 in LPS-stimulated BMMs; (B, E) IF staining for NLRP3 expression in macrophages. DAPI was used to visualize nuclei. Scale bar: 40 μm; (C, F) ELISA analysis of IL-1β levels in culture medium. $$n = 4$$–7/group, all data represent the mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Discussion
To the best of our knowledge, this study reveals that macrophage Caspase 6 can activate NR4A1/SOX9 signaling to regulate S100A9 transcription in response to IR-stimulated fatty liver inflammation. Importantly, we found that Caspase 6 promoted upregulation of SOX9 and NR4A1 expression in the nuclear, which subsequently induced the interaction between NR4A1 and SOX9 to mediate the transcription of the downstream target gene S100A9, ultimately leading to NEK7/NLRP3 inflammasome activation and pyroptosis in macrophages in fatty liver following IR. Our results highlight the importance of macrophage NR4A1-SOX9-S100A9 axis as a critical regulator in Caspase 6 regulation of innate immune responses in IR-induced fatty liver injury.
Fatty liver, a common type of marginal donor liver, is more sensitive to IR injury than healthy liver. It is an emerging risk factor of delayed or even loss of graft function after liver transplantation [18]. However, intracellular and molecular mechanisms of IR injury in fatty liver are poorly understood. Macrophages are the first defenders against harmful stimuli in the body [19]. However, macrophage overactivation may trigger innate immune activation by releasing a variety of danger-associated molecular patterns including HMGB1, which leads to strong inflammation response and further hepatocytes apoptosis/necrosis [20]. Single-cell RNA-sequencing analysis has proved that transplanted fatty donor liver exhibited proinflammatory macrophages accumulation compared with non-fatty donor liver [21]. In consistent with previous studies, IR stress aggravated fatty liver damage in both human and mice liver samples, as evidenced by the upregulation of serum ALT levels and increased histopathological injury. What’s more, the activation of macrophages and release of the proinflammatory factors were significantly increased.
The Caspase family can be divided into two categories: regulators of apoptosis and inducers of inflammatory cell death, which plays critical roles in regulation of programmed cell death and inflammatory actions [4]. Although being regarded as an apoptosis-related protein, accumulating evidence has suggested that Caspase 6 is a key regulator of innate immunity, inflammasome activation, and host defenses [5]. In the current study, endogenous Caspase 6 was significantly increased in IR-stressed fatty liver of both human and mice samples. Furthermore, IR-induced Caspase 6 expression was mainly accumulated in macrophages but not hepatocytes, suggesting a key role of macrophage-derived Caspase 6 in inflammatory response undergoing fatty liver IR. To clarify the functional role of Caspase 6, Caspase 6KO mice and in vivo macrophage targeted Caspase 6-siRNA injection were introduced to assess the degree of liver damage to ischemic fatty liver. As expected, Caspase 6 deletion significantly ameliorated structural and functional damage after fatty liver IR surgery, as evidence by downregulation of serum ALT levels, histopathological damage, macrophage/ neutrophil infiltration, and the expression of proinflammatory cytokines. Moreover, NEK7/NLRP3 inflammasome activation and C-caspase 1 expression were significantly inhibited in Caspase 6-deficient fatty liver induced by IR stress. Together, our findings demonstrate that deletion of macrophage-derived Caspase 6 attenuates innate immune activation and NLRP3 inflammasome activation during fatty liver IR.
It has been well documented that Caspase 6-mediated innate immune regulation is associated with a variety of signal transduction pathways [22]. Our RNA-sequencing data showed that Caspase 6 deficiency induced the changes of 1054 genes in ischemic fatty liver, which were involved in Lipid metabolism, Signal transduction, and the Immune system pathway. Among the top 80 differentially expressed genes, the changes of NR4A1 and SOX9 attracted our attention. NR4A1, belonging to NR4A family members, is widely expressed in organs or tissues. Previous study showed that activation of NR4A1 was an endogenous modulator of transforming growth factor β1 signaling [23]. During the progression of fatty liver, NR4A1 could disrupt ATP production by aggravating mitochondrial fission and Reactive oxygen species (ROS) liberation [24]. However, depletion of NR4A1 in B cells markedly promoted cholesterol and triglyceride levels and contributed to atherosclerosis in mice [25], indicating that the role of NR4A1 is “bidirectional”. Consistent with our RNA-sequencing results, western blotting analysis showed that IR activated the NR4A1 expression in fatty liver, which was reversed by Caspase 6 knockdown. What’s more, both IF staining and western blotting assay confirmed that Caspase 6 deficiency could inhibit NR4A1 expression in liver macrophages. Furthermore, in vivo transfection of NR4A1-activation plasmid into macrophages showed that NR4A1 overexpression antagonized the liver protective effects of Caspase 6 deficiency. Thus, our findings demonstrate that Caspase 6 activates macrophage NR4A1 expression to subsequently inhibit inflammatory responses in IR-induced fatty liver.
We also found that IR stress could increase SOX9 expression in ischemic fatty liver. Both RNA-sequencing and western blot analysis confirmed that Caspase 6 deletion mediated the downregulation of SOX9 in fatty liver following by IR. Similar to NR4A1, SOX9 is also a transcription factor that plays vital roles in sexual differentiation, liver fibrosis and tissue repair [26, 27]. Previous studies indicated that deletion of a partial enhancer of SOX9 could lead to complete XY male-to-female sex reversal [28]. In mice model of acute myocardial infarction, specific inhibition of SOX9 in fibroblasts weaken myocardial inflammation and fibrosis by reducing leukocyte infiltration [29]. Similarly, our data reveal that macrophage SOX9 knockdown inhibited hepatic structural and functional damage and also the production of proinflammatory mediators in IR-challenged fatty liver. Collectively, these results suggest that SOX9 is also a crucial signaling molecule for Caspase 6-mediated regulation of IR-induced inflammatory response in fatty liver.
Having clarifying the critical functions of both NR4A1 and SOX9 in macrophages during regulation of inflammatory action induced by fatty liver IR, we next examined what molecular mechanism confers the ability of NR4A1/SOX9 to regulate Caspase 6-controlled innate immune activation? He et al. identified the reciprocal feedback regulation between YAP and NR4A1 in the process of liver regeneration [30]. In both mouse and human liver tumors, YAP-SOX9 signaling determined hepatocyte plasticity and lineage-specific hepatocarcinogenesis [31]. Therefore, it can be deduced that the cross-communication occurs between NR4A1 and SOX9. Our in vitro data demonstrated that Caspase 6 depletion inhibited the translocation of NR4A1 and SOX9 into nuclear in macrophages. Further evidence confirmed the co-localization of NR4A1 and SOX9 in response to LPS stimulation. Notably, NR4A1 interacted with SOX9 through direct binding. Furthermore, ChIP data showed that NR4A1 could promote transcription by binding to the promoter region of S100A9, suggesting it as the downstream target gene of NR4A1 regulated by Caspase 6. Furthermore, disrupting SOX9 signaling decreased S100A9 expression, inactivated the NEK7/NLRP3 inflammasome, and inhibited pyoptosis in macrophages, indicating that SOX9 acts as a transcriptional coactivator of NR4A1 in Caspase 6-mediated immune regulation.
Another surprising finding is that S100A9 is key to controlling NLRP3 function and pyroptosis. S100A9, also known as myeloid-related protein 14 (MRP14), is a calcium binding protein belonging to the S100 family. S100A9 has been implicated in the regulation of a diverse range of cellular processes including inflammatory actions and cell death [32]. Recent studies have proved that S100A9-triggered cardiac inflammatory injury by inducing macrophages M1 polarization [33]. In addition, airway inflammation induced by the S100A9/nuclear factor-κB signaling cascade was the key mechanism responsible for smoking-related chronic obstructive pulmonary disease [34]. In line with previous reports, knockdown of macrophage S100A9 inhibited NEK7/NLRP3 inflammasome activation and C-caspase 1 expression, which was accompanied by decreased IL-1β release. Similarly, S100A9 activation increased NEK7, NLRP3, and C-caspase 1 levels in Caspase 6-deficient macrophages stimulated by LPS. Taken together, these results reveal the key role of macrophage S100A9 in regulating Caspase 6-mediated hepatic inflammatory injury.
Overall, we identify a novel role of Caspase 6 in regulation of NEK7/NLRP3 function and pyroptosis in fatty livers subjected to IR surgery. We confirm that Caspase 6 controls NR4A1-SOX9 interaction to drive liver inflammatory response. Specifically, SOX9 acts as a coactivator of NR4A1 to target the downstream gene S100A9, a key regulator of NEK7/NLRP3 inflammasome activation and pyroptosis (Fig. 8). Our findings provide potential therapeutic targets for the prevention of fatty liver IR injury. Fig. 8The schematic figure depicts putative molecular mechanisms in Caspase 6-mediated inflammation induced by IR in fatty liver. Caspase 6 controls NR4A1-SOX9 interaction to drive liver inflammatory response. Specifically, SOX9 acts as a coactivator of NR4A1 to target the downstream gene S100A9, a key regulator of NEK7/NLRP3 inflammasome activation and pyroptosis.
## Patients and clinical samples
This study was approved by the Academic Committee of Tianjin Medical University General Hospital (Tianjin, China). Human samples were obtained from 14 patients with benign liver tumors complicated with or without fatty livers undergoing partial hepatectomy (January 2021 to June 2022, Department of General Surgery, Tianjin Medical University General Hospital, Supplementary Table 1). Liver samples were obtained during reperfusion after liver resection (15–30 min of ischemia, followed by 15–20 min of reperfusion before abdominal closure). Liver injury was assessed by measuring serum levels of alanine aminotransferase (ALT) on the first postoperative day. Informed consent was obtained from all participants.
## Fatty liver IR model and treatments
Caspase 6 was knocked out in C57 mice by CRISPR/Cas9 technology (Cyagen Bioscience, Jiangsu, China). All animal experimental protocols were approved by the Animal Ethics Committee of Tianjin First Central Hospital. Liver steatosis was induced in 4-week-old male wild-type (WT) mice or Caspase 6KO mice fed a high-fat diet (HFD) (Supplementary Table 2 for details) or normal diet (ND) for 12 weeks. Subsequently, mouse liver IR model was established according to our previous study [35]. Briefly, the artery/portal vessels were clipped for 90 min and reperfusion was initiated by releasing the clamp. Liver tissue and serum were harvested 6 h after reperfusion. The same procedure was performed in the Sham groups without blocking the blood vessels. Alexa Fluor™ 488-labeled control vectors, Caspase 6-knockdown siRNA, and NR4A1 or SOX9 activation plasmids (2 mg/kg; Santa Cruz Biotechnology) were mixed with mannose-conjugated polymers (Polyplus-transfection, France) at a ratio according to the manufacturer’s instructions. Then they were injected through tail vein of mice 24 h before IR surgeries as described in our previous report [36]. Mice were then randomly divided into groups without blinding in different settings.
## ALT detection
Serum ALT levels were detected using an ALT kit (Sigma Aldrich) according to the manufacturer’s instructions.
## Enzyme-linked immunosorbent assay (ELISA)
Interleukin 1β (IL-1β) levels in serum or culture medium were evaluated according to commercially available ELISA kits (PI301, Beyotime).
## Histopathology
Livers were fixed with $4\%$ formalin for 24 h and subsequently embedded in paraffin and cut into 5-μm-thick sections. For liver histopathology, samples were stained with hematoxylin and eosin (H&E). For immunohistochemical (IHC) staining, samples were dehydrated, exposed to antigen, and then incubated with IL-1β (ab283818, Abcam, 1:100 dilution), Caspase 6 (ab185645, Abcam, 1:100 dilution), and Ly-6G (ab261916, Abcam, 1:100 dilution) antibodies respectively at 4 °C overnight. For immune-fluorescence (IF) staining, tissue sections or cultured cells were fixed with $4\%$ formalin for 30 min and then incubated at 4 °C overnight with antibodies against CD11b (ab184308, Abcam, 1:100 dilution), CD68 (#26042, Cell signaling Technology, 1:100 dilution), Caspase 6 (ab185645, Abcam, 1:100 dilution), NR4A1 (ab153914, Abcam, 1:100 dilution); SOX9 (ab185966, Abcam, 1:100 dilution), NEK7 (sc-393539, Santa Cruz Biotechnology, 1:100 dilution), and NLRP3 (ab270449, Abcam, 1:100 dilution). Then samples were incubated with the secondary antibody conjugated to Alexa Fluor 488 (Jackson Immunoresearch) or Alexa Fluor Cy5 (Jackson Immunoresearch) for 2 h at room temperature in the dark. Immunofluorescence images were captured using a fluorescence microscope (Keyence BZ-X810, Osaka, Japan).
## Quantitative real-time PCR
Total RNA was extracted with Trizol reagent (15596026, Invitrogen) and detected by real-time PCR. RNA was reverse transcribed into cDNA using PrimeScript RT kit (A15300, Invitrogen). qPCR experiments were performed using a SYBR Green PCR kit (4367659, Applied biosystems). β-actin was used as an internal control. Primer sequences of target genes including Caspase 6, IL-1β, TNF-α, CXCL-2, S100A9, and β-actin are listed in Supplementary Table 3.
## Western blot analysis
Tissues or cells were equilibrated in immunoprecipitation assay buffer at 4 °C for 30 min. The supernatant was collected and centrifuged at 12,000 × g for 20 min. After separating proteins by polyacrylamide gel electrophoresis, they were transferred to polyvinylidene fluoride (PVDF) membranes and incubated with antibodies against Caspase 6 (ab185645, Abcam, 1:1000 dilution), NEK7 (sc-393539, Santa Cruz Biotechnology, 1:1000 dilution), NLRP3 (ab270449, Abcam, 1:1000 dilution), cleaved Caspase 1 (C-caspase 1) (#89332, Cell signaling Technology, 1:1000 dilution), NR4A1 (ab153914, Abcam, 1:1000 dilution), SOX9 (ab185966, Abcam, 1:1000 dilution), S100A9 (ab242945, Abcam, 1:1000 dilution), and β-actin (#4970, Cell signaling Technology, 1:2000 dilution). β-actin was used as an internal reference. The nuclear and cytosolic fractions were prepared with NE-PER Nuclear and Cytoplasmic Extraction Reagents (ThermoFisher Scientific). Lamin B2 (#12255, Cell signaling Technology, 1:1000 dilution) was used as an internal reference of nuclear protein. IBright FL1000 (Invitrogen, Carlsbad, CA, USA) was used to analyze the expression of target proteins. Full and uncropped western blots have been shown in Supplementary Material [2].
## Isolation of primary Kupffer cells
Primary Kupffer cells were isolated from mice according to our previous methods [35]. Briefly, mouse livers were digested with preheated EGTA and 0.75 g/L collagenase type I solution at 37 °C. Liver non-parenchymal cells were obtained by centrifugation at 50 × g for 5 min and resuspended in Hank’s balance solution. Kupffer cells were isolated using a $50\%$/$25\%$ two-step Percoll gradient solution (1500 × g at 4 °C for 15 min). Kupffer cells located in the middle of centrifuge tubes were collected and resuspended in Dulbecco’s Modified Eagle Medium (DMEM) and $10\%$ fetal bovine serum (FBS). Non-adherent cells were removed by culture medium exchange.
## Bone marrow-derived macrophages (BMMs) isolation and transfection in vitro
BMMs extracted from the femur and tibia of mice were filtered through a 200-μm nylon cell filter. After centrifugation at 300 × g for 10 min, cell (1×106/well) were resuspended in $15\%$ L929-conditioned medium and $10\%$ FBS. After 7d of culture, cells were transfected with CRISPR-SOX9 activation, CRISPR-S100A9 activation, CRISPR/Cas9-S100A9 KO, or control vector (Santa Cruz Biotechnology). After 48 h, transfected cells were exposed to lipopolysaccharide (LPS) for 6 h.
## RNA-sequencing assay
Liver tissues harvested from fatty liver IR model of WT mice or Caspase 6KO mice were subjected to RNA-sequencing analysis ($$n = 3$$/group). Total RNA was extracted with Trizol reagent separately. The cDNA libraries were constructed using the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) according to the manufacturer’ instruction. Pathway analysis was used to figure out the significant pathways of the differential genes according to KEGG database.
## Protein immunoprecipitation assay
After lysing cells with NP-40 lysis buffer (50 mM Tris pH7.4, 10 mM EDTA, 150 mM NaCl, $1\%$ NP-40), total cell isolates were incubated with anti-SOX9 (sc-166505, Santa Cruz Biotechnology) or anti-NR4A1 (sc-365113, Santa Cruz Biotechnology) antibodies overnight at 4 °C, followed by protein-G/A Beads for 4 h at 4 °C. The protein was separated by heating at 95 °C for 5 min. Supernatants were collected and analyzed by standard immunoblot analysis.
## Chromatin immunoprecipitation (ChIP) assay
ChIP experiments were performed with a ChIP kit (ab185913, Abcam). Briefly, BMMs were treated with $1\%$ formalin for 15 min to crosslink protein and chromatin. The reaction was then terminated by adding 0.125 M glycine for 5 min. Cells were washed and resuspended in ChIP lysis buffer for 15 min. Cell lysates were centrifuged and resuspended in nuclear lysis buffer. After 15 min of ultrasonic oscillation, chromatin was analyzed on $2\%$ agarose gel for DNA fragment length analysis. Chromatin was subsequently immunoprecipitated overnight with anti-NR4A1 antibody (12235-1-AP, Proteintech), while a normal IgG antibody was used as a negative control. Antibody/chromatin samples were mixed with Protein A Sepharose beads. After repeated cleaning and purification, the obtained DNA products were tested by PCR. Primer sequences for the NR4A1-reactive S100A9 promoter region are listed in Supplementary Table 3.
## RNA in situ hybridization (RNA-ISH)
RNA ISH was detected using an RNAscope System according to the manufacturer’s protocol (Advanced Cell Diagnostics, Newark, CA, USA). Mouse S100A9 probes, as well as negative and positive control probes, were purchased from Advanced Cell Diagnostics. *In* general, BMMs on glass slides were fixed with $10\%$ neutral buffered formalin and subsequently dehydrated with $50\%$, $70\%$, and $100\%$ ethanol for 5 min. Slides were then dried at room temperature for 5 min and treated with hydrogen peroxide for 10 min, followed by protease IV for 30 min. Next, slides were incubated with probes in a HybEZ oven at 40 °C for 2 h. Conditions included treatment at 40 °C for 30 min (AMP1, AMP3, and AMP5) or 20 min (AMP2, AMP4, and AMP6). Finally, the RNA signal was detected by incubation with Fast Red for 10 min. Eosin was used to count cells, and slides were left to dry at 60 °C for 15 min. Finally, RNA-ISH staining images were captured using a light microscope.
## Statistical analysis
SPSS22.0 software was used for statistical analysis. All data are expressed as mean ± standard deviation (SD). Comparisons between groups were performed using ANOVA or t test. A two-sided $P \leq 0.05$ was considered statistically significant. Data are shown as representative of at least four independent experiments.
## Supplementary information
Reply to email confirming the agreement of the addition to author list Supplementary material [1] Original Data File The online version contains supplementary material available at 10.1038/s41420-023-01396-z.
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|
---
title: 'Exercise in adults admitted to hospital with diabetes-related foot ulcers:
a pilot study of feasibility and safety'
authors:
- Emily Aitken
- Jonathan Hiew
- Emma J Hamilton
- Laurens Manning
- Jens Carsten Ritter
- Edward Raby
- Paul M Gittings
journal: Journal of Foot and Ankle Research
year: 2023
pmcid: PMC10043540
doi: 10.1186/s13047-023-00616-0
license: CC BY 4.0
---
# Exercise in adults admitted to hospital with diabetes-related foot ulcers: a pilot study of feasibility and safety
## Abstract
### Background
Diabetes-related foot ulcers result in significant mortality, morbidity and economic costs. Pressure offloading is important for ulcer healing, but patients with diabetes-related foot ulcers are presented with a dilemma, because whilst they are often advised to minimise standing and walking, there are also clear guidelines which encourage regular, sustained exercise for patients with diabetes. To overcome these apparently conflicting recommendations, we explored the feasibility, acceptability and safety of a tailored exercise program for adults admitted to hospital with diabetes-related foot ulcers.
### Methods
Patients with diabetes-related foot ulcers were recruited from an inpatient hospital setting. Baseline demographics and ulcer characteristics were collected, and participants undertook a supervised exercise training session comprising aerobic and resistance exercises followed by prescription of a home exercise programme. Exercises were tailored to ulcer location, which complied with podiatric recommendations for pressure offloading. Feasibility and safety were assessed via recruitment rate, retention rate, adherence to inpatient and outpatient follow up, adherence to home exercise completion, and recording of adverse events.
### Results
Twenty participants were recruited to the study. The retention rate ($95\%$), adherence to inpatient and outpatient follow up ($75\%$) and adherence to home exercise ($50.0\%$) were all acceptable. No adverse events occurred.
### Conclusions
Targeted exercise appears safe to be undertaken by patients with diabetes-related foot ulcers during and after an acute hospital admission. Recruitment in this cohort may prove challenging, but adherence, retention and satisfaction with participation in exercise were high.
### Trial registration
The trial is registered in the Australian New Zealand Clinical Trials Registry (ACTRN12622001370796).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13047-023-00616-0.
## Introduction
Pressure offloading is a critical component of a multidisciplinary management plan to achieve diabetes related foot ulcer (DFU) healing [1]. The mechanism for this is through redistribution or elimination of the forces associated with ambulation and weight bearing which can be achieved by the usage of specific offloading devices. In addition to offloading devices, patients are often advised to minimise physical activity and exercise [2, 3]. However, physical activity and exercise are important aspects of diabetes management via several physiological benefits not limited to improved glycaemic control and stability, increased lean muscle mass, improved muscle strength, reduced mortality and improved quality of life [4–9].
As such, people living with diabetes-related foot ulcers (DFU) are often faced with a dilemma on what role physical activity and exercise has in the overall management plan for diabetes in the context of a DFU. In addition to the known benefits of physical activity and exercise, lower levels of physical activity in patients with diabetes has been shown to be associated with foot ulceration [10, 11] supporting the importance of participation in physical activity and exercise in this patient group.
Exercise programmes undertaken by people with diabetes-related peripheral neuropathy, with and without existing foot ulcers, do not increase the rate of ulceration [2, 11–14]. Instead, the physiological benefits of exercise may include improvements in neuropathic symptoms, nerve fibre branching and glycated haemoglobin (HbA1c) levels [2, 12–14]. One small study suggested that non-weight bearing exercise is safe and feasible for people with DFU in an ambulatory care setting [15]. It did not however explore the role of targeted exercise beyond non-weight bearing exercises. Recent systematic reviews have concluded that that there is insufficient high quality evidence for a conclusive outcome about the impact of exercise on patients with DFU, or DFU healing [16, 17]. Recommendations are for further research in this area.
This knowledge gap has been identified by The International Working Group on the Diabetic Foot (IWGDF) as an important area for future research and was amongst the top ten research priorities for Australian stakeholders which included consumers and health professionals [18]. It is known that every two hours, an Australian loses a limb to a diabetes-related foot ulcer (DFU) [19] with significant morbidity and economic costs. As such, investigation into interventions that may assist to reduce complications of DFU is an important endeavour. To assist in clarifying apparently conflicting physical activity and exercise recommendations in this vulnerable group, we aimed to determine whether undertaking exercise in people admitted to hospital with an acute DFU is feasible, acceptable and safe. Secondary aims included physical activity levels, perceived benefits and barriers to physical activity, muscle strength, exercise load and satisfaction with the exercise programme.
## Study Design
This non-randomised pilot study aimed to determine safety and feasibility of targeted exercise commencing during an acute inpatient setting. Ethics approval was granted by the South Metropolitan Health Service Human Research Ethics Committee (RGS 4173). Study data were collected and managed using Research Electronic Data Capture (REDCap) [20, 21].
## Participants
Patients admitted to hospital under the care of the Multidisciplinary DFU team between October 2020 to April 2021 were screened for eligibility. Participants were eligible if they were over the age of 18, had a diagnosis of diabetes mellitus and requiring admission to hospital for DFU of any type or severity. People were excluded from participation if they were unable to consent due to language or cognitive impairment, had an acute myocardial infarction, unstable angina, severe heart failure (New York Heart Association Functional Classification IV), cardiac arrhythmias causing haemodynamic compromise, musculoskeletal or neurological conditions precluding exercise or it was otherwise determined not to be in their best interest to participate. Written consent was obtained for all participants. The resources available for this study limited patient screening and recruitment to one day per week.
## Study procedures
Demographic information was collected, surveys and physical assessments (described in Outcomes section below) were completed. Participants completed an individually prescribed inpatient exercise session under supervision. Following this, they were provided with an individualised daily home exercise programme to complete for two weeks post discharge (see Intervention section below). Podiatric wound offloading recommendations were observed at all times during this study.
After completion of the inpatient phase of the study, participants were reviewed at their routine outpatient follow-up clinic, scheduled for two weeks after discharge. Adherence to their home exercise programme was assessed by review of a home exercise diary. Final surveys and physical outcome measurement were also completed. Participants were also offered an outpatient exercise session at this review. Participants who could not attend in-person after discharge were followed up via telephone and surveys were emailed to participants for completion using REDCap. Upon exit from the study, participants were provided a short survey asking about acceptability and satisfaction of the exercise intervention. Adverse events, participant comments and any reasons for withdrawal were collected throughout the study.
## Intervention
Exercise was commenced during hospital admission. Where a participant required surgery for their DFU, clearance from the treating team was obtained for the patient to commence exercise. The treating team made weight bearing orders in relation to the DFU location for each participant to follow. Participants who were able to weight bear but in offloading footwear were prescribed this by the Podiatrist. These included removable ankle high and knee high devices. Otherwise, participants who were ordered to be non-weight bearing were able to participate in exercise whilst adhering to this restriction. Blood glucose levels (BGL) were assessed prior to and during exercise sessions. Exercise was not commenced, or was stopped, where BGL < 5 mmol/L.
Supervised exercise sessions were comprised of both aerobic and strength training components. These exercises were individualised for each participant to maintain weight bearing orders given by the treating team. For example, participants who were ordered to be heel weight bearing undertook exercises weight bearing through the heel only. Participants ordered to be toe weight bearing completed exercises weight bearing through the toe only. Where a patient was strictly non-weight bearing on one leg, exercises were performed weight bearing on the opposite leg and non-weight bearing on the affected leg. Exercise selection was completed with respect to the participant’s ability to achieve the full range of movement of the selected exercises. This was assessed at the time of exercise by the supervising Physiotherapist.
The mode of aerobic exercise was selected using a combination of patient preference and adherence to wound offloading requirements. Participants completed five to 20 min of upper limb ergometry (Monark Exercise AB, Dalarna, Sweden), single leg or two-legged cycling on an upright exercise bike (Monark Exercise AB, Dalarna, Sweden), or recumbent exercise bike (SportsArt, Washington, United States of America). The amount of time for the aerobic component was based on exercise tolerance. Participants were instructed to exercise at a moderate intensity using Borg’s Rating of Perceived Exertion Category-Ratio scale (CR-10) [22].
Strength training exercises were completed as a circuit using body weight, resistance bands (Performance Health ANZ, Sydney, Australia), free weights and ankle weights, or pin-loaded cable weight machines (Cybex International, Illinois, USA). Exercises were selected based on weight bearing orders as described above. Participants were instructed to work to a moderate intensity on the CR-10 scale. Each participant completed a total of 2–3 sets of 8–15 repetitions of each exercise. Exercises targeted key muscles groups in both lower and upper limbs in standing, sitting or unilateral positions. Examples of some of the common exercises utilised were; bicep curl, shoulder press, deltoid fly, seated or bent over row, leg press, sit to stands, squats, bridging, heel raise, side lying or standing hip abduction and seated knee extensions. Participants were asked to rate the intensity of the full exercise session using the CR-10 scale at 10 min after their session.
A home exercise programme was prescribed and consisted of a selection of pre-determined exercises similar to those utilised in the inpatient session (Additional file 1). These accounted for individual ability and wound offloading requirements. These exercises were aimed at strength training and utilised hand weights (or water bottles / other common household items) or resistance bands (Performance Health ANZ, Sydney, Australia) which were provided. Home exercises were demonstrated by a Physiotherapist who ensured these were able to be completed safely prior to discharge. A home exercise diary was provided to participants and checked at the two week follow up. Where a patient was unable to attend in person, this was reported subjectively on the phone to the researcher.
## Primary outcome
The primary outcome of this study was the feasibility and safety of undertaking exercise in the study population (Table 1) and was assessed at the end of the study. These criteria and acceptability limits were agreed upon by the study team during development of this project.
Table 1Feasibility and safety outcome measurement and acceptability levelsOutcomeDescriptionAcceptabilityRecruitmentNumber of participants enrolled in the project as a proportion of all eligible participants approached for consent$50\%$ patient recruitmentRetentionNumber of participants who remained enrolled in the project$75\%$ retentionAdherence to studyNumber of participants who completed both inpatient and outpatient phases of the project$75\%$ adherenceAdherence to home exerciseCompletion of home exercise programmeAny amount of completionAdverse EventsSafety will be assessed by examination of the reported adverse events related to exercise participationN/A Adverse events in this study were defined as BGL < 5.0mmol/L during exercise, any event related to the exercise session which required a referral for an unplanned medical review, unplanned repeat debridement of index ulcer during the enrolment period or unplanned amputation involving the index ulcer during the enrolment period.
## Current levels of physical activity
Current participation in physical activity and exercise was assessed using the International Physical Activity Questionnaire (IPAQ) - short. This is a seven-item questionnaire which can be self-administered, or telephone administered. It is a validated tool to obtain data on health-related physical activity [23]. The questionnaire quantifies how much vigorous, moderate or walking based activity on an average week and converts this to a weighted estimate of total physical activity. This estimate is then used to classify participant’s physical activity level as low, moderate or high [23]. This was assessed at initial review and two week follow up.
## Benefits and barriers to physical activity
Perceived benefits of and barriers to physical activity and exercise was assessed using the Exercise Benefits/Barriers Scale (EBBS) [24]. This is a 43 item four-choice Likert scale in which the respondent rates their agreement with perceived barriers or benefits of exercise [24, 25]. The EBBS can be scored as a Benefits scale and a Barriers scale, where a higher score indicates the responder has greater perception of benefits or barriers to exercise respectively. In addition to the EBBS, we asked participants whether they believed that having a foot ulcer was a barrier to them participating in exercise. This was as a “yes/no” question format. This was assessed at initial review and two week follow up.
## Muscle strength
Grip strength was assessed in kilograms using a Jamar handheld dynamometer (Surgical Synergies, SI Instruments, SA, Australia). Grip strength has been demonstrated to be useful as a predictor for muscle mass and physical functioning [26]. This was assessed at initial review and two week follow up.
## Exercise Intensity and load
The intensity of exercise was rated using the CR-10 scale. This is a 0–10 scale used to grade exercise intensity [27]. This scale allows individuals to rate their exertion and monitor the intensity of exercise. The overall intensity of the exercise session was assessed by asking the patients for a session RPE (sRPE) score to quantify the intensity of the exercise session [28]. The sRPE is a multiplication of the intensity score for the session as a whole by the duration of the session in minutes. This was assessed 10 min after at the end of an exercise session.
## Satisfaction
An exit survey was given to participants at the end of enrolment in this study asking about the participant’s satisfaction with the intervention. It was based on previous research and investigated participant’s perceptions of benefit and safety [29]. A five-point Likert scale was used asking participants to rate their level agreement with the following statements, from strongly disagree [1] to strongly agree [5]: I found the exercise sessions in hospital usefulI felt safe completing exercise in the hospitalI felt that the supervision with exercise in hospital was adequateI was able to do the home exercise programme easily in my homeI found the home exercise programme usefulI would recommend participation in an exercise programme like this
## Sample size
The enrolment target was 30 participants.
## Statistical methods
Descriptive statistics were used to describe the demographic data, primary and secondary outcomes. Primary outcomes were compared to pre-determined criteria displayed in Table 1. Paired data from 15 participants who completed both study phases for the EBBS and IPAQ were analysed. The EBBS data were compared using a Wilcoxon signed-ranked analysis whilst data from the IPAQ was compared using a chi-square analysis. We have chosen to display the *Likert data* in chart form to highlight the frequency of answers in these fields.
## Results
Forty-two patients were identified as suitable candidates and 20 patients provided consent and were enrolled into the study. Fifteen participants continued through the study and provided end-study data. The flow of participants through this study is presented (Fig. 1). The most common reasons for declining to participate are shown (Table 2).Fig. 1Flow of participants through the studyTable 2Reasons for declining to participateReasonnParticipant feels they do enough exercise already5Declined to give a reason5Participant not feeling well enough to exercise4Participant is for imminent discharge from hospital4Participant feels it is too much of a time commitment2Participant does not like exercise1Participant worried about the impact of exercise on DFU1 *Demographic data* of enrolled participants are shown (Table 3). The exercise intervention was conducted before planned surgical intervention of the foot ulcer in 12 ($60\%$) of the participants and after planned surgery for 5 ($25\%$) of the participants. Wound offloading footwear was prescribed for 11 ($55\%$) of the participants. Six participants ($30\%$) were ordered to be non-weight bearing on the affected limb and subsequently undertook non-weight bearing exercises for that limb.
Table 3Demographic and foot ulcer characteristics of the study populationn (%)Age [mean (SD)]61.3 (12.3) yearsBMIa [mean (SD)]29.9 (5.2) kg/cm2 Duration of diabetes [mean (SD)]21.3 (12.5) yearsHbA1cb [mean (SD)]8.82 (2.23)Female7 [35]Diabetes - Type 12 [10] - Type 218 [90]Smoking History - Never smoked8 [40] - Previous smoker8 [40] - Current smoker4 [20]Chronic Kidney Disease7 [35]Cardiovascular Disease7[35]Musculoskeletal Disease16 [80]Previous foot ulcer14 [70]Ulcer Location: - Forefoot15 [75] - Midfoot3 [15] - Hindfoot2 [10]WIfIc wound grade [30] - 18 [40] - 27 [35] - 35 [25]*Data is* presented as [number (%)] unless otherwise informed described. Key aBody mass index bHemaglobin A1c cWound, ischemia and foot infection
## Primary outcomes
Feasibility and safety data is presented in Table 4.
Table 4Primary outcome feasibility and safety outcome dataOutcomen%Recruitment20 of 4247.6Retention19 of 2095.0Adherence to study15 of 2075.0Adherence to home exercise10 of 2050.0Adverse Events00.00
## Secondary outcomes
The secondary outcomes for the initial and final assessment time points are presented in Table 5, for participants that completed both phases of the study. The data for acceptability and satisfaction of the exercise programme are presented in Fig. 2.Fig. 2Post treatment acceptability of participating in an exercise intervention ($$n = 15$$). Participant responses to questions regarding acceptability of participating in an exercise intervention, as provided in the exit survey. Values represent frequency of responses for each questionTable 5Secondary outcomesOutcomeInitial Assessment ($$n = 20$$)Final Assessment ($$n = 15$$) p-valueIPAQ [n (%)] - High2 [13]2 [13]0.999 - Moderate3 [20]3 [20] - Low10 [67]10 [67]EBBS [mean (SD)] - Benefits Scale89.1 (11.8)82.8 (10.5)0.003 - Barriers Scale30.9 (5.5)30.3 (5.7)0.705Perception that a foot ulcer prevents exercise participation [n (%)]11 [73]7 [47]Grip strength [mean (SD)]Left: 28.3 (11.0)Right: 31.8 (13.6)Exercise Intensity RPEa [mean (SD)]3.5 (1.07)Exercise Session Training Load sRPEb [mean (SD)]151.5 (67.7)Key: a Rating of perceived exertion b Session rating of perceived exertion
## Discussion
The undertaking of an exercise intervention in a patient group hospitalised with a DFU was a novel feature of this pilot study. We chose to include patients during their acute hospital admission for a DFU as an opportunity to provide enhanced multidisciplinary care during the early, active phase of treatment. The results of this study suggest that the inclusion of an exercise programme is feasible and safe for people hospitalised with a DFU.
No adverse events occurred during exercise sessions. Providing supervision during exercise sessions was believed to be key in achieving this safety outcome. Supervision allowed for monitoring of vital clinical parameters (such as BGLs, heart rate and blood pressure) as well as subjective parameters (such as level of exercise intensity), thereby minimising potential risks associated with over-exertion in this population. Furthermore, supervision enabled the authors to provide exercise specifically tailored to individual wound offloading needs. Previous research has trialled non-weight bearing exercises to achieve offloading requirements [15]. This study also utilised a range of common exercises with altered foot positions to comply with ulcer off-loading requirements, without restricting exercise options to only non-weight bearing exercises in participants who were able to weight bear to some degree. Whilst we were unable to assess plantar pressures in this study, assessment of this would confirm effectiveness of this approach.
Weight bearing is an important consideration in this patient group. Whilst it is a recognised mode of protection for wound healing, the off-loading of a limb comes with its own health risks. A recent study has demonstrated bilateral reductions in bone mineral density (BMD) of 1.4–$2.8\%$ at the femoral neck and total BMD at the hip 12 weeks after hospital admission for DFU [31]. The authors of this study concluded that this finding was likely to be related to disuse due to prolonged offloading periods, and elevated levels of serum inflammatory markers. Exercise training is an intervention that has been shown to be effective in the maintenance or improvement of BMD in a number of clinical and healthy groups [32]. Exercise training has also been demonstrated to be effective in the reduction of systemic inflammation for people with diabetes [33]. The effect of commencing exercise in the acute phase of an admission due to DFU on BMD loss could assist in ameliorating these impacts of DFU treatment. Whist the current study has provided evidence that exercise is feasible, the effectiveness of this relatively low cost and simple intervention is not known and should be considered for further investigation as it may have important clinical implications, including avoiding secondary complications of a DFU.
The potential for exercise and its role in reducing the risk of complications associated with DFU has been identified as a research priority by health providers and consumers [18]. There are however, a number of known barriers for participation in exercise for this patient group that should be considered [12]. The current study demonstrated that the presence of a DFU was viewed as a barrier to exercise participation by the majority of participants at the commencement of the study. The demonstration of a lower percentage of participants viewing their DFU as a barrier to exercise at the end of the study period suggests that demonstrating simple modification to a variety of common exercises could be an effective way to reduce the belief of a DFU as a barrier to exercise. In clinical practice, reducing the perceived threat of participation in physical activity with a DFU may be a first and important step in people with a DFU meeting recommended levels of daily physical activity, engaging in exercise and subsequently improving their health status.
The short exercise intervention trialled in the current study did not result in a change in overall physical activity levels from the IPAQ survey, nor perceived barriers to exercise as assessed by the EBBS. Conversely, we did note a reduction in the perceived benefits of exercise. As there is a no documented minimally clinically important difference for this scale that we were able to find, we cannot determine if this change is clinically relevant. Satisfaction with the exercise programme was acceptable as recorded by the satisfaction survey. There is a possibility of participant bias in these results. Best efforts to control for this included ensuring participants knew data was confidential and providing the survey electronically to optimise anonymity. small sample size bias is also likely to have impacted these results.
Behaviour modification interventions have been trialled in studies of people with diabetes but without DFU with some success. These types of interventions may have applicability in the DFU population. A literature review and practice guideline outlined that the inclusion of behaviour change interventions, with a focus on self-efficacy and motivation to exercise, could be effective when added to exercise to increase physical activity levels in people with diabetes [34]. A study by Olson et al. [ 2015] [35] incorporated group workshops for goal setting and behaviour modification strategies in combination with walking programmes. Although long term behaviour change was not demonstrated in this study, the combination of psychological and physical interventions demonstrated short term success for increasing participant’s physical activity levels. Whilst promising in concept, the applicability of such a programme in people with DFU would need further investigation.
Another interesting finding in this study was the disproportionately high numbers of participants that lived regionally or rurally and were unable to attend in-person follow up sessions. This is a unique situation for Western Australia where the population is spread over an area of more than two-million square kilometres. It presents a challenge when delivering healthcare, particularly when using supervised or group exercise as a potential treatment modality. Provision of a home exercise programme rather than utilising an in-person supervised exercise environment is one way of combatting this, however innovation and improvements in healthcare delivery, including the ongoing use of telehealth modalities to monitor performance, are necessary to improve healthcare outcomes.
## Limitations
The availability of funding and personnel limited the undertaking of recruitment and intervention to one day per week for the duration of this study. This limited our ability to recruit participants who were unwell or fasting for surgical intervention on that scheduled day of recruitment. Therefore, if a patient was unwell on that day, they may not have had another opportunity to participate prior to discharge from hospital. Similarly, if a patient was for discharge on that day, they were more likely to decline participation. As such, our recruitment rate was short of our pre-planned acceptability level.
Another challenge for ongoing follow-up of participants was the government issued travel and hospital visitation restrictions associated with the COVID-19 pandemic. Our method for mitigation of this challenge was utilising virtual or telehealth modes of communication which enabled us to achieve high levels of survey completion in the follow up period. We were unable to use telehealth to provide home exercise supervision or monitoring of compliance for this project. This was reliant on demonstration or exercises on initial review and patient report of compliance throughout.
## Future research
This pilot study for feasibility and safety of exercise will be used to inform future research design. Future randomised trials in this population should have larger sample size with longer duration intervention and include outcomes of both the foot and musculoskeletal system.
Telehealth models of care should be considered for providing supervision of home exercise programmes to increase compliance with an exercise intervention. Patients who decline participation in an exercise research trial should be invited to provide outcome data only for comparison with trial participants, as well as inclusion in any qualitative study to explore barriers to exercise and participation.
## Conclusion
Exercise appears safe to be undertaken by patients with diabetes related foot ulcers during a hospital admission. Recruitment in this acute setting proved a challenge in this study due to clinical demands in the acute setting, but adherence, retention and satisfaction with participation in exercise met our pre-determined acceptable limits.
## Supplementary Information
Additional file 1. Home Exercise Programme. This PDF file is an example of the home exercise programme provided to participants. © South Metropolitan Health Service Western Australia 2021. Copyright to this material is vested in the State of Western Australia unless otherwise indicated. Apart from any fair dealing for the purposes of private study, research, criticism or review, as permitted under the provisions of the Copyright Act 1968, no part may be reproduced or re-used for any purposes whatsoever without written permission of the State of Western Australia.
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5. Hordern MD, Dunstan DW, Prins JB, Baker MK, Singh MAF, Coombes JS. **Exercise prescription for patients with type 2 diabetes and pre-diabetes: a position statement from Exercise and Sport Science Australia**. *J Sci Med sport* (2012.0) **15** 25-31. DOI: 10.1016/j.jsams.2011.04.005
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7. Kluding PM, Bareiss SK, Hastings M, Marcus RL, Sinacore DR, Mueller MJ. **Physical training and activity in people with Diabetic Peripheral Neuropathy: paradigm shift**. *Phys Ther* (2017.0) **97** 31-43. DOI: 10.2522/ptj.20160124
8. Otterman NM, van Schie CHM, van der Schaaf M, van Bon AC, Busch-Westbroek TE, Nollet F. **An exercise programme for patients with diabetic complications: a study on feasibility and preliminary effectiveness**. *Diabet Med* (2011.0) **28** 212-7. DOI: 10.1111/j.1464-5491.2010.03128.x
9. Sigal RJ, Kenny GP, Boulé NG, Wells GA, Prud’homme D, Fortier M. **Effects of aerobic training, resistance training, or both on glycemic control in type 2 diabetes: a randomized trial**. *Ann Intern Med* (2007.0) **147** 357-69. DOI: 10.7326/0003-4819-147-6-200709180-00005
10. Armstrong D, Lavery L, Holtz-Neiderer K, Mohler M. **Variability in activity May Precede Diabetic Foot Ulceration**. *Diabetes Care* (2004.0) **27** 1980-4. DOI: 10.2337/diacare.27.8.1980
11. Lemaster WJ, Reiber EG, Smith GD, Heagerty JP, Wallace JC. **Daily Weight-Bearing Activity does not increase the risk of Diabetic Foot Ulcers**. *Med Sci Sports Exerc* (2003.0) **35** 1093-9. DOI: 10.1249/01.MSS.0000074459.41029.75
12. Crews RT, Schneider KL, Yalla SV, Reeves ND, Vileikyte L. **Physiological and psychological challenges of increasing physical activity and exercise in patients at risk of diabetic foot ulcers: a critical review**. *Diabetes Metab Res Rev* (2016.0) **32** 791-804. DOI: 10.1002/dmrr.2817
13. Kluding PM, Pasnoor M, Singh R, Jernigan S, Farmer K, Rucker J. **The effect of exercise on neuropathic symptoms, nerve function, and cutaneous innervation in people with diabetic peripheral neuropathy**. *J Diabetes Complicat* (2012.0) **26** 424-9. DOI: 10.1016/j.jdiacomp.2012.05.007
14. Mueller MJ, Tuttle LJ, Lemaster JW, Strube MJ, McGill JB, Hastings MK. **Weight-bearing versus nonweight-bearing exercise for persons with diabetes and peripheral neuropathy: a randomized controlled trial**. *Arch Phys Med Rehabil* (2013.0) **94** 829-38. DOI: 10.1016/j.apmr.2012.12.015
15. Lindberg K, Møller BS, Kirketerp-Møller K, Kristensen MT. **An exercise program for people with severe peripheral neuropathy and diabetic foot ulcers - a case series on feasibility and safety**. *Disabil Rehabil* (2020.0) **42** 183-9. DOI: 10.1080/09638288.2018.1494212
16. Aagaard TV, Moeini S, Skou ST, Madsen UR, Brorson S. **Benefits and harms of exercise therapy for patients with diabetic foot ulcers: a systematic review**. *Int J Low Extrem Wounds* (2022.0) **21** 219-33. DOI: 10.1177/1534734620954066
17. Tran MM, Haley MN. **Does exercise improve healing of diabetic foot ulcers? A systematic review**. *J Foot Ankle Res* (2021.0) **14** 1-9. DOI: 10.1186/s13047-021-00456-w
18. Perrin BM, Raspovic A, Williams CM, Twigg SM, Golledge J, Hamilton EJ. **Establishing the national top 10 priority research questions to improve diabetes-related foot health and disease: a Delphi study of australian stakeholders**. *BMJ Open Diabetes Res Care* (2021.0) **9** e002570. DOI: 10.1136/bmjdrc-2021-002570
19. 19.Australian Commission on Safety and Quality in Health Care. The Fourth Australian Atlas of Healthcare Variation. Australian Commission on Safety and Quality in Health Care; 2021.
20. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. **Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support**. *J Biomed Inform* (2009.0) **42** 377-81. DOI: 10.1016/j.jbi.2008.08.010
21. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L. **The REDCap consortium: building an international community of software platform partners**. *J Biomed Inform* (2019.0) **95** 103208. DOI: 10.1016/j.jbi.2019.103208
22. Heath EM. **Borg’s Perceived Exertion and Pain Scales**. *Med Sci Sports Exerc* (1998.0) **30** 1461
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24. Sechrist KR, Walker SN, Pender NJ. **Development and psychometric evaluation of the exercise benefits/barriers scale**. *Res Nurs Health* (1987.0) **10** 357-65. DOI: 10.1002/nur.4770100603
25. Brown SA. **Measuring Perceived benefits and perceived barriers for physical activity**. *Am J Health Behav* (2005.0) **29** 107-16. DOI: 10.5993/AJHB.29.2.2
26. Bohannon WR. **Muscle strength: clinical and prognostic value of hand-grip dynamometry**. *Curr Opin Clin Nutr Metab Care* (2015.0) **18** 465-70. DOI: 10.1097/MCO.0000000000000202
27. Borg G. *Borg’s Perceived Exertion and Pain Scales* (1998.0) 53
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29. Connolly BP, Thompson A, Douiri A, Moxham J, Hart N. **Exercise-based rehabilitation after hospital discharge for survivors of critical illness with intensive care unit–acquired weakness: a pilot feasibility trial**. *J Crit Care* (2015.0) **30** 589-98. DOI: 10.1016/j.jcrc.2015.02.002
30. 30.Mills Sr JL, Conte MS, Armstrong DG, Pomposelli FB, Schanzer A, Sidawy AN, et al. The society for vascular surgery lower extremity threatened limb classification system: risk stratification based on wound, ischemia, and foot infection (WIfI). J Vasc Surg. 2014;59(1):220–34.
31. Nejatian MM, Sobhi S, Sanchez BN, Linn K, Manning L, Soh SC. **Reduction in femoral neck and total hip bone mineral density following hospitalisation for diabetes-related foot ulceration**. *Sci Rep* (2021.0) **11** 22742. DOI: 10.1038/s41598-021-02233-y
32. Zhang S, Huang X, Zhao X, Li B, Cai Y, Liang X. **Effect of exercise on bone mineral density among patients with osteoporosis and osteopenia: a systematic review and network meta-analysis**. *J Clin Nurs* (2022.0) **31** 2100-2111. DOI: 10.1111/jocn.16101
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35. Olson EA, McAuley E. **Impact of a brief intervention on self-regulation, self-efficacy and physical activity in older adults with type 2 diabetes**. *J Behav Med* (2015.0) **38** 886-98. DOI: 10.1007/s10865-015-9660-3
|
---
title: The association between soluble CD163, disease severity, and ursodiol treatment
in patients with primary biliary cholangitis
authors:
- Lars Bossen
- Tobias Stemann Lau
- Mette Bak Nielsen
- Marlene Christina Nielsen
- Astrid Højmark Andersen
- Peter Ott
- Sabine Becker
- Henning Glerup
- Lise Svenningsen
- Martin Eivindson
- Linda Kornerup
- Niels Bjørndal Kjeldsen
- Anders Neumann
- Holger Jon Møller
- Peter Jepsen
- Henning Grønbæk
journal: Hepatology Communications
year: 2023
pmcid: PMC10043550
doi: 10.1097/HC9.0000000000000068
license: CC BY 4.0
---
# The association between soluble CD163, disease severity, and ursodiol treatment in patients with primary biliary cholangitis
## Introduction:
The macrophage activation marker soluble (s)CD163 is associated with disease severity and prognosis in patients with primary biliary cholangitis (PBC). Ursodeoxycholic acid (UDCA) treatment attenuates fibrosis progression in PBC patients, but its effect on macrophage activation is unclear. We examined the effect of UDCA on macrophage activation, as determined by sCD163 levels.
### Methods:
We included 2 cohorts of PBC patients; 1 cohort with prevalent PBC patients, and 1 cohort of incident PBC patients before start of UDCA treatment and with follow-up after 4 weeks and 6 months. We measured sCD163 and liver stiffness in both cohorts. Further, we measured sCD163 and TNF-α shedding in vitro in monocyte-derived macrophages after UDCA and lipopolysaccharide incubation.
### Results:
We included 100 patients with prevalent PBC [$93\%$ women, median age 63 y (interquartile range: 51–70)] and 47 patients with incident PBC [$77\%$ women, median age 60 y (49–67)]. Prevalent PBC patients had a lower median sCD163 of 3.54 mg/L (2.77–4.72) than incident PBC patients with a median sCD163 of 4.33 mg/L (2.83–5.99) at inclusion. Patients with an incomplete response to UDCA and patients with cirrhosis had higher sCD163 than responders to UDCA and noncirrhosis patients. After 4 weeks and 6 months of UDCA treatment median sCD163 decreased by $4.6\%$ and $9.0\%$, respectively. In in vitro experiments, UDCA attenuated shedding of TNF-α, but not sCD163, from monocyte-derived macrophages.
### Conclusion:
In PBC patients, sCD163 levels correlated with liver disease severity and treatment response to UDCA. Further, after 6 months of UDCA treatment, we observed a decrease in sCD163, which may be related to the treatment.
## Abstract
## INTRODUCTION
Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease affecting intrahepatic bile ducts.1 Disease progression is very heterogeneous with a substantial part of the patients developing biliary cirrhosis and end-stage liver disease.2 Inflammation in PBC is driven by an immune response toward mitochondrial autoantigens located in apoptotic bodies from biliary epithelial cells (BECs) and includes both CD4 and CD8 cells, which activate macrophages through granulocyte macrophage colony-stimulating factor.3–5 The activated macrophages, together with activated T cells and anti-mitochondrial antibodies (AMAs), produce a proinflammatory response with subsequent damage to BECs resulting in biliary inflammation and portal fibrosis.6 Hence, macrophages are suggested to serve as a link between injury mediated by the innate immune system and BEC apoptosis.7,8 Further, in biopsies from PBC patients, macrophages comprise ~$30\%$ of mononuclear cells found in the cellular infiltrate.9 The macrophage activation marker soluble CD163 (sCD163)10,11 is associated with disease severity and prognosis12–20 and decreases after treatment13,14,19,21,22 in a number of acute and chronic inflammatory liver diseases. Recently, our group showed that sCD163 was associated with liver disease severity, for example, alkaline phosphatase (ALP) and with long-term risk of liver-related events in PBC patients.23 However, before-and-after treatment levels of sCD163 were not investigated, and reported levels of sCD163 were not separated according to the time-point in the disease (“prevalent” or “incident”).
Ursodeoxycholic acid (UDCA) is the first-line drug for the treatment of PBC patients.2 UDCA actions include protection of cholangiocytes against toxic effects of bile acids, stimulation of impaired biliary secretion, and beneficial changes of the bile acid pool composition, along with antiapoptotic effects in hepatocytes and cholangiocytes.7,24 These effects are associated with attenuation of fibrosis progression and improved liver transplantation-free survival. However, it is unknown if UDCA affects macrophage activation, determined by sCD163 levels, in PBC patients.
We aimed to investigate the association between sCD163 and disease severity in prevalent PBC patients, and the effect of UDCA on macrophage activation in incident PBC patients. We hypothesized that sCD163 is associated with liver disease severity and that UDCA treatment reduces macrophage activation, determined by sCD163 levels.
## Participants and study design
From 2016 to 2019 we included PBC patients at our tertiary outpatient clinic into 2 different cohorts. In the first cohort, we included all prevalent PBC patients, that is patients who were currently being treated with UDCA or had previously been treated with UDCA. In the second cohort, we included incident PBC patients before they started treatment with UDCA. Patients included as incident PBC patients were seen again after 4 weeks and 6 months after UDCA treatment initiation. We included patients from both our tertiary center and from all regional hospitals in the Central Denmark Region, who referred patients to our clinic for inclusion into the study cohorts. PBC diagnosis followed current guidelines from the European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD),2,25 and autoimmune hepatitis (AIH) overlap diagnosis followed current guidelines from AASLD.25 For PBC 2 of the 3 criteria were fulfilled: [1] elevated ALP, [2] presence of AMAs, and [3] a liver biopsy specimen showing florid bile duct lesions. For the AIH diagnosis, the patients had [1] elevated alanine aminotransferase, [2] elevated serum IgG and/or presence of smooth muscle antibodies, and [3] a liver biopsy with moderate to severe interface hepatitis.
The studies were approved by the local ethics committee and reported to clinicaltrials.gov (NCT02924701 and NCT02931513). The studies were conducted in accordance with both the Declarations of Helsinki and Istanbul, and all participants gave written consent before inclusion.
## Data collection
On the day of inclusion, we collected data on age, gender, date of diagnosis, liver biopsy, UDCA treatment, and AIH overlap. In addition, we performed transient elastography (TE) to measure liver stiffness using a FibroScan (M or XL probe as necessary), and collected blood samples. In the incident PBC patients, TE and blood sampling were also done at the 4-week and 6-month visits. Alanine aminotransferase, bilirubin, ALP, platelets, IgM, IgG, INR, albumin, and creatinine were measured in blood samples.
We measured sCD163 as a standard biochemical test by an in-house sandwich ELISA using a BEP-2000 ELISA-analyzer (Dade Behring)26 with a reference interval of 0.69–3.86 mg/L.10 The intraindividual variation of sCD163 is $9.0\%$ and the interindividual variation ~$35\%$.27 In the cohort of patients with prevalent PBC, we created a dichotomous variable of cirrhosis being “yes” if the patients fulfilled one or more of the following criteria: [1] cirrhosis on a previous liver biopsy, [2] history of variceal bleeding or ascites or varices at the latest performed gastroscopy, [3] liver stiffness ≥16.9 kPa at inclusion,28 and otherwise “no.” Moreover, we used the cutoff presented by Cristoferi et al29 to distinguish between those with high risk of advanced fibrosis (TE >11 kPa) and those with low risk (TE <11 kPa), and the new Baveno VII cutoffs for compensated advanced chronic liver disease, where a TE between 10 and 15 kPa is suggestive of advanced fibrosis and a TE above 15 kPa is highly suggestive of advanced fibrosis.30 Further, in the cohort with prevalent PBC patients, patients with ALP>1.67× the upper limit of normal (175 U/I) or abnormal bilirubin (>25 µmol/L) after at least 1 year of UDCA treatment were considered incomplete responders to UDCA treatment as defined by the POISE criteria.31 All biopsies in the cohort of incident PBC patients were staged according to the Ludwig system.32 Further, in 35 biopsies with enough material available, we used immunohistochemistry to stain and digital image analysis to quantify CD163 around the portal tracts. For further details, see Supplemental Methods (http://links.lww.com/HC9/A164).
## UDCA effect on macrophage shedding of sCD163
The ADAM17 enzyme is responsible for the shedding of sCD163 and TNF-α from macrophages,33 and UDCA has been shown to inhibit the ADAM17-driven shedding of TNF-α.34 We therefore hypothesized that UDCA inhibits the ADAM17-driven shedding of sCD163 too. To evaluate this in vitro, we measured sCD163 and TNF-α shedding from monocyte-derived macrophages after UDCA and lipopolysaccharide (LPS) incubation. For further details, see Supplemental Methods (http://links.lww.com/HC9/A164). To evaluate the hypothesis in vivo, we measured concurrent levels of sCD163 and TNF-α before and after UDCA treatment. Together, these analyses will help us understand whether UDCA reduces inflammation, and whether such an effect involves macrophages or it involves activated T cells, which shed TNF-α but not sCD163.2 TNF-α was measured as part of a V-PLEX proinflammatory kit also analyzing interferon-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, and IL-13.
## Statistical analysis
Patient characteristics from both cohorts are reported as percentage and mean (SD) if normally distributed and median (interquartile range, IQR) if non-normally distributed. Correlation analyses were performed using the Spearman rank correlation. In the cohort of incident PBC patients, 46 patients attended the 4-week visit of whom 43 had their sCD163 measured, and 37 patients attended the 6-month visit. For repeated measurements, a 1-way repeated ANOVA was used. There were a few missing data on TE in the cohort with incident patients, either because it was impossible to obtain 10 valid measurements or because the patient did not meet all criteria for the examination (eg, <4 h fasting or pacemaker).
## Baseline characteristics
We included 100 prevalent PBC patients of whom 93 ($93\%$) were women and the median age was 63 years (IQR: 51–70). Median time from diagnosis to inclusion was 6.6 years (IQR: 2.5–13.2). The patients had a median sCD163 of 3.54 mg/L (IQR: 2.77–4.72) and a median TE of 5.8 kPa (IQR: 4.8–8.4), and 8 patients had a TE >16.9 kPa indicating cirrhosis (Table 1).
**TABLE 1**
| Unnamed: 0 | Prevalent patients | Incident patients |
| --- | --- | --- |
| N | 100 | 47 |
| Age, median (IQR) (y) | 62.5 (51–70) | 60 (49–67) |
| Female, n (%) | 93 (93) | 36 (76.6) |
| Time since diagnosis, y (IQR) | 6.61 (2.48–13.17) | — |
| AMA positive at diagnosis, n (%) | 72 (75.8) | 41 (87.2) |
| UDCA treatment, n (%) | 95 (95) | 43 (91.5) |
| AIH overlap, n (%) | 25 (25) | 3 (6.4) |
| ALP, median (IQR), U/I | 140 (107–213) | 281 (170–362) |
| ALT, median (IQR), U/I | 33 (23–48) | 72 (40–109) |
| Bilirubin, median (IQR), µmol/L | 9 (6–12) | 10 (7–13) |
| Albumin, median (IQR), g/L | 37 (36–39) | 37 (35–40) |
| Platelets, median (IQR), 109/L | 245 (189–296) | 257 (216–320) |
| Coagulation factor 2, 7, and 10, median (IQR) | 1.06 (0.92–1.21) | 1.04 (0.86–1.18) |
| IgM, median (IQR), g/L | 2.68 (1.49–3.82) | 2.95 (1.87–4.07) |
| TE stiffness, median (IQR), kPa | 5.8 (4.8–8.4) | 6.2 (4.7–8.7) |
| sCD163, median (IQR), mg/L | 3.54 (2.77–4.72) | 4.33 (2.83–5.99) |
Among the 47 incident PBC patients, 36 ($76.6\%$) were women and the median age was 60 years (IQR: 49–67). At inclusion, the median sCD163 was 4.33 mg/L (IQR: 2.83–5.99) and the median TE was 6.2 kPa (IQR: 4.7–8.7) (Table 1). Forty-one of the 47 patients underwent liver biopsy of whom 26 ($63.4\%$) had stage 1 fibrosis according to the Ludwig system; 10 ($24.4\%$) had stage 2; 2 ($4.9\%$) had stage 3; and 3 ($7.3\%$) had stage 4.
## Association between sCD163 and disease severity
In the cohort of prevalent PBC patients, those with cirrhosis ($$n = 13$$) had higher median sCD163 levels (5.39 mg/L, IQR: 3.98–5.67) than those without cirrhosis ($$n = 87$$, median=3.21 mg/L, IQR: 2.67–4.47), $p \leq 0.001.$ Similarly, those with higher risk of advanced fibrosis had higher median sCD163 ($$n = 18$$, median=5.3 mg/L, IQR: 4.4–6.4) than those with lower risk of advanced fibrosis ($$n = 82$$, median=3.2 mg/L, IQR: 2.7–4.4), $p \leq 0.001.$ Further, those who was highly suggestive of compensated advanced chronic liver disease had higher median sCD163 ($$n = 9$$, median=5.6 mg/L, IQR: 5.4–6.6) than those who was suggestive of compensated advanced chronic liver disease ($$n = 10$$, median=4.7 mg/L, IQR: 3.7–5.4), who had higher median sCD163 than those with low risk of compensated advanced chronic liver disease ($$n = 81$$, median=3.2 mg/L, IQR: 2.7–4.3), $p \leq 0.001.$ The 26 incomplete responders to UDCA had higher median sCD163 (4.51 mg/L, IQR: 3.10–5.50) than the 64 responders (median=3.16 mg/L, IQR: 2.58–4.20), $$p \leq 0.004.$$ Ten patients had been treated with UDCA for <1 year. There was no difference in disease duration or the proportion of patients with cirrhosis between responders and incomplete responders.
sCD163 correlated with ALP and bilirubin in both the prevalent and incident PBC patients, but the correlation strength was modest (Figures 1 and 2). Further, sCD163 and TE correlated in the prevalent PBC patients (Figure 1). Correlations between sCD163 and bilirubin, TE, and ALP were stronger in the 26 incomplete responders than in the 64 responders.
**FIGURE 1:** *Scatterplots (lower left panels) and Spearman rho correlations (upper right panels) between sCD163, ALP, bilirubin, and TE in 100 prevalent PBC patients. Notes: sCD163 (mg/L), ALP (U/I), bilirubin (µmol/L), TE (kPa). Abbreviations: ALP, alkaline phosphatase; PBC, primary biliary cholangitis; sCD163, soluble CD163; TE, transient elastography.* **FIGURE 2:** *Scatterplots (lower left panels) and Spearman rho correlations (upper right panels) between sCD163, ALP, bilirubin, and TE in 44 incident PBC patients at baseline (3 patients had missing data on TE). Notes: sCD163 (mg/L), ALP (U/I), bilirubin (µmol/L), TE (kPa). Abbreviations: ALP, alkaline phosphatase; PBC, primary biliary cholangitis; sCD163, soluble CD163; TE, transient elastography.*
We did not observe any correlation between sCD163 and CD163 around the portal tracts in 35 biopsies (Spearman rho=0.24, $$p \leq 0.162$$). In 32 patients with <3 months between liver biopsy and blood sampling, the correlation between sCD163 and CD163 around the portal tracts was considerably stronger (Spearman rho=0.39, $$p \leq 0.025$$).
## UDCA and sCD163
Following UDCA treatment, median sCD163 decreased by $4.6\%$ after 4 weeks, and by $9.0\%$ after 6 months (Table 2). Of the 37 patients with follow-up data after 6 months of UDCA treatment, 26 had a decrease in sCD163 and 11 had an increase in sCD163 (Figure 3). Of those 11, 6 had normal sCD163 at baseline; and of those 6, 5 had a sCD163 still within the normal range after 6 months and the 1 remaining patient had the sCD163 increased to 3.87 mg/L (Figure 3). Two of the 11 also had an increase in their ALP (Figure 3).
Before UDCA treatment, sCD163 and TNF-α correlated moderately (Spearman rho=0.38, $$p \leq 0.008$$). After 6 months of treatment with UDCA, TNF-α was lower than that before treatment (Table 2). However, changes in sCD163 and TNF-α after UDCA treatment did not correlate and 16 of the 36 patients had discordant changes in sCD163 and TNF-α (Figure 4). Results from the remaining inflammatory markers are given in Supplementary Table 1 (http://links.lww.com/HC9/A164).
**FIGURE 4:** *Changes in sCD163 and TNF-α after 6 months of UDCA treatment. Notes: sCD163 (mg/L); TNF-α (pg/mL). Abbreviations: sCD163, soluble CD163; UDCA, ursodeoxycholic acid.*
## UDCA and macrophage shedding of sCD163 and TNF-α
In in vitro analysis, LPS stimulated sCD163 shedding from macrophages as expected (Figure 5). Further, there was a tendency to a higher sCD163 shedding with increasing UDCA concentration both in those with and those without LPS incubation. There was no TNF-α shedding in those not stimulated with LPS. In those stimulated with LPS, the highest dose of UDCA (1 mM) decreased the TNF-α shedding to a level almost comparable to that observed in those without LPS stimulation (Figure 5).
**FIGURE 5:** *In vitro analysis of sCD163 and TNF-α shedding from monocyte-derived macrophages preincubated with UDCA for 1 hour followed by incubation with 100 ng/mL LPS for 1 hour. Numbers above brackets are p values, n=4. Notes: sCD163 (mg/L), TNF-α (pg/mL). Abbreviations: LPS, lipopolysaccharide; sCD163, soluble CD163; UDCA, ursodeoxycholic acid.*
To summarize the changes in sCD163 and TNF-α from the in vivo and in vitro analyses, there was a reduction in median sCD163 and TNF-α after UDCA treatment in vivo, but only shedding of TNF-α from macrophages decreased, and only at the highest concentration of UDCA, in the in vitro experiment.
## DISCUSSION
Among patients with an established diagnosis of PBC, we confirmed that sCD163 is a marker of liver disease severity with increased levels in PBC patients with cirrhosis and in patients with an incomplete response to UDCA. As novel findings, in a prospective cohort of incident PBC patients, our data suggest that UDCA treatment reduced macrophage activation as determined by sCD163 levels. This finding may suggest that UDCA have an anti-inflammatory effect partly mediated through the inhibition of macrophage activation.
Levels of sCD163 presented here are comparable with those observed in early stage disease of other chronic liver diseases, that is hepatitis B and C and alcohol-associated liver disease,12,35,36 and levels in the group of “prevalent” PBC patients are comparable to those previously presented in PBC patients.23,37 Further, we observed that patients with an incomplete response had higher levels of sCD163 than those with a complete response similar to what was previously reported.23 Unfortunately, no data describing the levels of sCD163 before and after UDCA treatment have previously been published in other cholestatic liver diseases such as PSC, and hence we have no such data to compare with.
In PBC pathogenesis, liver macrophages are activated by CD4 and CD8 lymphocytes stimulated by the pyruvate dehydrogenase complex (PDC-E2), which is the autoantigen targeted by AMAs.3–5 The activated macrophages produce a proinflammatory response through the activation of cytotoxic T cells as well as Th1-positive and Th17-positive T cells producing interferon-γ and TNF-α. This proinflammatory milieu is associated with subsequent damage to BECs and destruction of bile ducts accompanied by bile leaking into the liver parenchyma causing damage to hepatocytes with subsequent biliary inflammation and portal fibrosis.2,6 In line with this, we observed an association between sCD163 and TNF-α in the newly diagnosed PBC patients before UDCA treatment. Further, when macrophages are activated, they may contribute to fibrogenesis, and it has consistently been shown that sCD163 levels are associated with fibrosis in other chronic inflammatory liver diseases.38 Thus, macrophages are thought to play a key role in PBC pathogenesis with inflammation and later fibrosis development.
We showed that UDCA has an anti-inflammatory effect with reduced levels of macrophage activation marker sCD163 and TNF-α after 6 months of treatment. The anti-inflammatory effect of UDCA has previously been reviewed,24 and in short, UDCA is reported to decrease TNF-α, TGF-β, IL-2, IL-4, and INF-γ, whereas IL-1, IL-6, IL-8, and IL-12 are unaffected by UDCA treatment.39–41 In our in vitro experiment, there was no reduction in sCD163 shedding from monocyte-derived macrophages after incubation with UDCA, whereas there was a large reduction of TNF-α shedding in those incubated with high doses of UDCA. This suggests that the anti-inflammatory effect on the macrophages observed in vivo is indirect. Further, our observation of no correlation between changes in sCD163 and TNF-α suggest that UDCA does not inhibit macrophages and activated T cells per se. Thus, the decrease in both sCD163 and TNF-α after UDCA treatment may suggest that both macrophages and T-cells are affected by UDCA. The discrepancy between sCD163 and TNF-α changes may also be due to different half-lives, as shown after LPS stimulation.42 Previous studies investigating sCD163 and TNF-α changes showed conflicting results: Two studies found parallel increases after induction of inflammation,43,44 whereas one study found unparallel changes after lifestyle interventions in obese children.13 In our study, patients with high sCD163 before start of treatment demonstrated a consistent decrease in sCD163 levels during treatment, whereas this was not the case in patients with sCD163 levels within the normal range before treatment. This possibly reflects that patients with normal sCD163 levels have less inflammation in the liver related to PBC, and hence UDCA is less likely to reduce the inflammation.
Currently, there is a clinical focus on identifying noninvasive markers of disease progression, prognosis, and treatment response in PBC research.45,46 *This is* the first study to present repeated measurements of the macrophage activation marker sCD163 before and after UDCA treatment, and to show that the marker decreases after UDCA treatment. Moreover, we observed that the levels of sCD163 were higher in the cohort of incident PBC patients than in patients with prevalent PBC, suggesting that the effect of UDCA treatment on macrophage activation is long lasting. It is of key interest to identify patients with incomplete response to UDCA, preferably already at diagnosis.47 *In this* study, we observed that patients who had an incomplete response to UDCA had higher sCD163 levels than responders. This finding indicates a possible role for the prediction of such patients using sCD163, and we suggest future trials and long-term observational studies to include sCD163 to further investigate the role of macrophage activation in PBC patients.
The strength of the present study is the high number of well-characterized PBC patients including follow-up in a smaller group of newly diagnosed patients before and after UDCA treatment. However, it is a limitation, that we only have 6 months’ follow-up, which limits our ability to investigate response to UDCA and whether pretreatment sCD163 levels predict response to UDCA. Moreover, we found relatively small changes in sCD163 after UDCA treatment, in relation to the natural biological variation, but even though this may suggest that smaller changes are not detected if analyzed several times in the same patient we did observe a significant change in the group of patients investigated.
In conclusion, we demonstrated an association between disease severity and macrophage activation in patients with PBC, and that incomplete responders to UDCA had higher sCD163 levels than responders. Further, our data may suggest that UDCA treatment indirectly reduced macrophage activation in PBC patients as determined by sCD163 levels.
## CONFLICTS OF INTEREST
Lars Bossen and Henning Grønbæk have received an investigator-initiated research grant from Intercept Pharmaceuticals Inc. Henning Grønbæk also received research grants from Abbvie, Arla, ADS AIPHIA Development Services AG, and the NOVO Nordisk Foundation. The remaining authors have no conflicts to report.
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|
---
title: 'Acute on chronic liver failure: prognostic models and artificial intelligence
applications'
authors:
- Phillip J. Gary
- Amos Lal
- Douglas A. Simonetto
- Ognjen Gajic
- Alice Gallo de Moraes
journal: Hepatology Communications
year: 2023
pmcid: PMC10043584
doi: 10.1097/HC9.0000000000000095
license: CC BY 4.0
---
# Acute on chronic liver failure: prognostic models and artificial intelligence applications
## Abstract
Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.
## INTRODUCTION
AI applications are being increasingly utilized in healthcare. Hepatology is no exception. Recently, there has been a significant application of artificial intelligence (AI) methodologies to hepatology including radiomics, histopathology, predictive and prognostic modeling, and estimation of posttransplant outcomes.1–4 To date, however, little data exist regarding AI applications specifically for critically ill patients with chronic liver disease the majority of whom meet diagnostic criteria for ACLF. This review seeks to outline ACLF as a syndrome, discuss current prognostic models for ACLF, define AI applications that are being used in healthcare, and discuss both current and potential AI applications in ACLF specifically.
## ACLF
ACLF is a recently recognized syndrome and is associated with increased short-term mortality.5,6 ACLF represents a syndrome in which there is systemic inflammation, in the setting of an inciting event, leading to the development of 1 or more organ failures including, but not limited to, acute decompensation of cirrhosis.5,7,8 The pathophysiology of ACLF is still unknown but immune dysregulation, similar to that seen in sepsis, with severe inflammation and oxidative stress have been implicated.9 In up to $40\%$ of patients presenting with ACLF, no precipitating event is identified. Infection, in particular spontaneous bacterial peritonitis and pneumonia, has been deemed a common cause of ACLF and has been independently associated with mortality.5,10 Other proposed precipitants include gastrointestinal bleeding, alcohol-associated hepatitis, drug-induced hepatitis, acute or reactivation of viral hepatitis, and less commonly, surgery, TIPS creation, or trauma.5,7,11,12 Identification of and intervention targeted toward the underlying etiology is essential and prospective data have demonstrated that ACLF is potentially reversible especially when considering the ACLF grade at presentation.13 Given the dynamic nature of ACLF, aggressive critical care focusing on identifying and addressing the associated organ failure or failures, is recommended for the first 3–7 days, while considering prognosis based on the patient’s progression or improvement.11 Current prognostic scores as highlighted below, were developed predominantly with traditional statistical methods and are limited in their capability of providing actionable or meaningful data in the acute phase of critical illness.
Currently, multiple definitions exist for ACLF as outlined by the European Association for the Study of the Liver—Chronic Liver Failure (EASL-CLIF), North American Consortium for the Study of End-stage Liver Disease (NACSELD), and the Asian Pacific Association for the Study of the Liver (APASL) (Table 1).16 These definitions cohort ACLF patients based on geographic distribution and etiology.
**TABLE 1**
| Group | EASL-CLIF5 | NACSELD14 | APASL (AARC)15 |
| --- | --- | --- | --- |
| Criteria | Hospitalized for AD cirrhosis, CLIF-SOFA ≥1, 28-d mortality >15% due to AD of cirrhosis+prior AD | Hospitalized with infection or develop infection while hospitalized, ≥2 OFs | Hospitalized for AD chronic liver disease (±cirrhosis), acute jaundice, and coagulopathy±HE |
| Exclusions | Scheduled procedure or treatment, HCC outside Milan criteria, severe chronic extrahepatic disease, HIV, immunosuppressives | Outpatient, HIV history, prior transplant, advanced malignancy | Bacterial infection or prior AD |
The EASL-CLIF definition was developed using a cohort of patients with predominantly alcohol-induced liver disease and utilized a Chronic Liver Failure—Sequential Organ Failure Assessment (CLIF-SOFA) score, which was a prespecified modified Sequential Organ Failure Assessment (SOFA) score specifically developed for the purposes of identifying and grading ACLF.5,17 The NACSELD definition was adapted from a previously defined infection-related ACLF score and subsequently validated in both infected and noninfected patients.11,14 The APASL definition was developed using a cohort of patients with predominantly HBV-related liver disease and has been recently validated in a non-Asian/Pacific Islander population.14,15,18,19 Prior prognostic scores used to estimate the degree of portal hypertension and qualify the severity of cirrhosis, Model for End-stage Liver Disease (MELD), Model for End-stage Liver Disease-Sodium (MELD-Na), Child-Turcotte-Pugh (CTP), among many, are likely not weighted properly to be meaningfully applied to ACLF specifically. Presumably due to the degree of inflammation present in ACLF, models incorporating scores detecting global organ dysfunction [SOFA, CLIF-SOFA, Acute Physiology and Chronic Health Evaluation II (APACHE II)] have been shown to be superior to liver-specific models.20–22 For example, the Chronic Liver Failure Consortium acute on chronic liver failure (CLIF-C ACLF) score has emerged as a leading prognostic model and outperformed CTP, MELD, and MELD-Na scores when predicting 28-day mortality in patients requiring admission to the hospital with ACLF. This score combines a Chronic Liver Failure—Organ Failure (CLIF-OF) score, which is a simplified addition to the aforementioned CLIF-SOFA score with age and white blood cell count.6,23 Other prognostic models have been validated in HBV-ACLF specifically.24–28 These scores have been validated only in specific cohorts (CLIF, APASL, etc.) which limits their generalizability to the overall heterogeneous global ACLF population.
The clinical course of ACLF in the initial days following the identification, that is, trajectory, likely has more prognostic value than the initial severity and might ultimately have a more meaningful impact when it comes to minimizing harm, limiting unnecessary cost, and improving end of life.13 EASL-CLIF ACLF grades, however, have been shown to correlate with 28-day mortality.5 Somewhat counter to this are data that suggest that patients with ACLF who undergo liver transplantation have similar mortality at 1 year regardless of severity grade before transplantation.29 *Using data* from the VOCAL study group evaluating patients with ACLF diagnosed using both EASL-CLIF and APASL criteria, models (VOCAL-Penn) have been developed for predicting the development of ACLF as well as mortality.16,30 They have been shown to better predict mortality when compared with MELD, MELD-Na, and CLIF-C ACLF models.16,30
## AI
Initially labeled by John McCarthy in 1955, AI is a nondescript term used to define a broad range of techniques that allow computers to perform tasks typically thought to require human reasoning and problem-solving skills.4,31 Modern AI applications typically involve a combination of multiple techniques to achieve a desired goal. Healthcare, given its complexity, is no exception to this. What follows is an overview of a selection of AI techniques relevant to healthcare (Figure 1).
**FIGURE 1:** *A simplified visual representation of AI applications in ACLF.*
Beneath the umbrella of AI applications, machine learning (ML) encompasses various techniques including supervised ML, unsupervised ML, semisupervised learning, reinforcement learning, neural networks, and deep learning (DL). Supervised ML involves labeled input and output data which the algorithm then uses to create a mathematical model from as it “learns” relationships between input and output data. Supervised ML is a technique frequently used in healthcare. Labeled input data represent various aspects of patient-level data and labeled output data represent various desired or expected clinical endpoints. The algorithm itself interprets input data and relationships within data. Unsupervised ML allows the algorithm to extract features from unlabeled input data without output data in an exploratory nature and effectively allowing the data to “speak for itself. ”4,32,33 Semisupervised ML, combining both supervised and unsupervised ML, incorporates unlabeled and labeled input and output data and the model is then iteratively trained as it effectively labels previously unlabeled data. Reinforcement ML models incorporate a system of reward and penalty with the algorithm targeting maximum reward or avoiding penalty based on desired outputs. These models learn iteratively by observing prior actions, sometimes in a simulated fashion, and subsequently, create and apply algorithms that maximize the return of reward-associated outputs.34 For simplicity’s sake, both semisupervised and reinforcement ML can also be classified as supervised ML. DL is a ML technique involving artificial neural networks (ANNs) that is designed with a number of layers between input and output data. A series of algorithms interact and form “hidden layers,” usually in a feed-forward fashion through connections that resemble neurons in a mammalian brain. The model can be further trained with more input data and errors can be addressed using a principle known as backpropagation.31,35 These models are typically developed from large and complex databases with multiple hidden neural layers and without interpretability constraints. They, therefore, provide limited transparency to the users and are described as “black-box” models. The user of “black-box” AI knows inputs and understands the outcomes of the model but how the output value was generated remains unknown due to the complexity of the algorithms created.34 Natural language processing is a technique that allows extraction of text from various data sources. In healthcare, this is typically used to extract text data and patient-level information which can then be utilized as input in other AI models.4 Knowledge engineering, more specifically expert systems, consist of designated domain experts contributing known or consensus-derived knowledge to better understand complex human decisions or outline complex human physiology or pathophysiology. Bayesian or probabilistic models, particularly in the form of directed-acyclic graphs (DAGs), use variables and their conditional dependencies to create a visually accessible way of representing expert systems and causal pathways. Multiple less complex DAGs can then be combined into larger, more complex hierarchical models.36 Digital twins are simulation models, created using DAGs using both data and expert knowledge, which can receive inputs from their real-world “twin” and return outputs that can enhance insight and assist in decision-making.34 Digital twins have been developed in various engineering and geopolitical arenas but are only recently being applied to healthcare.37–39 These models offer the benefit of being able to provide actionable knowledge whether in an educational environment, in research, or at the bedside.34
## AI and ACLF
In recent years, there has been a significant increase in the application of AI to the fields of critical care and hepatology. This has coincided with significant advancement in AI methodology and computing power as well as the availability of large data cohorts. A recent review by Ahn et al4 summarizes various applications of AI in hepatology. Prior prognostic modeling and scoring tools for ACLF utilized logistic regression and related methodology. It is important to note, however, that the majority of these and other models have been developed in a data-driven fashion utilizing registry data or retrospective cohorts.4,40 These models tend to evaluate long-term outcomes and in some cases improve diagnosis, but they may only provide limited prognostic enrichment compared with prior models which mostly provided long-term prognostic estimates. Interestingly, when used for clinical modeling various ML approaches had previously been shown to be no better than traditional statistical approaches, but this field is growing rapidly.41 A few recent studies in ACLF patients have effectively utilized AI approaches. Table 2 outlines prognostic models and other approaches leveraging AI methods recently applied to ACLF.42–46
**TABLE 2**
| References | Methodology | Criteria | Outcome | Comparators | Performance | Comments |
| --- | --- | --- | --- | --- | --- | --- |
| Zheng et al42 | ANN | APASL | 90-d mortality | MELD, MELD-Na, MELDNa, MESO, iMELD | AUROC 0.765, p < 0.0001 | Causes of ACLF other than HBV were excluded |
| Xu et al43 | Imbalanced and semisupervised ML, SVM | | Predictive accuracy | MELD | | Model designed to predict outcomes in cohorts with missing or incomplete data |
| Shi et al24 | CART, LR | APASL (suspected) | 90-d mortality | MELD | AUROC CART: 0.896, LR 0.914 p < 0.001 | Causes of ACLF other than HBV were excluded |
| Garcia et al44 | Semisupervised ML, LR | EASL-CLIF | Predictive accuracy for ACLF-related mortality | | | Methodological approach to data preparation for AI modeling |
| Hou et al45 | ANN | APASL | 28- and 90-d mortality | MELD, MELD-Na, CTP, CLIF-ACLF | AUROC 0.748 and 0.754, respectively | Causes of ACLF other than HBV were excluded |
| Musunuri et al46 | ANN | APASL | 30- and 90-d mortality | | AUROC 0.915 and 0.921, respectively | Patients with malignancy were excluded |
Zheng and colleagues created and validated a model including patients diagnosed with HBV-associated ACLF by APASL criteria. Patients with causes of liver disease other than HBV were excluded from both the training and validation cohorts. Using ANN, patient-level modeling was compared with various MELD-based predictors of mortality with 90-day mortality as an outcome. In the validation cohort, authors reported an area under the receiver operating characteristic 0.765 ($95\%$ CI: 0.608–0.722, $p \leq 0.0001$) demonstrating increased accuracy compared with common MELD-based scoring systems.42 Hou and colleagues created and validated 2 ANN models to predict 28- and 90-day mortality also in patients with HBV-associated ACLF meeting APASL criteria. Compared with MELD, both models were found to be more accurate using the training and validation data sets. This study was novel given the creation of an accurate 28-day mortality prediction model using ML. There were differences in predictive accuracy between the training and validation cohorts for both models that may be explained by the varied mortality rates in each cohort.45 Musunuri and colleagues similarly created and validated an ANN that unlike prior models included numerous predictive scores such as MELD, quickSOFA, CLIF-SOFA, and CTP in addition to demographic and biochemical characteristics. Patients included were diagnosed with HBV-associated ACLF by APASL criteria. The validated models demonstrated predictive accuracy for 30- and 90-day mortality with the area under the receiver operating characteristic 0.915 and 0.921, respectively.46 Shi and colleagues evaluated a large cohort of over 1000 patients with suspected ALCF due to HBV and created and validated novel classification and regression tree and logistic regression models that outperformed the traditional MELD score with the area under the receiver operating characteristic 0.896 and 0.914, respectively ($p \leq 0.001$). Given their improved accuracy and specificity to ACLF, these scoring systems may serve as comparators for future ML approaches, especially when evaluating 90-day mortality in patients with ACLF by APASL criteria.24 *In a* methodological approach, Xu and colleagues utilized supervised learning techniques similar to small sphere and large margin and twin support vector machines to develop prediction models for ACLF patients with missing demographic and biochemical data and imbalanced mortality and survival outcome data. Models were then compared based on their ability to accurately predict mortality. Mortality prediction was then also correlated with MELD scores.43 Garcia and colleagues utilized the large CANONIC database consisting of patients meeting EASL-CLIF criteria for ACLF in a methodologic and exploratory study. The authors outlined logistic regression-related methods for preparing heterogeneous patient data with many inconsistencies ranging from missing data resulting from patient death and high short-term mortality to large amounts of noninformative patient-level data. They targeted patient characteristics expected to predict mortality and formulated an approach to preparing such data for ML application to be used in future studies.44ML techniques have also been used to predict liver transplant outcomes including allograft fibrosis and while these were not targeted specifically toward an ACLF population they may ultimately be applicable when combined with other AI applications.47–51 These studies demonstrate the benefits of utilizing AI approaches for predictive modeling and other aspects of ACLF. When compared with traditional scoring systems there was improved prognostic accuracy. These approaches present tools for preparing and analyzing heterogeneous or incomplete data, which are common for ACLF patient-level data, with higher accuracy than the currently applied standards which were not created for ACLF patients. However, with the exception of the methodologic study conducted by Garcia and colleagues which included patients meeting EASL-CLIF criteria for ACLF, data to date are limited in scope to a predominantly HBV-associated ACLF cohort fulfilling APASL criteria. Further, the majority of the predictive models developed using AI approaches utilized an ANN which is a black-box approach. In the context of ACLF, AI approaches are especially subject to limitations related to such black-box approaches, biases in dataset creation, limited and heterogeneous data due to high short-term mortality, preexisting geographical cohort distributions, and minimal prospective data available for validation.
When thinking about the near future, applying novel AI techniques to ACLF patient populations has the potential to shed light on treatment strategies, transform our current understanding of patient outcomes including organ transplantation, and even highlight previously unknown or misunderstood physiological and pathophysiological mechanisms.52 ML, more specifically reinforcement learning, has been shown to “learn” optimal treatment strategies in septic patients which represent a population with a complex and incompletely understood pathophysiology similar to ACLF.53 AI, specifically supervised ML, offers the opportunity to improve on existing prognostic and predictive models in ACLF and the potential to significantly impact disease trajectory in the short term. There may also be benefit to ML approaches to ACLF populations to better understand pathophysiology, adapt or redefine current definitions, outline novel phenotypes, or influence prospective trial design.54–58 To date, we are unaware of data evaluating natural language processing or digital twins and other expert systems in patients with ACLF but data have demonstrated that expert systems can replicate and accurately estimate outcomes in diseases with complex underlying pathophysiology such as diabetes and sepsis.59–61 *These data* are promising given similarities to ACLF given the acute on chronic flow of patients throughout health systems similar to diabetes and complex pathophysiological mechanisms outlined in ACLF resembling sepsis.9 DAGs, which are developed from current understanding of the pathophysiology of disease are forms of expert systems that one can envision will allow one to identify points (variables) where interventions may have impact on disease progress and therefore impact outcomes through clinical decision support. With an iterative approach using expert consensus, retrospective and prospective data, digital twins can be iteratively refined, validated, and avoid or limit the downsides of traditional “black-box” ML approaches.36 These “in-silico” patients offer a means to assess interventions at or at least temporally proximate to the point of care. Providers can then assess expected impact without exposing actual patients to interventions that are not so useful or even harmful. One can also envision a more personalized approach to care of ACLF patients by leveraging AI such as natural language processing, ML, and other relevant data to create digital twins or other expert systems and tailor therapies to each patient throughout various phases of their care.
We anticipate that eventually these models can also be used for medical education and other training purposes and, with proper validation, to evaluate decisions in critically ill patients including those with ACLF without exposing patients to unnecessary risk.
## Ethical considerations and AI limitations
While AI, and, more specifically, expert systems will enable a more individualized approach to care, we must be mindful of ethical issues and address them before and as they arise. In North America and Europe, major oversight organizations have set regulations on AI applications but such applications in healthcare are a new phenomenon. With the rapid expansion of AI in healthcare, regulatory mechanisms remain limited in their scope especially when it comes to ML applications.62 Data privacy, including individual and institutional rights to health-related data, will require consideration. Availability of these technologies could also have ethical and societal implications due to discrimination and isolation of certain populations especially when it comes to the potential for training validation data sets with bias and subsequent resource allocation or decision support. As these technologies are developed, proper refinement and transparency will be required and during this process they should augment and supplement provider decision-making rather than replace it. Inevitably, decisions “influenced” by AI will have implications when it comes to allocation of responsibility and lines between algorithm-based decisions and clinician decisions will become further blurred. Questions regarding well-established ethical concepts such as patient autonomy, beneficence, nonmaleficence, and justice will inevitably arise.37,63
## CONCLUSIONS
ACLF is a recently established syndrome associated with significant mortality. Current data are almost exclusively limited to predictive and prognostic models developed using traditional statistical approaches based on retrospective data. Modern AI approaches are increasingly being applied to ACLF and those previously developed for similarly complex disease processes can likely be applied to ACLF to enhance education and training as well as to aid decision-making. As with all new technologies and especially given the complexity of related decision-making, ethical implications, and methodologic considerations unique to these new approaches must be considered when applying AI to patients with ACLF.
## FUNDING INFORMATION
This study was supported by a grant from the Critical Care IMP Research Subcommittee, Mayo Clinic, Rochester, Minnesota, USA.
## CONFLICT OF INTEREST
Douglas A. Simonetto consults for Mallinckrodt and BioVie. The remaining authors have no conflicts to report.
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Academy of Royal Medical Colleges. Artificial intelligence in healthcare. 2019. Accessed February 10, 2022. https://www.aomrc.org.uk/wp-content/uploads/2019/01/Artificial_intelligence_in_healthcare_0119.pdf.
|
---
title: A novel immune-related model to predict prognosis and responsiveness to checkpoint
and angiogenesis blockade therapy in advanced renal cancer
authors:
- Peng Chen
- Feng Bi
- Weili Tan
- Lian Jian
- Xiaoping Yu
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10043594
doi: 10.3389/fonc.2023.1127448
license: CC BY 4.0
---
# A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer
## Abstract
### Background
Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC.
### Materials and methods
Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified via weighted gene co-expression network analysis. We also conducted single-sample gene set enrichment analysis to develop a novel immune-related risk score (IRS) model and further estimate the prognosis of patients with RCC by predicting their sensitivity to chemotherapy and responsiveness to immunotherapy. IRS model was further validated using the JAVELIN Renal 101 cohort, the E-MTAB-3218 cohort, the IMvigor210 and GSE78220 cohort. Predictive significance of the IRS model for advanced RCC was assessed using receiver operating characteristic curves.
### Results
The IRS model was constructed using nine immune-associated DEGs: SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH and CCL3L1. Advanced RCC patients with high IRS had a high risk of undesirable clinical outcomes (hazard ratio = 1.91; $95\%$ confidence interval = 1.43–2.55; $P \leq 0.0001$). Transcriptome analysis revealed that the IRS-low group exhibited significantly high expression levels of CD8+ T effectors, antigen-processing machinery, and immune checkpoints, whereas the epithelial–mesenchymal transition pathway was enriched in the IRS-high group. IRS model effectively differentiated the responders from non-responders to ICB combined with angiogenesis blockade therapy or immunotherapy alone, with area under the curve values of 0.822 in the IMmotion151 cohort, 0.751 in the JAVELIN Renal 101 cohort, and 0.776 in the E-MTAB-3218 cohort.
### Conclusion
IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.
## Introduction
RCC is the 12th most common solid tumor that accounted for > 400,000 new diagnoses and approximately 175,000 cancer-associated deaths worldwide in 2018 [1]. Approximately $25\%$ of RCC cases are diagnosed at an advanced stage [2]. Clear cell RCC (ccRCC) is the most frequent histological subtype, accounting for approximately $75\%$ of all renal tumors [3]. Approximately $20\%$ of patients with metastatic RCC (mRCC) have sarcomatoid elements. Sarcomatoid RCC (sRCC) is a rare subtype of RCC characterized by aggressive biology with rapid metastasis, unsatisfactory clinical outcomes, and limited efficacy of anti-angiogenic therapies (4–6).
Loss or mutation of the von Hippel-Lindau (VHL) gene is one of the primary characteristics of ccRCC that leads to the constitutive activation of the hypoxia-inducible factor, which further activates the vascular endothelial growth factor (VEGF) and increases angiogenesis in the ccRCC tumor microenvironment (7–12). Targeting the VEGF pathway with receptor tyrosine kinase inhibitors (TKIs), such as sunitinib, or anti-VEGF monoclonal antibodies, such as bevacizumab, is the first-line treatment for locally advanced or metastatic RCC [13, 14]. However, almost all patients develop drug resistance over time, and particular patient subgroups, including those with sRCC and/or those expressing the programmed death-ligand 1 (PD-L1), hardly benefit from VEGF pathway blockade (15–17). Therefore, it is necessary to explore novel therapeutic targets and drug combinations for patients with mRCC [18, 19].
Intervention with immune checkpoint inhibitors (ICIs), such as anti-PD-L1 antibody atezolizumab, has induced durable responses and improved the survival of patients with mRCC [16, 20]. T cell-mediated tumor cytotoxicity of atezolizumab can be strengthened by counteracting the VEGF-mediated immunosuppressive effect via the addition of bevacizumab [21]. Owing to variable hypervascularity, immune cell infiltration, and PD-L1 expression in ccRCC, blocking the VEGF pathway and PD-L1 axis as a combination therapy has significantly prolonged the overall survival (OS) of patients with mRCC. A phase 2 study revealed that in a subset of patients with mRCC with PD-L1 expression, compared to sunitinib as a single drug, atezolizumab combined with bevacizumab significantly increased progression-free survival (PFS) and percentage of patients achieving an objective response, indicating the complementary activity of bevacizumab and atezolizumab in patients with mRCC [20]. To reduce the financial burden and side effects of tumor therapy, it is necessary to develop effective strategies to select a subgroup of patients who can achieve optimal improvement with a specific combination therapy for mRCC.
In this study, we aimed to correlate the clinical annotation with molecular mechanisms by comprehensively analyzing the multi-omics information of 407 patients with advanced RCC from a randomized global phase III trial (IMmotion151). We also established a novel and promising prognostic model composed of nine immunotherapy-associated genes to accurately stratify a subset of patients with advanced RCC who can benefit from anti-angiogenic combined with ICB (atezolizumab plus bevacizumab) therapy. Moreover, our model can be used to develop personalized treatment strategies for patients with advanced RCC.
## Collection and processing of data
To determine the correlation between the immune-related risk score (IRS) and efficacy of cancer therapy, five immunotherapeutic cohorts with available RNA-seq data and clinicopathological parameters were included in this study: [1] IMmotion151 cohort, advanced patients with RCC treated with atezolizumab plus bevacizumab [22], [2] JAVELIN Renal 101 trial, advanced patients with RCC treated with the combination of avelumab (anti-PD-L1) + axitinib (TKI targeting VEGF receptors) vs. sunitinib (multitarget TKI) [23], [3] E-MTAB-3218 dataset, patients with mRCC treated with nivolumab (anti-PD-1 ICI) [24], [4] IMvigor210 cohort, patients with advanced urothelial cancer treated with atezolizumab [25], and [5] GSE78220 cohort, patients with metastatic melanoma treated with pembrolizumab (anti-PD-1 antibody) [26].
All data from the IMmotion151 cohort is deposited in the European Genome-Phenome Archive under the accession number EGAS00001004353, and we obtained it according to Hoffmann-La Roche policy. Clinical response information and normalized RNA-seq data were acquired from the supplementary material of Choueiri et al. [ 23]. Original data of E-MTAB-3218 dataset were downloaded from https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-3218?accession=E-MTAB-3218# [24]. Additionally, RNA-seq and clinical data for the IMvigor210 cohort were obtained from http://research-pub.gene.com/IMvigor210CoreBiologies. Raw data were normalized using the “DEseq2” R package and further transformed into TPM values. RNA-seq data (FPKM-normalized) and the clinical phenotypes of 28 melanoma patients in the GSE78220 cohort were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220. Additionally, well-recognized immune-related genes were downloaded from http://www.gsea-msigdb.org/gsea/msigdb/index.jsp [27].
Above datasets merely contain anonymized and de-identified patient information. Secondary analysis of de-identified data was confirmed exempt from review by the medical ethics committee of Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine as it was classified as negligible risk research. Thus, our study was exempt from the ethical review or the patient consent.
## Establishment of a weighted gene co-expression network (WGCNA)
WGCNA is a widespread systematic algorithm used to generate gene modules with similar expression patterns and determine the correlations between modules and clinical traits [28]. In this study, we screened the immune-related gene expression profiles in the IMmotion151 cohort and further identified a correlation network including significant clinical characteristics and genes using the “WGCNA” R package. We also developed an adjacency matrix to characterize the correlation strength between the nodes, which was further changed to a topological overlap matrix. Subsequently, modules containing more than 30 genes were identified via hierarchical clustering. To compare the co-expression levels, modules were clustered based on their correlation with module eigengenes (MEs). When the correlation of MEs > 0.80, module merging was performed, indicating that the expression profiles of the modules were similar [29]. Pearson’s correlation coefficient was used to assess the correlations between the modules and various clinicopathological parameters. Finally, gene significance (GS) and module membership (MM) were used to quantify the relationships between the genes and the clinicopathological characteristics in the module. *Hub* genes were considered as those with MM > 0.8 and GS > 0.2 [29].
## Identification of significant immune-related differentially expressed genes (DEGs) between responders and non-responders
Based on the results of WGCNA analysis, 269 immunotherapy-associated genes in the turquoise module were selected for further analysis. Meanwhile, DEGs between the patients with complete response (CR) and those with progressive disease (PD) in the IMmotion151 cohort were identified using the “ggplot2” R package [29]. Nine immune-related genes significantly affecting the patient responsiveness to immunotherapy were ultimately identified using the intersection of the above two kinds of genes and used to construct an IRS model.
## Functional and pathway enrichment analyses
Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted using the “clusterProfiler” R package to identify the DEG-associated signaling pathways and biological processes [30, 31]. Pathways with a nominal $P \leq 0.05$ and false-discovery rate (FDR) < 0.05 were considered to be statistically significant.
## Establishment and validation of an IRS model
Given the individual heterogeneity and intricacy of the clinical outcomes of advanced RCC cases treated with ICB combined with anti-angiogenic drugs, we formulated a scoring system, termed as the IRS model, using the ssGSEA algorithm on the basis of the mRNA expression levels of the identified nine immunotherapy-related genes in a single sample to quantify the prognostic level of each patients with mRCC for in-depth analysis.
Optimal cut-off point identified by the “surv-cutpoint” function of the “survminer” R package stratified all advanced RCC cases in the IMmotion151 cohort into high- or low-risk subgroups. In this approach, different values are grouped as cut-off values for statistical testing, and the result with the lowest P value is considered as the optimal cut-off point that corresponds to the most significant association with the clinical outcome.
A heat map was constructed to visualize the IRS distribution and clinicopathological parameters. Survival analysis between high- and low-risk group were conducted using Kaplan–Meier curves with log-rank test and the “survival” R package [32]. Hazard ratios (HRs) and the corresponding $95\%$ confidence intervals (CIs) were estimated.
Area under the curve (AUC) values of the receiver operating characteristic (ROC) curves established using the “survival ROC” R package were used to evaluate the predictive efficiency of the IRS model [33]. In addition, to test the robustness of our IRS model, we verified its predictive capability using other external independent datasets: JAVELIN Renal 101, E-MTAB-3218, IMvigor210, and GSE78220 cohorts.
An identical median value based on the IMmotion151 cohort was also applied to the validation groups, effectively categorizing all patients into high- and low-risk groups.
## Correlations between IRS and common biological processes
We further determined the correlations between IRS and subsequent biological processes. Mariathasan et al. [ 25] curated multiple gene sets associated with specific biological pathways, including [1] CD8+ T-effector signature [34], [2] antigen processing machinery [35], [3] epithelial–mesenchymal transition (EMT) biomarkers [26, 36, 37], [4] immune checkpoints [25].
## Correlation between IRS and drug sensitivity
RNA-seq data of approximately 1000 tumor cell lines, AUC values for evaluating the efficacy of antineoplastic drugs in tumor cell lines, and targets or pathways of drugs were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC; https://www.cancerrxgene.org/) [38]. Spearman correlation coefficient was used to evaluate the correlation between drug sensitivity and the IRS model, and |Rs| > 0.2 and P-value < 0.05 were considered to be significant.
## Use of WGCNA to screen immunotherapy-related genes
All immune-related genes from GSEA are listed in Supplementary Table 1. After we intersected the above immune-related genes with the RNA-seq data of 407 patients with advanced RCC from the IMmotion151 cohort, 942 immune-related genes were identified and further subjected to WGCNA analysis (Supplementary Table 2). In line with the standard scale-free network distribution, the soft threshold power value was determined to be three (Supplementary Figure 1). Based on this dissimilarity, a dendrogram of all the gene clusters was formulated, which displayed 10 different modules (Figure 1A). Correlations among all clinical modules are illustrated in Figures 1B, C. We further assessed the correlations between MEs and clinical traits, including PD-L1 IHC, MSKCC risk score, PFS, objective response, metastatic status, and sarcomatoid histology. Turquoise module was most significantly associated with the objective response of patients with mRCC to atezolizumab plus bevacizumab ($r = 0.45$, $P \leq 0.0001$) (Figure 1D), indicating that genes in the turquoise module potentially exert a crucial effect on the clinical outcome of atezolizumab plus bevacizumab interventions. This turquoise module was further analyzed, and the genes in this module were found to be significantly correlated to the efficacy of atezolizumab plus bevacizumab therapy ($r = 0.76$, $P \leq 0.0001$) (Figure 1E).
**Figure 1:** *The immunotherapy-related genes are identified by WGCNA analysis. (A) Cluster dendrogram representing immune-related genes clustering based on different metrics. (B, C) Heatmap depicting the correlation coefficient in the modules. (D) Heatmap displaying the correlation between the module eigengenes and multiple clinical parameters in RCC. (E) Scatter plot exhibiting the correlation coefficient in the turquoise module. (F) Venn diagram illustrating the intersection of immune-related genes and immunotherapy-related DEGs in IMmotion150 cohort. (G) Volcano plot depicting immunotherapy-related DEGs between responders and non-responders.*
We analyzed the gene expression profiles of 23 responders (patients with CR) and 75 non-responders (patients PD) in the IMmotion151 cohort, and identified 72 DEGs associated with the effects of atezolizumab plus bevacizumab (│log2FC│ > 1, $P \leq 0.05$, FDR < 0.05) (Supplementary Table 3). Moreover, we cross-referenced the above 72 DEGs and all genes in the turquoise module to select a total of nine DEGs, including seven downregulated DEGs in responders (serine protease inhibitor Kazal type 5 [SPINK5], semaphorin 3E [SEMA3E], roundabout guidance receptor 2 [ROBO2], bone morphogenetic protein 5 [BMP5], orosomucoid 1 [ORM1], C-reactive protein [CRP], and cathepsin E [CTSE]; log2FC < 1; $P \leq 0.05$) and two up-regulated DEGs in responders (promelanin-concentrating hormone (PMCH) and C-C motif chemokine ligand 3-like 1 [CCL3L1]; log2FC > 1; $P \leq 0.05$) (Figures 1F, G).
## Biological functions of DEGs associated with the efficacy of atezolizumab plus bevacizumab therapy
We further identified the mRNA expression profiles of the above nine genes in mRCC and found that, compared with the responders to atezolizumab plus bevacizumab therapy in the IMmotion151 cohort, the expression levels of seven genes (SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, and CTSE) were significantly increased and those of two genes (PMCH and CCL3L1) were decreased in the non-responders ($P \leq 0.05$) (Figure 2A). GO analysis of the nine DEGs linked them to neutrophil-mediated immunity, leukocyte migration, acute inflammatory response, humoral immune response, and cell chemotaxis (Figure 2B; Supplementary Table 4), most of which were associated with the modulation of immunity and immunotherapy. Similarly, KEGG pathway analysis revealed that these DEGs were correlated with cytokine–cytokine receptor interactions, complement and coagulation cascades, antigen processing and presentation, and neutrophil extracellular trap formation (Figure 2C; Supplementary Table 5), indicating their significance and conferring the basis to investigate a potential association between these genes and immunophenotypes.
**Figure 2:** *The potential biological processes associated with immunotherapy-related DEGs are determined by functional analysis. (A) Bar charts representing the expression levels of immunotherapy-related DEGs between responders and non-responders. Circular plot representing the potential biological pathways related to immunotherapy-related DEGs based on (B) GO analysis and (C) KEGG analysis. * p<0.05, ** p<0.01, *** p<0.001.*
## Differences in the biological roles and clinical outcomes of IRS-high and -low groups
Based on the strength of the optimal cut-off point (–0.46), 407 individuals with mRCC were stratified into high- and low-risk groups (high: IRS > –0.46 and low: IRS < –0.46) (Supplementary Table 6). To investigate the potential mechanism of the impact of IRS on atezolizumab plus bevacizumab therapy for mRCC, a combined heat map was constructed to visually demonstrate the correlations between IRS and multiple clinicopathological parameters, including PD-L1 IHC, MSKCC risk score, objective response, metastatic status, and sarcomatoid histology, in the high and low IRS groups. We compared the high and low IRS groups in the IMmotion151 cohort and found that more patients in the IRS-low group had positive PD-L1 IHC results than those in the IRS-high group. Metastatic tumors were primarily distributed in the IRS-high cluster, whereas the proportion of sarcomatoid histological subtypes in the IRS-low group was greater than that in the IRS-high group. Additionally, patients with low IRS were primarily characterized by a higher expression of CD8+ T-effectors, antigen-processing machinery, and immune checkpoint signatures in the IRS-low group than in the IRS-high group. Conversely, patients in the IRS-high group showed relatively high expression levels of EMT-associated genes (Figure 3A). We further assessed the clinical outcomes of patients treated with atezolizumab plus bevacizumab in the IRS-high and -low groups. Survival analysis revealed that short PFS in patients with high IRS (HR = 1.91; $95\%$ CI = 1.43–2.55; $P \leq 0.0001$) (Figure 3B).
**Figure 3:** *Association between transcriptional signatures, clinical outcome to atezolizumab plus bevacizumab and the IRS model. (A) Heatmap displaying the IRS distribution grouped by transcriptional signatures. (B) Kaplan-Meier curves of PFS in mRCC patients with high or low IRS in the IMmotion151 cohort.*
Furthermore, IRS was significantly higher in the SD/PD group than in the CR/PR group (Figure 4A), indicating that IRS was negatively associated with the magnitude of response to atezolizumab plus bevacizumab in mRCC. Compared to tumors that were negative for PD-L1 IHC, tumors that were positive for PD-L1 IHC exhibited lower IRS (Figure 4B). We also observed enrichment of metastatic tumors (Figure 4C) and sarcomatoid histological subtypes (Figure 4D) in the IRS-high group. These findings suggest that the IRS can predict the efficacy of atezolizumab plus bevacizumab.
**Figure 4:** *Association between clinicopathological parameters and the IRS model. Violin plot and bar chart howing the correlation between the IRS and (A) response to Atezolizumab + Bevacizumab, (B) PD-L1 expression, (C) metastatic status and (D) histological subtype. * p<0.05, *** p<0.001, **** p<0.0001.*
## Validation of the IRS model with multiple immunotherapy datasets
ROC curve was used to determine the ability of the IRS model to distinguish between immunotherapy responders and non-responders. The IRS model displayed a satisfactory performance to differentiate responders from non-responders, with an AUC of 0.822 ($95\%$ CI = 0.782–0.863) in the IMmotion151 cohort (Figure 5A). We also selected two external independent datasets: the JAVELIN Renal 101 cohort (patients with mRCC treated with a combination of avelumab and axitinib) and the E-MTAB-3218 cohort (patients with mRCC treated with nivolumab). When assessing survival prediction, we found that the AUC of our IRS model was 0.751 ($95\%$ CI = 0.699–0.803) in the JAVELIN Renal 101 cohort (Figure 5B) and 0.776 ($95\%$ CI = 0.684–0.868) in the E-MTAB-3218 cohort (Figure 5C). Additionally, we validated our model in the IMvigor210 cohort (patients with advanced urothelial cancer who received atezolizumab therapy) and GSE78220 (patients with metastatic melanoma who received pembrolizumab therapy), with AUC of 0.902 ($95\%$ CI = 0.868–0.936) (Supplementary Figure 2A) and 0.879 ($95\%$ CI = 0.7437–1) (Supplementary Figure 2B), respectively. Therefore, our results highlight that IRS has a favorable capability to stratify a subset of patients who will benefit from immunotherapy.
**Figure 5:** *Validation of the IRS in multiple immunotherapy datasets. ROC curve displaying the predictive power of the IRS in (A) IMmotion151 cohort, (B) JAVELIN Renal 101 cohort and (C) E-MTAB-3218 cohort. Kaplan-Meier curves of PFS in tumor patients with high or low IRS in (D) JAVELIN Renal 101 cohort and (E) E-MTAB-3218 cohort.*
Patients with high IRS exhibited an inferior prognosis compared to those with low IRS in the JAVELIN Renal 101 cohort (HR = 1.77; $95\%$ CI = 1.24–2.54; $$P \leq 0.002$$) (Figure 5D) and E-MTAB-3218 cohort (HR = 4.74; $95\%$ CI = 1.31–17.2; $$P \leq 0.018$$) (Figure 5E). Similarly, patients in IRS-high group were characterized with a shorter OS than those in IRS-low group in the IMvigor210 cohort (HR = 2.59; $95\%$ CI = 1.87–3.58; $P \leq 0.001$) (Supplementary Figure 2C) and GSE78220 cohort (HR = 4.22; $95\%$ CI = 1.11–16.0; $$P \leq 0.034$$) (Supplementary Figure 2D).
## Correlation between IRS and anti-tumor chemotherapy efficacy
A total of 26 correlated pairs between the IRS model and drug sensitivity in the GDSC database were analyzed using Spearman’s correlation analysis [38]. There was significant correlation between drug sensitivity and IRS in 11 pairs, including CGP-082996, CGP-60474, and bicalutamide (Rs < –0.2, $P \leq 0.05$). In contrast, 15 pairs, including sunitinib, sorafenib, and temsirolimus (Rs > 0.2, $P \leq 0.05$), were characterized by significant correlation between drug resistance and the IRS model (Figure 6A). In addition, drugs whose sensitivity correlated with low IRS primarily targeted chromatin histone acetylation, p53 pathway, phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (MTOR) signaling and protein stability, and degradation signaling pathways. However, drugs whose sensitivity was linked to high IRS mostly targeted the AKT2, IKK2, CDK2, and chromatin histone methylation signaling pathways (Figure 6B). Collectively, these results indicate that our IRS model is also associated with chemotherapy response in RCC.
**Figure 6:** *The potential relationship between the IRS model and efficacy of antitumor chemotherapy. (A) Box diagram displaying the correlation between the IRS and drug sensitivity. Rs > 0 or Rs < 0 indicated drug resistance or drug sensitivity, respectively. (B) Dot plot summarizing the signal pathways related to drugs that were resistant or sensitive to the IRS.*
## Discussion
In our study, specific promising gene biomarkers were determined by investigating RNA-*Seq data* from the IMmotion151 cohort. Currently, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases are considered as data sources for developing RCC prognosis prediction models in the majority of publications, which fails to effectively promote the prediction accuracy of immunotherapy in RCC. Thus, we extracted mRCC cases from the IMmotion151 cohort to conduct this study, which avoided the potential effect of non-locally advanced RCC on risk prediction models and scoring systems.
We established an immune-related risk score model to evaluate the efficacy of atezolizumab plus bevacizumab in patients with mRCC and further verified our model based on multiple cohorts. Our report demonstrated that in the IMmotion151 cohort, mRCC cases with low IRS were associated with a favorable prognosis and effective responsiveness to atezolizumab plus bevacizumab. *Hub* genes significantly associated with the efficacy of atezolizumab plus bevacizumab in the turquoise module were initially identified using WGCNA. Nine immunotherapy-related DEGs were confirmed following the overlap of hub genes and DEGs between patients with PD and CR.
Among the nine immunotherapy-related DEGs (SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH, and CCL3L1), differential analysis showed that SPINK5 was significantly overexpressed in head and neck squamous cell carcinoma (HNSCC) samples compared to that in normal tissues, and SPINK5 expression levels were positively associated with Treg cells in the tumor microenvironment [39]. SEMA3E triggered macrophages-mediated inflammation [40]. Inflammation contributes greatly to tumorigenesis and tumor development [41], indicating that SEMA3E potentially accelerates tumor progression by regulating chronic inflammation, which is a hallmark of various malignancies [42]. ROBO2 belongs to the ROBO family and is a conserved transmembrane receptor protein that is primarily located in the nervous system, vascular endothelial cells, and muscle cells [43]. SLIT2/ROBO2-mediated PI3K-γ activation accelerated microglia/macrophage chemotaxis and tumor-supportive polarization, thus enhancing macrophage invasion and diminishing efficacy of chemotherapy and immunotherapy in gliomas [44]. BMP5 is recognized as a secreted growth factor and a member of the transforming growth factor-beta superfamily, which exerts crucial effects on the pathogenesis of inflammatory and autoimmune disorders, including Keshan disease [45] and autoimmune encephalomyelitis [46], BMP5 triggered keratin expression in adherent bone marrow cells, thereby contributing to the progression of chronic cutaneous neoplasms [47]. ORM1 is linked to tumor immunity, including antigen processing and presentation, T-cell receptor signaling, and cytokine-cytokine receptor interactions. Specifically, ORM1 potentially acts as an inhibitory factor to protect tumor cells from attack by the immune system, thereby leading to the immune escape of tumors [48]. CRP is a biomarker of systemic inflammation and can be generated by RCC cells [49]. Increased CRP levels are linked to the infiltration of immunosuppressive cells, including regulatory T (Treg) cells and tumor-associated macrophages, and thus predict undesirable outcomes in patients (50–52). CTSE is associated with lipid metabolism. CTSE participates in antigen processing and modulates the processing of antigenic peptides during MHC class II-mediated antigen presentation [53]. Some studies have demonstrated that CTSE is overexpressed in tumor tissues than in normal tissues in various types of cancer, such as bladder cancer [53], pancreatic cancer [54], and hepatocellular carcinoma [55]. PMCH functions as a neuromodulator of neuronal function that regulates goal-directed behavior [56]. Downregulation of PMCH in ccRCC is significantly associated with advanced TNM stage, distant metastasis, and undesirable outcomes [57]. CCL3L1 belongs to the CC chemokine family, which exerts an anti-tumor effect by inducing multiple immune cells, including CD8+ T cells and immature dendritic cells [58]. However, CCL3L1 overexpression is also involved in the progression of glioblastoma [59]. Therefore, the characteristics of CCL3L1 in RCC should be further explored. Thus, the correlation between certain immunotherapy-related DEGs and immunotherapy may provide promising targets for immune checkpoint inhibitors in RCC treatment.
Notably, these genes were primarily associated with inflammation and immune responses. Inflammation is a well-known hallmark of tumor progression. Various inflammatory signaling cascades are closely related to tumorigenesis and the development of RCC, particularly the VHL [60], mTOR, tumor necrosis factor (TNF), and signal transducer and activator of transcription pathways (60–63). Additionally, inflammation-associated factors, TNF-α, CXCR4 and CCR3, are significantly correlated with the prognosis and staging of RCC cases [64]. Inhibition of pro-inflammatory pathways may be an effective strategy to retard the development of RCC. For example, LY294002, which targets the PI3K/AKT pathway, is potentially conducive to the prognosis of patients with RCC [65]. Immunotherapy with nivolumab combined with ipilimumab has great potential for the treatment of RCC [66].
It has been demonstrated that kidney stone disease (KSD) is linked to RCC and RCC is more frequent among individuals with kidney stones (67–70). KSD is primarily composed of monohydrate (COM) crystals [71]. COM crystals triggers renal cell injury through inducing reactive oxygen species overproduction and accelerating oxidative DNA damage [71, 72]. Oxidative DNA damage exerts crucial effects on inflammation and the initiation and development of RCC [73, 74].
A recent study demonstrated that COM crystals accelerate the process of EMT, strengthen the invasion ability, cell-aggregate formation, chemoresistance to cisplatin, and secretion of VEGF, and trigger the overexpression of oncogene TPX2 and the downregulation of tumor suppressor genes, including PTEN, VHL, and ARID1A, which are conventional inflammation-associated factors, ultimately exhibiting several carcinogenic characteristics in non-cancerous renal cells [70]. Thus, there is a potential and sophisticated crosstalk between KSD, RCC, and inflammation.
The validation in additional four dependent cohorts demonstrated that the risk model exhibited satisfactory and robust prediction efficiency. The diagnosis and treatment of individuals with tumors will benefit from the validity and rationality of constructing a model based on big data algorithms. A risk model incorporating nine genes has generally been studied for multiple tumors; however, there are no reports on immunotherapy-related risk models for RCC. Patients with mRCC with low-risk scores showed improved PFS and could benefit from the dual combination of nivolumab plus ipilimumab, as evaluated by the IRS model, whereas cases in the high-risk group displayed numerically inferior results for PFS with nivolumab + ipilimumab. In this study, patients with mRCC with low risk scores were enriched in the CD8+ T effector, antigen processing machinery, and immune checkpoint pathways. In contrast, patients with high-risk scores displayed greater expression of EMT-related genes. These results provide a molecular explanation for the better prognosis of favorable-risk cases with therapeutic regimens comprising nivolumab + ipilimumab. Previous studies have shown that ICIs block inhibitory immune receptors and activate dysfunctional T cells, including CD8+ T cells. CD8+ T effectors in the adaptive immune system exert a potent anti-tumor immune response and form the cornerstone of tumor immunotherapy [75]. The antigen-processing machinery exerts a vital impact on the synthesis and expression of HLA class I tumor antigen-derived peptide complexes that trigger the identification and elimination of malignant cells mediated via cognate T cells [76].
We performed a thorough molecular analysis of 407 samples from patients with advanced RCC who underwent atezolizumab plus bevacizumab therapy and further established the first prognostic model to accurately distinguish responders from non-responders based on a randomized global Phase III clinical trial IMmotion151 cohort. Specifically, patient-reported outcomes (PROs) in IMmotion151 suggest a lower overall treatment burden with atezolizumab plus bevacizumab than with sunitinib in patients with treatment-naïve mRCC and provide further evidence for the clinical benefit of this regimen. A report evaluated PROs in the phase III IMmotion151 trial and demonstrated that compared with sunitinib in patients with mRCC, those receiving atezolizumab plus bevacizumab therapy were characterized by a lower overall therapy burden, including longitudinal and time to deterioration for core and RCC symptoms and their interference with daily life, therapy side effects, and health-related quality of life [77]. Another study indicated that although a clinical benefit was revealed in atezolizumab plus bevacizumab based on PFS analysis, the final analysis exhibited a similar median OS in patients treated with atezolizumab plus bevacizumab and sunitinib. Biomarker analysis demonstrated that sunitinib improved the median OS in patients whose tumors were characterized by a higher prevalence of angiogenesis; conversely, atezolizumab plus bevacizumab displayed a trend of improved OS in tumors with poor angiogenesis, but T-effector/proliferative, proliferative, or small nucleolar RNA transcriptomic profiles. These results potentially provide guidance for the individualized treatment of patients with mRCC [78].
This study has some limitations. Owing to the limited number of patients receiving immunotherapy and the complexity and difficulty in collecting clinical tissues from patients with advanced RCC treated with immunotherapy, we failed to conduct external verification based on our own dataset. Nevertheless, we validated our IRS model using four additional public immunotherapy cohorts to overcome this disadvantage. Moreover, our IRS model comprised nine immunotherapy-related DEGs. The biological properties and potential molecular mechanisms of these genes in mRCC need to be explored to facilitate the widespread clinical application of IRS models.
## Conclusion
In conclusion, we identified the most significant immunotherapy-associated genes in patients with advanced RCC from the IMmotion151 cohort and developed a novel and promising immunotherapy prediction model and scoring system to estimate the responsiveness of patients with advanced RCC to atezolizumab plus bevacizumab therapy. Our model can further aid in patient stratification and development of personalized therapies for patients with untreated advanced RCC.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
Five datasets analyzed in our report merely contain anonymized and de-identified patient information. Secondary analysis of de-identified data was confirmed exempt from review by the medical ethics committee of Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine as it was classified as negligible risk research. Thus, our study was exempt from the ethical review or the patient consent.
## Author contributions
XY and PC designed/planned the study and wrote the paper. PC performed computational modeling, acquired, and analyzed data. PC, FB, WT, and LJ performed imaging analysis. PC, FB, WT, LJ, and XY participated in discussion of related data. XY and PC drafted the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1127448/full#supplementary-material
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|
---
title: Change of metformin concentrations in the liver as a pharmacological target
site of metformin after long-term combined treatment with ginseng berry extract
authors:
- Choong Whan Lee
- Byoung Hoon You
- Sreymom Yim
- Seung Yon Han
- Hee-Sung Chae
- Mingoo Bae
- Seo-Yeon Kim
- Jeong-Eun Yu
- Jieun Jung
- Piseth Nhoek
- Hojun Kim
- Han Seok Choi
- Young-Won Chin
- Hyun Woo Kim
- Young Hee Choi
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10043734
doi: 10.3389/fphar.2023.1148155
license: CC BY 4.0
---
# Change of metformin concentrations in the liver as a pharmacological target site of metformin after long-term combined treatment with ginseng berry extract
## Abstract
Metformin as an oral glucose-lowering drug is used to treat type 2 diabetic mellitus. Considering the relatively high incidence of cardiovascular complications and other metabolic diseases in diabetic mellitus patients, a combination of metformin plus herbal supplements is a preferrable way to improve the therapeutic outcomes of metformin. Ginseng berry, the fruit of Panax ginseng Meyer, has investigated as a candidate in metformin combination mainly due to its anti-hyperglycemic, anti-hyperlipidemic, anti-obesity, anti-hepatic steatosis and anti-inflammatory effects. Moreover, the pharmacokinetic interaction of metformin via OCTs and MATEs leads to changes in the efficacy and/or toxicity of metformin. Thus, we assessed how ginseng berry extract (GB) affects metformin pharmacokinetics in mice, specially focusing on the effect of the treatment period (i.e., 1-day and 28-day) of GB on metformin pharmacokinetics. In 1-day and 28-day co-treatment of metformin and GB, GB did not affect renal excretion as a main elimination route of metformin and GB therefore did not change the systemic exposure of metformin. Interestingly, 28-day co-treatment of GB increased metformin concentration in the livers (i.e., 37.3, $59.3\%$ and $60.9\%$ increases versus 1-day metformin, 1-day metformin plus GB and 28-day metformin groups, respectively). This was probably due to the increased metformin uptake via OCT1 and decreased metformin biliary excretion via MATE1 in the livers. These results suggest that co-treatment of GB for 28 days (i.e., long-term combined treatment of GB) enhanced metformin concentration in the liver as a pharmacological target tissue of metformin. However, GB showed a negligible impact on the systemic exposure of metformin in relation to its toxicity (i.e., renal and plasma concentrations of metformin).
## 1 Introduction
The combination therapy of anti-diabetic drug with herbal products (i.e., complementary and alternative medicines) is practiced in over $30\%$ of diabetic patients in the United States and more common in Asia (Wang et al., 2013; Gupta et al., 2017). Clinically, the use of a combination of antidiabetic drug with herbal medicines has already been proven to improve therapeutic outcomes. Reports and researches to evaluate the efficacy and/or safety for the drug combination with herbal products have been increasing (Wang et al., 2013; Wang et al., 2018; Yang et al., 2019). Moreover, the pharmacokinetic evaluation of herb-drug interaction (HDI), which is well-known among drug interactions, identifies how the herb changes efficacy and/or toxicity of a drug (Chen et al., 2012; Choi, 2020; Choi and Chin, 2020). The combined treatment of the herb can affect the circulating level and specific target tissue level of a drug, therefore altering the clinical outcome of the drug (Chen et al., 2012). To prevent unexpected risk or potential of HDIs, in vitro and in vivo models have been regularly adjusted and recommended to use in the evaluation of pharmacokinetic HDIs (Iwatsubo, 2020; Sudsakorn et al., 2020). Considering that many herbs are orally administered with a chronic regimen, the HDI evaluation is focused on their interactions in the intestine, liver, kidneys and pharmacological target organs (Chen et al., 2012; Choi, 2020; Choi and Chin, 2020).
Metformin, an oral glucose-lowering drug, is commonly used for the treatment of type 2 diabetic mellitus (Choudhary et al., 2012). Despite the relative safety and popular utilization of metformin for a long time, it has still been identified that mechanism of actions of metformin targeting multiple pharmacological sites in the body (Hermann et al., 1994; El-Mir et al., 2000; Madiraju et al., 2014). Individual variations of diabetic patients’ responses during metformin treatment have been sometimes observed (Hermann et al., 1994), and patients with diabetic mellitus shows the increased risks of cardiovascular complications and other metabolic diseases. Thus, combinations of metformin with herbal products are popularly conducted, and drug interactions of metformin with herbal products have been occasionally occurred (Wang et al., 2018; Yang et al., 2019). In terms of pharmacokinetics, metformin is well absorbed, and extensively distributed to the liver as its pharmacological target tissue and kidneys as its main elimination organ (Stage et al., 2015). In other words, metformin extensively distributed to liver, and reduces glucose production in the livers. Metformin is also considerably distributed to the kidneys and excreted into urine, which supports the idea that the renal excretion of metformin highly determines its systemic exposure and toxicity (e.g., lactic acidosis) (Gong et al., 2012). Thus, it is important to understand the transporter-mediated metformin disposition [i.e., organic cation transporters (OCTs) and multidrug and toxin extrusions (MATEs)] in livers and kidneys to elucidate the efficacy and toxicity of metformin, which has attracted attention in understanding metformin pharmacokinetics and pharmacodynamics (Sundelin et al., 2020; Yang et al., 2021).
Ginseng berry, i.e., the fruit of Panax ginseng Meyer, has been shown to ameliorate hyperglycemia, hyperlipidemia, obesity, hepatic steatosis and inflammation, mainly due to the pharmacological activities of the ginsenosides in ginseng berry (Dey et al., 2003; Zhang et al., 2016; Chae et al., 2019). Choi et al. [ 2018] proved that the combinated ginseng berry extract (GB) improved metformin’s glucose lowering effect without any adverse effect in clinical study, which accelerating the combination therapy of GB and metformin. Chae et al. [ 2019] reported that GB enhanced metformin efficacy against obesity and hepatic steatosis through AMPK activation. Therefore, as an extended follow-up study, the present work assesses how GB affects OCTs and/or MATEs-mediated changes on metformin pharmacokinetics. The treatment period effect of GB and metformin was specially evaluated in mice, because the combination of metformin and GB has the possibility to be applied in long-term treatments.
## 2.1 Chemicals and reagents
GB from 4-year-old Korean ginseng berries (P. ginseng) was provided from Amorepacific Corporation (Gyeonggi-do, Republic of Korea) (Chae et al., 2019). Metformin hydrochloride, carbamazepine [Internal standard (IS) for the analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS)], and cimetidine were purchased from Sigma-Aldrich Chemical Company (St. Louis, MO, United States. HEK293 cells overexpressing hOCT1 (SLC22A1), hOCT2 (SLC22A2), hMATE1 (SLC47A1) and hMATE2-K (SLC47A2) were purchased from Corning Life Sciences (Corning, NY, United States). Trizol and SYBR green supermix were purchased from the Molecular Research Center (Cincinnati, OH, United States) and TAKARA (Kusatsu, Shiga, Japan), respectively. All other chemicals and reagents were categorized as analytical grade.
## 2.2 Effect of GB on metformin uptake in HEK293-cells overexpressing hOCT1, hOCT2, hMATE1, or hMATE2-K
The effect of GB on metformin uptake was assessed using slightly modified methods (You et al., 2018). HEK293 cells overexpressing hOCT1, hOCT2, hMATE1 or hMATE2-K were seeded in 24-well plates coated with poly-D-lysine at a density of 4.0 × 105 (cells/well and incubated for 24 h with Dulbecco’s modified eagle medium (DMEM) plus $10\%$ fetal bovine serum (FBS). Cells were maintained at 37°C in humidified atmosphere $8\%$ CO2. For cells overexpressing hMATE1 and hMATE2-K, DMEM supplemented with $10\%$ FBS and 2 mM sodium butyrate was used instead of DMEM with $10\%$ FBS. After washing the cells twice with prewarmed Hank’s balanced salt solution (Hank’s solution) with Ca2+ and Mg2+, the cells were preincubated with Hank’s solution for 10 min. To set the driving force of hMATE1 and hMATE2-K, the following step was conducted after preincubation: rewarmed Hank’s solution containing 40 mM ammonium chloride was added into each well and incubated at 37°C for 20 min, after which prewarmed Hank’s solution containing 40 mM ammonium chloride was washed out. This step was not required for the hOCT1 and hOCT2 assays. Then, metformin uptake was also initiated through replacement with Hank’s solution containing metformin with and without GB. For this purpose, 10 μM of metformin and 5 or 500 μg/mL of GB were used. A 100, 100, 1 and 10 μM of cimetidine was adjusted for a well-known inhibitor of hOCT1, hOCT2, hMATE1 and hMATE2-K, respectively, rather than GB. In choosing concentrations of cimetidine and metformin, the respective concentration of cimetidine for each transporter was determined considering the IC50 value for each transporter reported by Jin et al. [ 2019]. The metformin concentrations were based on previous work by You et al. [ 2018]; Jin et al. [ 2019]. After 10 min of incubation, Hank’s solution was removed and the cells were immediately washed with ice-cold pure Hank’s solution. The cells were lysed with distilled water and harvested by scraping them off in 200 μL of distilled water, after which they were ultrasonicated for 10 s at 4°C. The samples were centrifuged at 12,000 rpm and 4°C for 10 min, and then the supernatant was stored at −80°C until the LC-MS/MS analysis of metformin (You et al., 2018).
## 2.3 Animal treatment
All protocols used in the animal studies were approved by the Institute of Laboratory Animal Resources of Dongguk University_Seoul, Republic of Korea (Approval number: IACUC-2015-044). Male ICR mice (5-weeks-old, weight 20–25 g) were purchased from the Charles River Company (Seoul, Republic of Korea). Upon arrival, all mice were randomized and housed three per cage under strictly controlled conditions (22°C–25°C and $48\%$–$52\%$ relative humidity) with a 12 h light/dark cycle at an intensity of 150–300 Lux. All mice had free accessed to food and water. They were allowed to acclimate for a week prior to inclusion in the experiments.
The mice were randomly assigned into four groups (i.e., the 1 M, 1 MGB, 28 M and 28 MGB groups) depending on the treatment period (i.e., 1-day and 28-day).
## 2.4 Effect of GB on pharmacokinetics of metformin
In the pre-treatment period (i.e., 27 days), distilled water as vehicle as a vehicle was orally administered to the 1 M and 1 MGB groups for each of the 27 days. Meanwhile, 50 mg/kg metformin (dissolved in distilled water) and 50 mg/kg metformin with 200 mg/kg GB (dissolved in distilled water) were orally administered to the 28 M and 28 MGB groups, respectively, for each of the 27 days. The pharmacokinetic study was conducted at the 28th day after beginning the treatment.
The pharmacokinetic study was conducted by modifying a previously reported method (You et al., 2021). Beginning 8 hrs before the start of experiment, mice were fasted overnight while still having free access to water. On the experiment day (i.e., on the 28th day), metformin with or without GB was orally administered by oral gavage as follows: 50 mg/kg metformin was orally administered to the 1 M ($$n = 24$$ for 7 profile set) and 28M ($$n = 28$$ for 8 profile set) groups, while 50 mg/kg metformin with 200 mg/kg GB was simultaneously administered to the 1 MGB ($$n = 24$$ for 7 profile set) and 28 MGB ($$n = 27$$ for 8 profile set) groups. Anesthesia was conducted by intraperitoneal (i.p.) injection of 0.05 mL per kg composing 3:1 mixture of zoletil (i.e., tiletamine 125 mg + zolazepam 125 mg in 5 mL) and rompun (xylazine HCl 23.3 mg in 5 mL) before heart puncture. In heart puncture, 31-gauge needle was used to minimize damage to cardiac and pericardial tissues along the needle track and to keep mice alive for several blood collections. The heart puncture was also conducted within the recommended guideline and the approved protocol as followings: a 0.15 mL of one-time blood sampling volume (Florida Atlantic University Institutional Animal Care and Use Committee, 2021), a 1.8 mL of blood sampling as total volume when multiple blood sampling (Diehl et al., 2001), and approximately three times of heart puncture (Golde et al., 2005) in mouse with 20–25 g body weight are recommended. An approximately 120 µL of blood sample was collected via heart puncture at 0 (to serve as a control), 5, 15, 30, 60, 120, 180, 240, 360 or 480 min after oral administration of each drug. As three blood samples were obtained per mouse and 24–28 mice were used in total, seven or eight sets of pharmacokinetic data were produced in each group. Insulin syringes coated with heparin (20,000 IU/20 mL) were used for blood sampling. Blood samples were immediately centrifuged at 9,000 rpm and 4°C for 1 min, and a 50 µL aliquot of plasma was collected from the supernatant of each blood sample. At the end of the experiment (24 h), each metabolic cage was rinsed with 5 mL of distilled water, and the rinsing was combined with the 24-h urine sample. After the exact volume of the combined urine sample had been measured, a 50 µL aliquot of the combined urine sample was collected from each mouse. At this time, each mouse was sacrificed by cervical dislocation. The abdomen was opened, and then the entire gastrointestinal tract (GI, including its contents and feces) was removed and transferred into a beaker. The GI was cut into small pieces and 20 mL of methanol was added. After stirring with a glass rod for 1 min, 50 µL of the supernatant was collected from each beaker. All biological samples of plasma, urine and GI were stored at −70°C until being used for the LC-MS/MS analysis of metformin (You et al., 2018).
## 2.5 Effect of GB on metformin distribution to liver and kidneys
In the same way as the pharmacokinetic study, the pre-treatment of metformin with and without GB were administered to the 1 M, 1 MGB, 28 M and 28 MGB groups. On the experiment day (on the 28th day), 50 mg/kg metformin was orally administered to the 1 M and 28 M groups whereas 50 mg/kg metformin with 200 mg/kg GB was orally administered to the 1 MGB and 28 MGB groups. At 1 or 4 h after oral administration, whole blood was collected into the abdominal aorta under the anesthesia, and then $0.9\%$ NaCl-solution was sufficiently perfused through the hepatic portal vein to remove blood form the tissues and to measure the metformin concentration in tissues accurately (Hu et al., 2021; You et al., 2021). Mice were sacrificed by loss of blood and cervical dislocation. After centrifugation of the collected blood, 50 µL of plasma was collected from each blood sample. Liver and kidneys were excised, and approximately 1 g of each tissue was weighted. A 4-fold volume of normal saline was added to each tissue, which was homogenized and centrifuged at 12,000 rpm and 4°C for 10 min. Then, 50 µL of the supernatant of each tissue was collected. All collected samples were stored at −80°C until LC-MS/MS analysis of metformin (You et al., 2018).
## 2.6 Effect of GB on mRNA levels of OCTs and MATEs in liver and kidneys
Control, 1 M, 1 MGB, 28 M and 28 MGB groups were assessed to measure the mRNA levels of OCT1, OCT2, MATE1 and MATE2-K in liver and kidneys. Distilled water as vehicle was orally administered to the control group for 28 days. In the other groups, the treatment method was the same as that described in the pharmacokinetic study.
After excising liver and kidneys, total cellular RNA was isolated using a Trizol RNA extraction kit (Thermo Fisher Scientific, Waltham, USA) according to the manufacturer’s instructions. Briefly, total RNA (1 μg) was converted to cDNA using 200 units of reverse transcriptase and 500 ng oligo-dT primers in 50 mM Tris-HCl (pH 8.3), 75 mM KCl, 3 mM MgCl2, 10 mM dithiothreitol, and 1 mM dNTPs at 42°C for 1 h. The reaction was then stopped by incubating the reaction at 70°C for 15 min, after which 1 µL cDNA mixture was used as template for PCR amplification. PCR reactions were performed using 1 μL cDNA and 9 μL master mix containing iQ SYBR Green Supermix (Bio-Rad), 5 pmol of forward primer, and 5 pmol reverse primer, in a CFX384 Real-Time PCR Detection System (Bio-Rad). The reaction conditions were 3 min at 95°C followed by 40 cycles of 10 s at 95°C and 30 s at 55°C, after which the plate was read. Fluorescence signal generated with SYBR Green I DNA dye were measured during annealing steps. The specificity of the amplification was confirmed using a melting curve analysis. Data were collected and recorded by CFX Manager Software (Bio-Rad) and expressed as a function of the threshold cycle (CT). The relative quantity of the gene of interest was then normalized to the relative quantity of GAPDH (ΔΔCT). The mRNA abundance in the sample was calculated using equation 2-(ΔΔCT). The following specific primer sets were used (5′to 3′): mouse–β-actin: TCAAGATCATTGCTCCTCCTG (forward), GCTCAGTAACAGTCCGCCTAG (reverse); mouse–Oct1: TGGAGCAAATTGCACAGAAG (forward), GGTCTGCAAAC-GAAGGACTC (reverse); mouse–Oct2: AAATGGTCTGCCTGGTCAAC (forward), TCCAGCCAGATGTCAGTGAG (reverse); mouse–Mate1: TCCTTCCTGCAACTGGCTAT (forward), ACTCCACCATGCCAAGGATA (reverse); mouse–Mate2: AGCTGGGCTAAAAAGCAACA (forward), CCAGTCTGGCTCTCTGGTCT (reverse). Gene specific primers were custom-synthesized by Bioneer (Daejeon, Republic of Korea).
## 2.7 Effect of GB on plasma protein binding of metformin
The plasma protein binding values of metformin with or without GB were measured using a Rapid Equilibrium Dialysis (RED) system (Thermo Fisher Scientific, Rockford, IL, USA). To begin, 300 µL of buffer solution containing 100 mM sodium phosphate and 150 mM sodium chloride, pH 7, was inserted into one chamber of the RED well plate. Meanwhile, 100 µL of mouse plasma containing 1 μg/mL of metformin was inserted into another chamber of the RED well plate. The well plate was then covered with plastic and incubated at 37°C and 50 rpm for 4 h. To measure the effect of GB on the plasma protein binding of metformin, 100 µL of mouse plasma containing 1 μg/mL of metformin with 1 or 100 μg/mL of GB was used. After 4 h incubation, a 50 µL aliquot from each chamber was transferred into a 1.5 mL tube, at which point 500 µL of acetonitrile containing IS (20 ng/mL of carbamazepine) was added into each tube. After being vortexed and centrifugated at 12,000 rpm and 4°C for 10 min, a 5 µL aliquot of the supernatant was injected into the LC-MS/MS system for the analysis of metformin (You et al., 2018). Because metformin was categorized as a positive drug with a low plasma protein binding (Gong et al., 2012; You et al., 2018), 1 μg/mL of donepezil was additionally used as a positive compound as a high plasma protein binding drug (Seltzer, 2005).
## 2.8 LC-MS/MS analysis of metformin and ginsenosides in GB
Metformin was analyzed according to a previously reported method (You et al., 2018). Briefly, 100 μL of acetonitrile containing IS (20 ng/mL carbamazepine) was added to 50 μL of each biological sample. After vortexing and centrifuging a sample at 12,000 rpm for 10 min, 10 µL of the supernatant was analyzed using the LC-MS/MS system. The analytes were monitored using a API4000 triple quadrupole mass spectrometer (AB Sciex, Foster City, CA) equipped with a turbo ion spray interface for electrospray ionization and operated in positive ion mode at 5.5 KV and 500°C. The mass transitions for metformin and IS were m/z 130.1 → 71.0 (collision energy, 31 eV) and 237.2 → 194.1 (25 eV), respectively, in the multiple reaction monitoring (MRM) mode. In the LC system (Thermo Fisher scientific, Seoul, Republic of Korea), the reversed-phase C18 column (X-bridge C18, 2.1 mm × 100 mm i.d., 3 μm; waters, Ireland) was kept at 4°C. The gradient mobile phase composition was changed from $100\%$ of $0.1\%$ formic acid in water to $100\%$ of acetonitrile for 1.5 min, then switched to $100\%$ of $0.1\%$ formic acid in water for 3.5 min and maintained for 5.5 min at the flow rate of 0.3 mL/min. The retention times of metformin and IS were 1.10 min and 4.08 min, respectively. The detection limit of metformin was 5 ng/mL.
## 2.9 Pharmacokinetic parameters
Standard methods (Gibaldi and Perrier, 1982) were used to calculate the following pharmacokinetic parameters by using non-compartmental analysis (WinNonlin; version 2.1; Scientific Consulting): maximum observed plasma concentration (Cmax), time of maximum observed plasma concentration (Tmax), area under the plasma concentration-time curve from the time of dosing extrapolated to infinity (AUC), renal clearance (CLR) and terminal half-life (t$\frac{1}{2}$).
## 2.10 Statistical analysis
A p-value < 0.05 was deemed to be statistically significant using a Student’s t-test between the two means for the unpaired data or a Tukeyʼs multiple range test of the Social Package of Statistical Sciences (SPSS) posteriori analysis of variance (ANOVA) among the three, four or five means for the unpaired data. The pharmacokinetic parameters were expressed as means ± standard deviations except for Tmax, which was expressed as median (ranges).
## 2.11 Biochemistry analysis
A 24 h urine sample was collected to measure urine output and creatinine (Cr) levels to measure kidney function in the 1 M, 1 MGB, 28 M and 28 MGB groups ($$n = 5$$ for each group). Blood was collected to measure total protein, albumin, urea nitrogen, glutamate oxaloacetate transaminase, glutamate pyruvate transaminase and creatinine concentration in plasma. Blood samples were analyzed by Green Cross Reference Lab (Seoul, Republic of Korea).
## 3.1 Effect of GB on metformin uptake in HEK293 cells overexpressing hOCT1, hOCT2, hMATE1 and hMATE2-K
As shown in Figure 1, metformin uptake in HEK293 cells overexpressing hOCT2, hMATE1 and hMATE2-K was significantly reduced (by 24.1, 61.8, and $37.6\%$, respectively) by 500 μg/mL of GB. However, 5 μg/mL of GB did not change metformin uptake in any of the groups.
**FIGURE 1:** *.Metformin uptake in HEK293 cells overexpressing (A) hOCT1, (B) hOCT2, (C) hMATE1, and (D) hMATE2-K. Cimetidine at 100, 100, 1 and 10 μM was used as a well-known of OCT1, OCT2, MATE1 and MATE2-K, respectively. aMC group was significantly different (p < 0.05) from other groups. bMGB500 and MC groups were significantly different (p < 0.05) from M and MGB5 groups. cMGB500 and MC groups were significantly different (p < 0.05) from M and MGB5 groups, and also there was a significant difference (p < 0.05) between MC and MGB500 groups.*
As a well-known inhibitor against each transporter, cimetidine significantly reduced metformin uptake in HEK293 cells overexpressing hOCT1, hOCT2, hMATE1 and hMATE2-K (by $72.9\%$, $46.8\%$, $94.5\%$ and $96.2\%$, respectively).
## 3.2 Effect of GB on metformin pharmacokinetics
The mean arterial plasma concentration-time profile of metformin after its oral administration with and without GB is shown in Figure 2, and some relevant pharmacokinetics parameters are listed in Table 1. There was no change on any pharmacokinetic parameters of metformin among the four groups.
**FIGURE 2:** *The mean plasma concentration-time profiles of metformin after oral administration of metformin with (●; 1 MGB and 28 MGB) and without GB (○; 1 M and 28 M) to mice, respectively. The doses of metformin and GB were 50 and 200 mg/kg, respectively. Treatment periods were 1-day (A) and consecutive 28-day (B), respectively.* TABLE_PLACEHOLDER:TABLE 1
## 3.3 Effect of GB on metformin distribution in the livers and kidneys
Metformin concentrations in plasma, livers and kidneys, along with their T/P ratios at 1 and 4 h, are shown in Table 2. In the livers, metformin concentration in the livers of 28 MG B group at 4 h was significantly higher (by 37.3, $59.3\%$ and $60.9\%$ versus 1 M, 1 MGB and 28 M, respectively) than other three groups. There was no difference on liver concentrations of metformin at 1 h among the four groups. The T/P ratios of metformin at 1 and 4 h in the livers also were changed among the four groups.
**TABLE 2**
| Unnamed: 0 | Unnamed: 1 | 1 M (n = 5) | 1 MGB (n = 5) | 28 M (n = 5) | 28 MGB (n = 5) |
| --- | --- | --- | --- | --- | --- |
| Plasma | Plasma | Plasma | Plasma | Plasma | Plasma |
| 1 h | Concentration (μg/mL) | 3.43 ± 0.230 | 3.48 ± 0.625 | 3.52 ± 0.624 | 3.01 ± 0.597 |
| 4 h | Concentration (μg/mL) | 0.591 ± 0.132 | 0.506 ± 0.191 | 0.501 ± 0.180 | 0.502 ± 0.0979 |
| Livers | Livers | Livers | Livers | Livers | Livers |
| 1 h | Concentration (μg/g liver) | 16.9 ± 4.96 | 15.3 ± 4.93 | 14.6 ± 4.06 | 19.8 ± 2.40 |
| 1 h | T/P | (4.96 ± 1.51) | (4.68 ± 2.50) | (4.39 ± 1.96) | (6.73 ± 1.06) |
| 4 h | Concentration (μg/g liver) a | 5.79 ± 1.15 | 4.99 ± 0.765 | 4.94 ± 0.892 | 7.95 ± 1.22 |
| 4 h | T/P | (10.4 ± 4.39) | (11.9 ± 7.15) | (10.8 ± 3.72) | (16.3 ± 3.57) |
| Kidneys | Kidneys | Kidneys | Kidneys | Kidneys | Kidneys |
| 1 h | Concentration (μg/g kidney) | 33.9 ± 15.1 | 28.4 ± 6.94 | 29.4 ± 7.68 | 22.3 ± 2.16 |
| 1 h | T/P | (9.87 ± 4.17) | (8.16 ± 1.31) | (8.37 ± 1.67) | (7.68 ± 1.82) |
| 4 h | Concentration (μg/g kidney) | 16.4 ± 0.818 | 15.2 ± 1.21 | 12.2 ± 2.23 | 11.9 ± 2.04 |
| 4 h | T/P | (28.9 ± 6.56) | (35.6 ± 19.2) | (25.6 ± 4.56) | (24.8 ± 8.07) |
In the kidneys, there were no difference of metformin concentrations and T/P ratios among the four groups.
## 3.4 Effects of GB on mRNA levels of OCT1, OCT2, MATE1 and MATE2-K in liver and kidney
The mRNA levels of OCT1, OCT2, MATE1 and MATE2-K in each of the liver and kidneys were measured in the control, 1 M, 1 MGB, 28 M and 28 MGB groups, and the results are shown in Figure 3. In the livers, the mRNA level of OCT1 in 28 MGB group was significantly increased (by $55.4\%$, $58.4\%$, $96.5\%$, and $44.6\%$) compared to control, 1 M, 1 MGB, and 28 M, respectively. On the other hands, the mRNA level of MATE2-K in 28 MGB group was significantly decreased (by $66.0\%$, $66.0\%$, $56.9\%$, and $61.6\%$) compared to control, 1 M, 1 MGB, and 28 M, respectively. There was no difference of mRNA level of MATE2-K in the livers among the four groups, and the mRNA level of OCT2 was not detected in the livers of any groups.
**FIGURE 3:** *Relative mRNA expressions of OCT1, OCT2, MATE1 and MATE2-K in (A) livers and (B) kidneys of control, 1 M, 1 MGB, 28 M and 28 MGB groups, respectively. a28 MGB was significantly different (p < 0.05) from other groups.*
In the kidneys, no changes were observed in the mRNA levels of any transporters in any groups.
## 3.5 Effect of GB on plasma protein binding of metformin
The plasma protein binding values of metformin with or without GB were as follows: $15.9\%$ ± $6.54\%$, $14.3\%$ ± $5.47\%$ and $17.3\%$ ± $4.17\%$ with metformin alone, metformin with 1 μg/mL GB, and metformin with 100 μg/mL GB, respectively ($$n = 3$$ for each group). There were no significant difference between any of these values. For validation, the plasma protein binding value of 1 μg/mL donepezil as a high plasma protein binding drug was $81.2\%$, which was similar as the previously reported value (Seltzer, 2005).
## 3.6 Effect of GB on livers and kidney functions
The body weight, biochemical profiles, and parameters for liver and kidney function for the 1 M, 1 MGB, 28 M and 28 MGB groups are presented in Table 3. In all groups, all biochemical profiles and tissue weights (i.e., livers and kidney) were within normal ranges according to the reference values from Mitruka and Rawnsley [1981].
**TABLE 3**
| Unnamed: 0 | 1 M (n = 5) | 1 MGB (n = 5) | 28 M (n = 5) | 28 MGB (n = 5) |
| --- | --- | --- | --- | --- |
| Body weight (g) | 23.3 ± 5.26 | 24.5 ± 4.95 | 24.6 ± 3.48 | 25.4 ± 3.79 |
| Plasma | Plasma | Plasma | Plasma | Plasma |
| Total protein (g/dL) | 5.10 ± 0.64 | 4.95 ± 0.84 | 5.15 ± 0.48 | 4.90 ± 0.78 |
| Albumin (g/dL) | 3.64 ± 0.45 | 3.82 ± 0.548 | 3.90 ± 0.51 | 3.75 ± 0.31 |
| Glucose (mg/dL) | 260 ± 49.2 | 253 ± 21.7 | 261 ± 31.8 | 249 ± 52.7 |
| AST (IU/L) | 78.3 ± 6.48 | 80.3 ± 10.2 | 76.2 ± 4.85 | 79.3 ± 7.11 |
| ALT (IU/L) | 26.5 ± 5.12 | 27.0 ± 3.99 | 25.8 ± 5.47 | 26.9 ± 2.18 |
| Urea nitrogen (mg/dL) | 43.5 ± 9.12 | 42.3 ± 3.48 | 44.3 ± 3.88 | 45.1 ± 5.71 |
| Creatinine (mg/dL) | 0.51 ± 0.026 | 0.50 ± 0.031 | 0.53 ± 0.019 | 0.52 ± 0.040 |
| Liver weight (% of BW) | 2.83 ± 0.12 | 2.69 ± 0.31 | 2.70 ± 0.25 | 2.72 ± 0.19 |
| Kidney weight (% of BW) | 0.97 ± 0.11 | 0.98 ± 0.084 | 0.99 ± 0.057 | 0.98 ± 0.061 |
## 4 Discussion
In drug interactions (e.g., drug-drug and drug-herb interactions), treatment period is a critical factor to influence metabolic enzymes and/or transporters, which affects the pharmacokinetic profile of a victim drug (Choi, 2020; Choi and Chin, 2020; Iwatsubo, 2020; Sudsakorn et al., 2020). For example, Ma et al. [ 2015] reported that consecutive co-treatment of atenolol (specially, 7-, 15-, 30-, and 60-day treatment) reduced the renal excretion of metformin by the downregulation of MATE1 in kidneys, and consequently increased plasma concentrations of metformin. However, single co-treatment of atenolol with metformin did not change the pharmacokinetics of metformin. You et al. [ 2018] reported that 28-day treatment of *Houttuynia cordata* extract with metformin improved the glucose tolerance of metformin, because H. cordata extract reduced MATE1-mediated metformin efflux from the livers and consequently increased metformin concentration in the livers. In another study, the results for the combination of Scutellariae Radix extract and metformin also showed that only 28-day co-treatment of Scutellariae Radix extract altered the pharmacokinetics of metformin (Yim et al., 2017). These cases support that the treatment period of combination therapy can cause dramatically different outcomes in a victim drug’s pharmacokinetic characteristics. Thus, we designed the experiments to evaluate the treatment period effect (e.g., 1- and 28-day) of GB on metformin pharmacokinetics.
*In* general, the change in the systemic exposure of a victim drug serves as primary evidence of the occurrence of pharmacokinetic interaction in HDIs (Choi, 2020). Systemic exposure is typically evaluated depending on AUC fold-change (Jin et al., 2019): if the AUC of a victim drug is increased, it might be attributable to an increase in absorption, a decrease in tissue distribution, or the elimination of a victim drug by combined treatment of herb. If there is no change in the AUC of a victim drug, HDI can be regarded as absent. On the other hand, the erroneous interpretation of systemic exposure change has been reported in drug interactions (Ma et al., 2015; Yim et al., 2017; Choi et al., 2018; You et al., 2018; Choi, 2020; Choi and Chin, 2020; Sanz Codina and Zeitlinger, 2022; Yu and Wang., 2022), because drug concentrations in tissue as opposed to overall systemic exposure of the drug can contribute to pharmacological and/or toxicological activities. Therefore, it is highly recommended to assess both systemic exposure and tissue distribution in vivo of the drug to evaluate or predict drug interactions (Choi, 2020; Kimoto et al., 2022; Zang et al., 2022).
The OCTs and MATEs are main transporters to trigger metformin movement in the body, which is associated with metformin efficacy or toxicity (Stage et al., 2015; Storelli et al., 2022). Metformin concentration in the livers is determined by transporter-mediated metformin uptake and efflux, because metformin metabolism is negligible. The OCT1 in sinusoidal membrane uptakes metformin from blood to hepatocytes, after which MATE1 in canalicular membrane effluxes metformin into bile (i.e., biliary excretion) (Burt et al., 2016; Li et al., 2022). In the proximal tubules of kidneys, OCT2 in sinusoidal membrane uptakes metformin from blood into proximal tubules and then MATE1 and MATE2-K in the apical membrane transports metformin from proximal tubules into urine. Renal excretion is a main elimination route of metformin, and metformin transport among blood, proximal tubules and urine governs systemic exposure (i.e., the AUC and metformin concentration in blood) (Scotcher et al., 2020; Krishnan et al., 2022). Overall, metformin concentrations in the blood as well as liver and kidneys are associated with efficacy (i.e., inhibition of gluconeogenesis in liver) and toxicity (i.e., lactic acidosis and fluid retention) (You et al., 2018; Chae et al., 2019; Krishnan et al., 2022). In line with this view, it is necessary to investigate whether GB affects both systemic exposure and tissue concentration of metformin in liver and kidneys considering the change of efficacy or toxicity along with the pharmacokinetic changes.
First, the effect of GB on in vitro transporter-mediated metformin transport can be different from their changes in vivo system. In our results, the in vivo change of metformin transports by GB (Tables 1, 2) were not the same as the in vitro inhibitory effect of GB (Figure 1), probably due to the different effect of GB on OCTs and MATEs-mediated metformin transporters. It was reported that the percentages of ginsenosides-Rb1, -Rb2, -Rc, -Rd, -Re, -Rg1, -R-Rg2, -S-Rg2, -R-Rg3 and -S-Rg3 in GB were 1.29, 2.56, 0.86, 3.30, 6.50, 0.24, 0.47, 0.75, $0.14\%$ and $0.36\%$, respectively, in our previous paper (Han and Choi, 2020). In Figure 1, 500 μg/mL of GB significantly inhibited metformin uptake (by 46.8, $61.8\%$ and $37.6\%$ via OCT2, MATE1 and MATE2-K, respectively) compared to metformin group. This result proposed that 6.45, 12.8, 4.30, 16.6, 32.5, 1.20, 2.35, 3.75, 0.70 and 1.80 μg/mL of ginsenosides-Rb1, -Rb2, -Rc, -Rd, -Re, -Rg1, -R-Rg2, -S-Rg2, -R-Rg3 and/or -S-Rg3 in 500 μg/mL GE (in the highest concentration GB used in vitro study) might be involved to inhibit MATE1 or MATE2-mediated metformin uptake in this in vitro study. As a view for in vivo exposure of ginsenosides, only ginsenosides-Rb1, -Rc, -Rd and -Re were measured in plasma of 1 MG B and 28 MG B group ($$n = 3$$ for each group as the preliminary study). In 1 MG B group, the mean C max values of ginsenoside-Rb1, -Rc, -Rd and -Re were 0.252 ± 0.108, 0.201 ± 0.0684, 0.782 ± 0.247 and 0.654 ± 0.301 μg/mL, respectively. In the livers of 1 MG B group, only ginsenosides-Rd and -Re were detected with their concentration ranges were 0.239–0.684 μg/mL and 0.523–2.48 μg/mL, respectively. Although 500 μg/mL of GB directly inhibited OCT2, MATE1 and MATE2-K mediated metformin uptake in vitro (Figure 1), there was no change of metformin concentrations in the plasma, liver and kidneys (Tables 1, 2; Figure 2) and mRNA expressions of OCT1, OCT2, MATE1, or MATE2-K in vivo in the livers and/or kidneys of 1 MG B group (Figure 3). These results indicated that 200 mg/kg GB administration might not enough to directly inhibit transporter-mediated metformin movement in vivo. As possible reasons, the lower concentrations of ginsenosides-Rd and Re in the livers than those contained in 500 μg/mL GB can be considered. In addition, it might be deduced that the concentrations of other unknown compounds of GB, $83.5\%$ of GB, and their direct inhibitory activities against OCT1, MATE1, and/or MATE2-K in the livers of 1 MG B group were different from those in vitro 500 μg/mL GB treatment, although the $83.5\%$ of the constituents’ concentrations in GB could not be measured. In addition, HEK293 cells overexpressing one of OCT1, OCT2, MATE1 and MATE2-K are commercial products of cryopreserved transporter cells. These cells transiently overexpress each single human transporter protein, and they can be used to conveniently evaluate transporter-mediated drug interactions regardless of their expressed tissues (Burt et al., 2016; Sudsakorn et al., 2020; Mathialagan et al., 2021). In HEK293 cells individually overexpressing OCT1, OCT2, MATE1 or MATE2-K, the high concentration of GB (500 μg/mL, not 5 μg/mL) substantially inhibited OCT2, MATE1 and MATE2-K-mediated metformin uptake: the magnitude of the inhibitory effect of GB, which was evaluated in individual transporters, was found to be highest in MATE1 and MATE2-K, followed by OCT2 (Figure 1). However, different from in vitro individual transporter-mediated metformin uptake, it might be attributed that in vivo OCTs and MATEs operate metformin transport together, and they diversely regulate metformin transport according to their expressed membrane sides and/or tissues (Krishnan et al., 2022; Storelli et al., 2022). If the distributions (i.e., consequent concentrations) of any components in GB are diverse in the livers and kidneys, they can produce different inhibitory effect on the transporters between the livers and kidneys (Han and Choi, 2020). For example, the mRNA level of OCT1 was increased and that of MATE1 was reduced in the livers of 28 MG B group compared to other four groups, whereas there was no change of any mRNA levels of OCT1, OCT2, MATE1, and MATE2-K in the kidneys of all groups (Figure 3).
Secondly, GB co-treatment differently changed metformin concentration in the tissues and blood. The 28-day co-treatment of GB significantly increased metformin concentration in the liver of the 28 MGB group compared to that in the 28 M group (Table 2), probably due to the changed mRNA levels of OCTs and MATEs in the liver (Figure 3): the increase of OCT1-mediated metformin uptake into hepatocyte and the decrease of MATE1-mediated metformin efflux into bile might cause the higher metformin concentration in the liver of the 28 MGB group than the 28 M group, as was similarly described in previous reports (Ma et al., 2015; You et al., 2018). However, GB did not affect any AUC values of metformin (i.e., systemic exposure of metformin) in any groups (Table 1) due to the no alternation of any mRNA levels of OCT1, OCT2, MATE1 and MATE2-K in the kidneys of any groups (Figure 3). The metformin distribution in kidneys and its renal excretion mainly determine the systemic exposure. In particular, OCT2 is the most predominant transporter in metformin uptake into kidneys and OCT2-mediated metformin uptake might contribute more substantially to the renal concentration of metformin than MATE1 and MATE2-K-mediated metformin (Motohashi and Inui, 2013; Burt et al., 2016). Thus, the unchanged metformin concentrations in the kidneys and subsequently unchanged systemic exposure of metformin (Tables 1, 2) can be explained based on the no alternation of any mRNA level of OCT1, OCT2, MATE1 and MATE2-K in the kidneys of any groups (Figure 3).
Third, treatment period is an important factor to cause transporter-mediated drug interactions. Aside from 28-day co-treatment of GB and metformin, 1-day co-treatment of GB did not affect the mRNA levels of OCT1 and MATE1 in liver or kidneys (Figure 3) or metformin concentrations in plasma, liver and kidneys (i.e., systemic exposure and local exposure in the tissues) (Table 1, 2). In tissue distribution, 28-day co-treatment of GB with metformin increased metformin concentration via OCT1 induction and MATE1 inhibition in the livers, a pharmacological target tissue. Similar phenomena were reported by Yim et al. [ 2017] and You et al. [ 2018]: long-term treatment of herbal products changed the mRNA levels of OCT1 and/or MATE2-K-mediated metformin transport in liver.
Forth, the pharmacokinetic change of metformin by GB can be associated with the efficacy change of metformin. Changes in metformin concentration in the liver, as its pharmacological target tissue, might be related to enhancing metformin’s efficacy. Chae et al. [ 2019] suggested that the increased concentration of metformin in the liver is a possible reason to improved metformin efficacy such as enhancing glucose tolerance and ameliorating insulin resistance and hepatic steatosis in metformin plus GB combination compared to metformin single treatment. The following phenomena were observed: the significantly higher concentration (by $83.6\%$) of metformin in the livers along with the reduced AUCglucose (by $29.3\%$) and reduced HOMA-IR (by $15.4\%$) values in mice fed high-fat diet with metformin plus GB for 98 days compared to those in mice fed with fed high-fat diet with metformin for 98-day. This could be due to an increase of OCT-mediated metformin uptake into liver and the subsequent increase in AMP-activated protein kinase together with co-treated GB. Thus, it is plausible that GB changes metformin distribution in the liver without systemic exposure alternation. Although the efficacy change of metformin by GB co-treatment was not investigated at this time, metformin’s efficacy can be improved by 28-day co-treatment of GB, in accordance with the increased metformin concentration in the liver as the similar result of Chae et al. [ 2019]. Moreover, the toxicity of metformin did not arise with GB co-treatments (Table 3). Lactic acidosis is one of the major metformin toxicities, and it is associated with metformin’s systemic exposure (Mathialagan et al., 2021). The lack of changes in metformin concentration in the blood in all groups indicates that additional metformin toxicity was not observed.
## 5 Conclusion
We can suggest that the change in metformin pharmacokinetics by GB co-treatment can contribute to the changes in efficacy and toxicity. In particular, these results indicate that systemic exposure and tissue distribution depending on treatment periods may be important in HDIs, although further study is needed to clarify the mechanism involved in the transcriptional or transcriptional regulation of MATE1 expression in the liver depending on treatment periods. Although the mechanism of metformin and GB combinational effect change was not proved, the pharmacokinetic change of metformin by 28-day co-treatment of GB can be suggested.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by Institute of Laboratory Animal Resources of Dongguk University_Seoul, Republic of Korea.
## Author contributions
Y-WC and YHC; methodology, SY, BHY, H-SC, SYH, and PN; software, SY and BHY; validation, SH; investigation, SY, BHY, H-SC, and SYH; resources, HK, H-SC, Y-WC, and Y-WC; data curation, SY, BHY, H-SC, and YHC; writing original draft preparation, CWL, SY, BHY, H-SC, and SYH; writing review and editing, H-SC, HK, HWK., Y-WC, and YHC; visualization, CWL, BHY, H-SC, CWL, S-YK, MB, J-EY, and JJ; supervision, YHC; project administration, HK, HSC, Y-WC, and YHC; funding acquisition, HSC, Y-WC, and YHC All authors have read and agreed to the published version of the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Peroxisome proliferator-activated receptor ɣ agonist mediated inhibition of
heparanase expression reduces proteinuria
authors:
- Marjolein Garsen
- Baranca Buijsers
- Marloes Sol
- Lena Gockeln
- Ramon Sonneveld
- Toin H. van Kuppevelt
- Mark de Graaf
- Jacob van den Born
- Jan A.A.M. Kamps
- Daniël H. van Raalte
- Rutger W. van der Meer
- Hildo J. Lamb
- Jan-Luuk Hillebrands
- Ton J. Rabelink
- Marissa L. Maciej-Hulme
- Guido Krenning
- Tom Nijenhuis
- Johan van der Vlag
journal: eBioMedicine
year: 2023
pmcid: PMC10043778
doi: 10.1016/j.ebiom.2023.104506
license: CC BY 4.0
---
# Peroxisome proliferator-activated receptor ɣ agonist mediated inhibition of heparanase expression reduces proteinuria
## Body
Research in contextEvidence before this studyThe heparan sulfate-degrading enzyme, heparanase, is essential for the degradation of glomerular endothelial glycocalyx and the development of proteinuria in glomerular diseases. Thiazolidinediones are synthetic, high affinity agonists for the nuclear transcription factor, peroxisome proliferator-activated receptor ɣ (PPARɣ), which ameliorates proteinuria in certain glomerular diseases. Added value of this studyThis paper describes that heparanase is regulated by PPARy both in vitro, in cultured glomerular cells, and in vivo, in a rat model for focal segmental glomerulosclerosis. In humans treated with pioglitazone, plasma heparanase activity tended to be decreased. It is demonstrated that PPARɣ directly binds to the heparanase promotor region in both podocytes and glomerular endothelial cells. Implications of all the available evidencePPARy-mediated regulation of heparanase expression provides an additional mechanism that explains the anti-proteinuric and renoprotective effects of thiazolidinediones in clinical practice.
## Summary
### Background
Proteinuria is associated with many glomerular diseases and a risk factor for the progression to renal failure. We previously showed that heparanase (HPSE) is essential for the development of proteinuria, whereas peroxisome proliferator-activated receptor ɣ (PPARɣ) agonists can ameliorate proteinuria. Since a recent study showed that PPARɣ regulates HPSE expression in liver cancer cells, we hypothesized that PPARɣ agonists exert their reno-protective effect by inhibiting glomerular HPSE expression.
### Methods
Regulation of HPSE by PPARɣ was assessed in the adriamycin nephropathy rat model, and cultured glomerular endothelial cells and podocytes. Analyses included immunofluorescence staining, real-time PCR, heparanase activity assay and transendothelial albumin passage assay. Direct binding of PPARɣ to the HPSE promoter was evaluated by the luciferase reporter assay and chromatin immunoprecipitation assay. Furthermore, HPSE activity was assessed in 38 type 2 diabetes mellitus (T2DM) patients before and after $\frac{16}{24}$ weeks treatment with the PPARɣ agonist pioglitazone.
### Findings
Adriamycin-exposed rats developed proteinuria, an increased cortical HPSE and decreased heparan sulfate (HS) expression, which was ameliorated by treatment with pioglitazone. In line, the PPARɣ antagonist GW9662 increased cortical HPSE and decreased HS expression, accompanied with proteinuria in healthy rats, as previously shown. In vitro, GW9662 induced HPSE expression in both endothelial cells and podocytes, and increased transendothelial albumin passage in a HPSE-dependent manner. Pioglitazone normalized HPSE expression in adriamycin-injured human endothelial cells and mouse podocytes, and adriamycin-induced transendothelial albumin passage was reduced as well. Importantly, we demonstrated a regulatory effect of PPARɣ on HPSE promoter activity and direct PPARy binding to the HPSE promoter region. Plasma HPSE activity of T2DM patients treated with pioglitazone for $\frac{16}{24}$ weeks was related to their hemoglobin A1c and showed a moderate, near significant correlation with plasma creatinine levels.
### Interpretation
PPARɣ-mediated regulation of HPSE expression appears an additional mechanism explaining the anti-proteinuric and renoprotective effects of thiazolidinediones in clinical practice.
### Funding
This study was financially supported by the $\frac{10.13039}{501100002997}$Dutch Kidney Foundation, by grants 15OI36, 13OKS023 and 15OP13. Consortium grant LSHM16058-SGF (GLYCOTREAT; a collaboration project financed by the PPP allowance made available by $\frac{10.13039}{100016036}$Top Sector Life Sciences & Health to the $\frac{10.13039}{501100002997}$Dutch Kidney Foundation to stimulate public-private partnerships).
## Evidence before this study
The heparan sulfate-degrading enzyme, heparanase, is essential for the degradation of glomerular endothelial glycocalyx and the development of proteinuria in glomerular diseases. Thiazolidinediones are synthetic, high affinity agonists for the nuclear transcription factor, peroxisome proliferator-activated receptor ɣ (PPARɣ), which ameliorates proteinuria in certain glomerular diseases.
## Added value of this study
This paper describes that heparanase is regulated by PPARy both in vitro, in cultured glomerular cells, and in vivo, in a rat model for focal segmental glomerulosclerosis. In humans treated with pioglitazone, plasma heparanase activity tended to be decreased. It is demonstrated that PPARɣ directly binds to the heparanase promotor region in both podocytes and glomerular endothelial cells.
## Implications of all the available evidence
PPARy-mediated regulation of heparanase expression provides an additional mechanism that explains the anti-proteinuric and renoprotective effects of thiazolidinediones in clinical practice.
## Introduction
Proteinuria is one of the first clinical signs of many glomerular diseases and an independent risk factor for the progression to renal failure.1 Proteinuria can be caused by damage to any of the 3 layers of the glomerular filtration barrier (GFB), which is composed of a fenestrated endothelium covered with a glycocalyx, the glomerular basement membrane and podocytes. All layers of the GFB should be intact to prevent the development of proteinuria.2 Peroxisome proliferator-activated receptor ɣ (PPARɣ) is a transcription factor that belongs to the superfamily of nuclear receptors. Upon stimulation, PPARɣ forms a heterodimer with the retinoid X receptor, and this heterodimer regulates the transcription of various target genes.3 PPARɣ has a large binding pocket that enables it to interact with naturally occurring and synthetic ligands of great structural variety. A group of synthetic, high affinity agonists for PPARɣ are the thiazolidinediones (TZDs), which include pioglitazone and rosiglitazone. Initially, TZDs were developed to reduce insulin resistance and thereby to treat type 2 diabetes.4,5 However, a number of studies also suggested that PPARɣ agonists have direct renoprotective effects in experimental diabetes.6, 7, 8 Moreover, PPARɣ agonists have also been suggested to be renoprotective in several (experimental) non-diabetic glomerular diseases, for example, human focal segmental glomerulosclerosis.9, 10, 11, 12, 13 A recent study showed that PPARɣ agonists reduced heparanase (HPSE) gene transcription in hepatocellular carcinoma metastases by direct binding of PPARɣ to the HPSE promoter.14 HPSE is the only mammalian enzyme that can cleave negatively charged heparan sulfate (HS),15 and loss of HS in the GFB has been associated with the development of proteinuria.16,17 We previously showed that HPSE is essential for the development of proteinuria and subsequent renal damage in experimental glomerulonephritis and diabetic nephropathy.18,19 Furthermore, several studies showed reduced proteinuria and improved renal function after inhibition of HPSE activity in glomerular diseases including experimental diabetic nephropathy, glomerulonephritis, anti-GBM antibody disease, and passive Heymann nephritis.18,20, 21, 22, 23 We hypothesized that PPARɣ agonists exert their renoprotective effect by inhibiting the expression of glomerular HPSE since HPSE is essential for the development of proteinuria, TZDs reduce proteinuria, and PPARɣ regulates HPSE expression in liver cancer cells. In the current study, we evaluated the effects of PPARɣ agonism and antagonism on glomerular HPSE and HS expression in vivo and in vitro. We evaluated whether PPARɣ directly regulates HPSE transcription. Finally, we assessed the plasma HPSE activity in type 2 diabetes mellitus (T2DM) patients before and after $\frac{16}{24}$ weeks treatment with the PPARɣ agonist pioglitazone.
## Animals
Adriamycin nephropathy (AN) was induced in Wistar rats (8-week-old; Charles River Laboratories, Wilmington, MA (RRID:RGD_2312511)) as previously described.11 Rats were treated daily with 12 mg/kg pioglitazone (Sigma–Aldrich) or vehicle via an intraperitoneal injection. After 6 weeks, rats were sacrificed. In addition, healthy Wistar rats were treated with daily intraperitoneal injections with 2.5 mg/kg body weight of the PPARɣ antagonist GW9662 (Sigma–Aldrich) or vehicle, as previously described.11 Rats were sacrificed after 3 weeks.
## Cell culture
Opossum Kidney (OK) cells (RRID:CVCL_0472) were cultured as described previously.24 Conditionally immortalized mouse podocytes (mPC-5, RRID:CVCL_AS87), human podocytes (hPOD), mouse glomerular endothelial cells (mGEnC-1), and human glomerular endothelial cells (ciGEnC, RRID:CVCL_W185) were cultured as described previously.25, 26, 27, 28 HPSE was silenced in mGEnC-1 by a HPSE shRNA construct (Qiagen, Venlo, the Netherlands). Differentiated mPC-5 and mGEnC-1 were stimulated with vehicle or 0.25 μg/ml adriamycin (Sigma–Aldrich) and treated with 10 μM pioglitazone (Sigma–Aldrich). In addition, differentiated mPC-5 and mGEnC-1 were treated with 1 μM or 10 μM of the PPARɣ antagonist GW9662 (Sigma–Aldrich). All experiments were performed at least in triplicate.
## Immunofluorescence staining
Glomerular expression of HPSE and HS was determined by immunofluorescence staining as described previously.29 Primary antibodies included the polyclonal anti-HPSE antibody HPA1 (ProsPecTany, Rehovot, Israel (RRID:AB_2246577)) and the VSV-tagged single chain HS variable fragment (scFv) antibody EV3B2 (N-, and 6-O sulfation).30 Secondary antibodies included goat anti-rabbit IgG Alexa 488 (Invitrogen Life Technologies, RRID:AB_143165) for detection of HPSE and anti-VSV Cy3 (Sigma–Aldrich, RRID:AB_259043) for detection of EV3B2. Staining intensities of HPSE and HS were scored in fifty glomeruli per section on a scale between 0 (no staining) and 10 (maximal staining intensity). Scoring was performed on blinded sections by two independent investigators.
## RNA isolation and real-time PCR
Total RNA was isolated from rat renal cortex, mPC-5 and mGEnC-1 using the RNeasy mini kit (Qiagen), according to manufacturer's instructions. 1 μg of RNA was reverse transcribed into cDNA using the Transcriptor First Strand cDNA Synthesis kit (Roche Diagnostics, Mannheim, Germany). HPSE mRNA expression was determined by real-time PCR on the CFX real-time PCR system (Bio-Rad Laboratories, Hercules, CA, USA) using SYBR Green Supermix (Roche Diagnostics) and gene-specific primers (Table 1; Isogen Life Science, de Meern, the Netherlands). Relative HPSE mRNA expression was determined using the delta–delta CT method with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as housekeeping gene. Table 1Primers used in real-time PCR.*Target* genePrimer sequencemHPSE(F) 5′-GAGCGGAGCAAACTCCGAGTGTATC-3′(R) 5′-GATCCAGAATTTGACCGTTCAGTT-3′rHPSE(F) 5′-GAGCGAAGCAAACTCCGAGTGTAC-3′(R) 5′-GATCGGTTTGACCGTTCAGTTGG-3′GAPDH(F) 5′-AGAAACCTGCCAAGTATGATGAC-3′(R) 5′-TCATTGTCATACCAGGAAATGAG-3′mHPSE, mouse heparanase; rHPSE, rat heparanase; mGAPDH, glyceraldehyde-3-phosphate dehydrogenase; F, forward; R, reverse.
## Heparanase activity assay
Renal cortical HPSE activity was determined by a commercially available assay (AMS Biotechnology, Abingdon, UK, cat no# Ra001-02-K) following manufacturer's instructions. Plasma HPSE activity was determined by a commercially available heparan degrading enzyme assay kit (Takara Bio, Shiga, Japan, cat no#MK412) according to the manufacturer's instructions.
## Heparanase protein assay
HPSE protein in plasma was measured using a human heparanase ELISA kit (Abcam, Cambridge, UK, cat. no. # ab256401) according to the manufacturers instruction.
## Transendothelial albumin passage
mGEnC-1 were seeded on polyester membranes in tissue culture inserts (Corning Incorporated, NY, USA). After differentiation, cells were treated with adriamycin in the presence or absence of the PPARɣ agonist pioglitazone, or with the PPARɣ antagonist GW9662 as outlined above. Transendothelial albumin passage was determined as described previously.31
## Luciferase reporter assay
OK cells transfected with a pGL3 firefly luciferase vector containing the 3.5 kb promoter region of the human HPSE gene32 or an empty pGL3 vector construct, were treated with 10 μM GW9662 (Sigma–Aldrich). The pRL-CMV construct (Promega Corp., Fitchburg, WI) was used to correct for transfection efficiency. OK cells were harvested 24 h after transfection and luciferase activity was determined using the Dual-Luciferase reporter assay (Promega) according to the manufacturer's instructions.
## Chromatin immunoprecipitation assay to determine PPARɣ binding to the HPSE promoter
Chromatin was cross-linked using 1.5 mM ethylene glycol bis (succinimidyl succinate) (EGS, Thermofischer Scientific) and $1\%$ formaldehyde (Sigma) in phosphate buffered saline (PBS). Cells were scraped, the cell suspensions were collected and centrifuged at 2500 rpm for 4 min. The pellet, containing the cells was stored at −80 °C until further use. After thawing, nuclei were isolated with subsequent lysis buffer 1 (50 mM Hepes-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, $10\%$ glycerol, $0.5\%$ NP-40, $0.25\%$ Triton-X-100) supplemented with freshly added proteinase inhibitor cocktail (1:100, v/v), followed by centrifugation at 2000 × g for 5 min. Nuclei were dissolved in lysis buffer 2 (10 mM Tris HCl pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA) supplemented with freshly added phosphatase inhibitor, followed by centrifugation at 2000 × g for 5 min. Afterwards, the pellet was dissolved in lysis buffer 3 (10 mM Tris HCl pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, $0.1\%$ Na-deoxycholate, $0.5\%$ N-lauroylsarcosine, $1\%$ NP-40) supplemented with freshly added phosphatase inhibitor. The chromatin was sonicated using a Bioruptor (Diagenode, Seraing, Belgium) with 10 cycles of 30′ ON/OFF. $1\%$ (v/v) Triton-X-100 was added to the sonicated samples and the samples were centrifuged on full speed at 4 °C for 10 min and the pellet discarded. Immunoprecipitation was performed with 1.07 μg of PPARɣ antibody (Genetex) or IgG control (Abcam ab46540) coupled to 30 μl of Dynabeads Protein-A (Invitrogen). 5 μg of pre-cleared chromatin was added to the antibody–beads complexes and incubated at 4 °C with rotation overnight. Beads were washed with RIPA buffer (50 mM HEPES pH 7.6, 1 mM EDTA, $0.7\%$ Na-deoxycholate, $1\%$ NP-40, 0.5M LiCl) and afterwards with TE buffer (10 mM Tris–HCl, pH8.0, 1 mM EDTA, pH8.0). The antigen–antibody complexes were eluted with elution buffer ($1\%$ SDS, 0.1M NaHCO3) at 62 °C for 4 h. Input samples served as controls. Eluted samples were incubated with 2 μl RNAse (10 mg/ml stock, Thermo Scientific) at 37 °C for 1 h. Next, 4 μl of Proteinase K (20 mg/ml stock, Roche Diagnostics, Germany) was added to the samples and incubated at 55 °C for 2 h. DNA fragments were purified using the QIAquick PCR purification kit (Qiagen) according to the manufacturers’ instructions. DNA enrichment was analyzed by real-time PCR using a forward and reverse primer for the predicted PPARɣ-binding site in the promoter of HPSE (Table 2). The enrichment of the promoter sequences in the DNA samples was calculated relative to the percentage of input. Table 2Primers used for ChIP-qPCR assays. Genomic regionPrimer sequencemHPSE_-1667(F) 5′-GGCGAGTTGCTAACAGGAAG-3′(R) 5′-TCTGGAGCCAGACCTGAGAT-3′mHPSE_-436(F) 5′-GTTAAAAGCCCCGGTTGAG-3′(R) 5′-CAATGCTCGGATCAGGTTTT-3′mHPSE_+1007(F) 5′-GTGCCAGTCTGCAAGTGTGT-3′(R) 5′-TGTACCTCGCATGCAAGAAG-3′mHPSE_-1667, mouse heparanase 1667 bases before HPSE TSS; mHPSE_-436, mouse heparanase 436 bases before HPSE TSS, mHPSE_+1007, mouse heparanase 1007 bases after HPSE TSS; F, forward; R, reverse.
## Patients and study design
Patient plasma samples ($$n = 38$$) were obtained from two separate studies. 34 patient plasma samples were obtained from the PIRAMID (Pioglitazone Influence on tRiglyceride Accumulation in the Myocardium In Diabetes) study, which was a 24-week prospective, randomized, double-blind, double-dummy with active comparator, 2-center parallel-group intervention.33 Males 45 to 65-year old with uncomplicated T2DM were eligible. The inclusion criteria were a glycohemoglobin level of $6.5\%$–$8.5\%$ at screening, body mass index [weight/(length2)] of 25–32 kg/m2, and blood pressure below $\frac{150}{85}$ mm Hg. The exclusion criteria were any clinically significant disorder, particularly any history or complaints of cardiovascular or liver disease or diabetes-related complications, and prior use of thiazolidinediones or insulin. Study procedures are described previously.33 In short, patients received pioglitazone (15 mg once daily, titrated to 30 mg once daily after 2 weeks) and underwent outcome measurements at baseline and at study termination after 24 weeks. Four patient plasma samples were obtained from a 16-week phase IIIB multicenter randomized double-blind study.34,35 Inclusion and exclusion criteria of this study were similar to the criteria described for the PIRAMID study. Study procedures are described previously.34,35 In short, patients received pioglitazone (30 mg once daily), and outcome measurements were obtained at baseline and at study termination after 16 weeks.
## Statistical analysis
Data is presented as mean ± SEM. Significance was evaluated by a one-way ANOVA and post hoc analysis with the Tukey's multiple comparison test. A student's t-test was used to evaluate differences between two groups. Significance in transendothelial albumin passage was evaluated by a 2-way repeated measures ANOVA with a Bonferroni post-test. In the PPARɣ-ChIP, outliers were identified using a ROUT-test with a Q-value of $5\%$. Significant differences were evaluated by a paired student's t-test. For the patient data, D'Agostino & Pearson normality test was performed to test for normality of data. Significance was determined by Student's t-test or Mann Whitney test to compare two groups. Relationship analysis was performed using Pearson's correlation coefficient. All analysis were performed using GraphPad Prism V.8.4.2 (La Jolla, USA). A P-value of ≤0.05 was considered statistically significant.
## Ethics statement
All animal experiments were approved by the Animal Ethical Committee of the Radboud University Nijmegen and performed in accordance with the guidelines of the Dutch Council for Animal Care (Approval number DEC2014136). The protocols for the patient studies were approved by the medical ethics committee at each study site (Approval numbers P04.193 and NL2264902908CCMO), and the study was performed in full compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants.
## Role of funders
The funding sources had no role in study design, data collection, data analysis, interpretation and writing of this manuscript.
## Adriamycin-induced heparanase expression is attenuated by the PPARɣ agonist pioglitazone
To study the effect of PPARɣ agonism on glomerular HPSE and HS expression in vivo, adriamycin nephropathy (AN; an animal model for human FSGS) was induced in rats that were subsequently treated with the PPARɣ agonist pioglitazone or vehicle for 6 weeks. Induction of AN resulted in the development of proteinuria, which was significantly reduced by treatment with pioglitazone, as we described previously.11 Cortical HPSE mRNA expression (Fig. 1a), glomerular HPSE protein expression (Fig. 1b and e), and cortical HPSE activity (Fig. 1c) were significantly increased by induction of AN. Daily treatment with pioglitazone normalized HPSE expression and activity (Fig. 1a–c and e). Moreover, glomerular HS expression was significantly reduced by induction of AN but preserved by treatment with pioglitazone (Fig. 1d and f).Fig. 1The PPARɣ agonist pioglitazone reduced glomerular heparanase expression and preserved glomerular HS expression in rats with AN. ( a) Cortical heparanase mRNA expression, (b and e) glomerular heparanase protein expression by quantification of immunofluorescence images, and (c) cortical heparanase activity were significantly increased 6 weeks after induction of AN. Heparanase expression and activity were reduced by daily treatment with 12 mg/kg body weight pioglitazone. ( d and f) Glomerular HS expression was significantly reduced by induction of AN, but preserved by daily treatment with pioglitazone. Representative pictures of (e) glomerular heparanase protein expression and (f) glomerular HS expression (magnification ×400). 8 rats per group were used for analyses. Data are expressed as mean ± SEM. ∗$P \leq 0.05$, ∗∗$P \leq 0.01$, ∗∗∗$P \leq 0.001$, and ∗∗∗∗$P \leq 0.0001.$ AN, adriamycin nephropathy; ADRIA, adriamycin; AU, arbitrary units.
## The PPARɣ agonist pioglitazone reduced HPSE expression and transendothelial albumin passage in vitro
To extend the in vivo effects of the PPARɣ agonist pioglitazone on glomerular HPSE expression in AN, we evaluated whether pioglitazone regulates HPSE expression in cultured mGEnC-1, mPC-5, ciGEnC and hPOD. Cell stimulation with adriamycin significantly induced HPSE mRNA expression in mouse podocytes, whereas HPSE mRNA expression was reduced by treatment with pioglitazone (Fig. 2a). Stimulation of mouse glomerular endothelial cells with adriamycin reduced HPSE mRNA expression, which was not affected by treatment with pioglitazone (Fig. 2b). Notably, HS expression on mGEnC-1 was reduced by stimulation with adriamycin.31 At the functional level, transendothelial albumin passage was significantly increased by stimulation with adriamycin, which was reduced by additional treatment with pioglitazone (Fig. 2c). Human glomerular endothelial cells did upregulate HPSE mRNA expression upon adriamycin stimulation, which was ameliorated by treatment with pioglitazone (Fig. 2d). On the contrary, human podocytes show no response to adriamycin on HPSE mRNA expression, which might indicate that hPODs are not a good model system for this study (data not shown).Fig. 2The PPARɣ agonist pioglitazone reduced heparanase expression and transendothelial albumin passage in vitro. ( a) Stimulation of mouse podocytes (mPC-5) with adriamycin for 24 h significantly increased heparanase mRNA expression. Heparanase mRNA expression was reduced by treatment with 10 μM of the PPARɣ agonist pioglitazone. ( b) Stimulation of mouse glomerular endothelial cells (mGEnC-1) with adriamycin for 16 h reduced heparanase mRNA expression. Heparanase mRNA expression was not affected by treatment with 10 μM of the PPARɣ agonist pioglitazone. ( c) Stimulation of mGEnC-1 with adriamycin for 16 h significantly increased the passage of albumin across the endothelial monolayer. Transendothelial albumin passage was reduced by treatment with 10 μM of the PPARɣ agonist pioglitazone. ( d) Stimulation of human glomerular endothelial cells (ciGEnC) with adriamycin for 24 h increased heparanase mRNA expression. Heparanase mRNA expression was ameliorated by treatment with 10 μM of the PPARɣ agonist pioglitazone. Data are expressed as mean ± SEM. ∗$P \leq 0.05$, ∗∗$P \leq 0.01$, and ∗∗∗$P \leq 0.001.$ ADRIA, adriamycin.
## Pharmacologic inhibition of PPARɣ induces heparanase expression and activity in vivo
Healthy rats were treated daily with the irreversible PPARɣ antagonist GW9662 for 3 weeks to study the effects of pharmacologic inhibition of PPARɣ on glomerular HPSE and HS expression in vivo. As we described previously, treatment with GW9662 induced significant proteinuria.11 Cortical HPSE mRNA expression (Fig. 3a), glomerular HPSE protein expression (Fig. 3b and e), and cortical HPSE activity (Fig. 3c) were increased by treatment with GW9662, although this was not significant for HPSE protein expression ($$P \leq 0.09$$). In line, glomerular HS expression was reduced by treatment with GW9662 (Fig. 3d and f).Fig. 3The PPARɣ antagonist GW9662 induced glomerular heparanase expression and reduced glomerular HS expression in mice. ( a) Cortical heparanase mRNA expression, (b and e) glomerular heparanase protein expression, and (c) cortical heparanase activity were increased by treatment with 2.5 mg/kg body weight of the PPARɣ antagonist GW9662 for 3 weeks, whereas glomerular HS expression was reduced according to semi-quantitative analysis of immunofluorescence images (d and f). Representative pictures of (e) glomerular heparanase protein expression and (f) glomerular HS expression (magnification ×400). 4 rats per group were used for analysis. Data are expressed as mean ± SEM. ∗$P \leq 0.05$ and ∗∗$P \leq 0.01.$ AU, arbitrary units.
## The PPARɣ antagonist GW9662 induces transendothelial albumin passage in vitro in a heparanase-dependent manner
Cultured mGEnC-1, mPC-5, ciGEnC and hPOD were treated with 1 or 10 μM of the PPARɣ antagonist GW9662 for 24 h to evaluate the effects of PPARɣ antagonism on glomerular HPSE expression in vitro. Treatment of mouse podocytes with 10 μm GW9662 significantly increased HPSE mRNA expression, whereas treatment with 1 μM GW9662 was not effective (Fig. 4a). HPSE mRNA expression in mGEnC-1 was increased by treatment with both 1 and 10 μM GW9662 (Fig. 4b). At the functional level, passage of albumin across an mGEnC-1 monolayer was significantly increased by treatment with 10 μM GW9662 (Fig. 4c). On the contrary, human podocytes and endothelial cells did not up-regulate HPSE mRNA expression upon stimulation with GW9662 (data not shown).Fig. 4The PPARɣ antagonist GW9662 induced heparanase expression and increased transendothelial albumin passage in a heparanase-dependent manner. ( a) Treatment of mouse podocytes with 10 μM of the PPARɣ antagonist GW9662 for 24 h significantly increased heparanase mRNA expression. ( b) Treatment of mouse glomerular endothelial cells (mGEnC-1) with 1 μM and 10 μM of the PPARɣ antagonist GW9662 for 24 h resulted in increased heparanase mRNA expression. ( c) The cumulative passage of albumin across mGEnC-1 monolayers was significantly increased by treatment with 10 μM of the PPARɣ antagonist GW9662 for 24 h. (d) Treatment of heparanase-silenced mGEnC-1 (knockdown efficiency $68\%$) with 10 μM of the PPARɣ antagonist GW9662 for 24 h led to a lower transendothelial albumin passage compared with scrambled mGEnC-1 treated with GW9662. Data are expressed as mean ± SEM. ∗$P \leq 0.05$, ∗∗$P \leq 0.01$, ∗∗∗$P \leq 0.001$, and ∗∗∗∗$P \leq 0.0001.$
To evaluate whether the GW9662-induced transendothelial albumin passage was HPSE-dependent, HPSE was silenced in mGEnC-1, which resulted in ∼$50\%$ reduction of HPSE mRNA expression. The GW9662-induced increase in transendothelial albumin passage was significantly ameliorated in HPSE-silenced mGEnC-1 compared with mGEnC-1 transfected with a scrambled shRNA (Fig. 4d), indicating that the GW9662-induced transendothelial albumin passage is HPSE-dependent.
## The PPARɣ antagonist GW9662 increased heparanase promoter activity
A luciferase reporter assay was performed to evaluate whether the in vivo and in vitro observed effects of the PPARɣ antagonist GW9662 on HPSE expression are caused by a direct regulation of HPSE promoter activity.32 Treatment with 10 μM GW9662 significantly induced HPSE promoter activity (Fig. 5a).Fig. 5PPARɣ directly binds to the heparanase promoter. ( a) Opossum kidney cells were transfected with the HPR1-3.5 HPSE promoter construct and treated with 10 μM of the PPARɣ antagonist GW9662 for 24 h. Treatment with the PPARɣ antagonist GW9662 significantly increased heparanase promoter activity. ( b) Treatment of mouse podocytes (mPC-5) with adriamycin for 24 h decreased binding of PPARɣ to the heparanase promoter significantly at binding sites −436 and +1007. A trend was observed for binding site −1667. Treatment with 10 μM of the PPARɣ agonist pioglitazone prevented the adriamycin-induced decrease in PPARɣ binding at all binding sites. Treatment with 10 μM of the PPARɣ antagonist GW9662 tended to reduce PPARɣ binding to binding sites −436 and −1667 (c) Stimulation of mouse glomerular endothelial cells (mGEnC-1) with adriamycin and/or 10 μM pioglitazone, or GW9662 did not significantly affect PPARɣ binding to the heparanase promoter, although similar trends were observed as found for the mPC-5 cells. Data are expressed as mean ± SEM. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$
## Direct binding of PPARɣ to the HPSE promoter is regulated by adriamycin and PPARɣ agonists and antagonists
Next, a Chromatin Immunoprecipitation (ChIP) assay was performed to evaluate whether the observed effects of adriamycin, PPARɣ agonist pioglitazone, and PPARɣ antagonist GW9662 on HPSE expression in mPC-5 and mGEnC-1 cells were mediated by direct binding of PPARɣ to the endogenous HPSE promoter. Within the region of 2000 bases before and after the HPSE transcription start site (TSS), three putative PPARɣ binding sites (PPRE's) can be identified using MotifMap,36 i.e at a distance of −1667, −436, and +1007 bases. Binding of PPARɣ to the HPSE promoter was evaluated in cultured mPC-5 and mGEnC-1 treated with adriamycin either with or without pioglitazone, or with the PPARɣ antagonist GW9662, and in untreated cells. In mPC-5 exposed to adriamycin, binding of PPARɣ to the HPSE promoter was significantly reduced for binding site −436 and +1007, whereas there was a similar trend for the binding site −1667. Treatment with pioglitazone prevented the adriamycin-induced decline in PPARɣ binding to all 3 PPARɣ binding sites in the HPSE promoter. However, treatment with GW9662 did not reveal a significant reduction in PPARɣ binding to the PPARɣ binding sites in the HPSE promoter, although there was a trend for lower PPARɣ binding for binding sites −436 and −1667 (Fig. 5b). In mGEnC-1, the different treatments did not significantly change PPARɣ binding to the HPSE promoter for all 3 binding sites. Nevertheless, there is a trend for adriamycin-induced lowering of PPARɣ binding for binding sites −1667 and +1007, as well as for GW9662-induced lowering of PPARɣ binding for all three binding sites (Fig. 5c).
## Pioglitazonde tends to reduce the plasma HPSE activity in type 2 diabetes mellitus patients
Full clinical characterization is described in the original manuscripts of the studies from which the samples have been obtained.33, 34, 35 The most important clinical characteristics of the patients are summarized in Table 3. Overall, hemoglobin A1c (HbA1C), plasma glucose, and diastolic blood pressure levels were decreased upon $\frac{16}{24}$ weeks pioglitazone treatment. HDL cholesterol was increased after treatment with pioglitazone. The plasma creatinine level, insulin level, LDL cholesterol level, triglycerides level, systolic blood pressure and heart rate remained unaltered. Table 3Clinical characteristics of patients. VariablesBaseline$\frac{16}{24}$ weeksPNumber3731HbA1C, %7.13 (0.19)6.51 (0.17)0.023Plasma creatinine, μmol/L79.41 (2.37)82.61 (2.30)0.153Plasma glucose, mmol/L9.08 (0.35)7.93 (0.29)0.024Insulin, ρmol/L68.16 (5.80)55.41 (4.29)0.180LDL cholesterol, mmol/L2.61 (0.11)2.68 (0.81)a0.781HDL cholesterol, mmol/L1.12 (0.04)1.26 (0.05)0.034Triglycerides, mmol/L1.84 (0.16)1.69 (0.20)0.412Systolic blood pressure, mm Hg131.2 (2.05)132.1 (1.98)0.716Diastolic blood pressure, mm Hg77.92 (1.19)81.13 (1.43)0.041Heart rate, beats/min65.14 (1.45)62.58 (0.98)0.236Data are presented as mean (SEM).HbA1C, hemoglobin A1c; LDL, low-density lipoprotein; HDL, high-density lipoprotein.aIndicates missing value.
Patients with type 2 diabetes mellitus (T2DM) are known to have increased levels of HPSE activity, and it is well known that HPSE is essential for the development of diabetic nephropathy.18,37 Therefore, the plasma HPSE activity of T2DM patients was measured at baseline and compared with the HPSE activity level upon $\frac{16}{24}$ weeks treatment with pioglitazone. Pioglitazone did not affect the plasma HPSE activity of the T2DM patients ($$P \leq 0.21$$) (Fig. 6a). However, a decrease in plasma HPSE activity can be observed in patients in the lowest quartile of the cohort, which can be appreciated when the lowest quartile of the baseline is compared with the lowest quartile after treatment with pioglitazone presented in the boxplot ($$P \leq 0.07$$) (Fig. 6a). It should be noted that the HPSE activity was measured in plasma, whereas a more pronounced effect of pioglitazone on HPSE activity might be observed in renal cortex. Similarly, a trend of decreased heparanase expression levels in plasma can be observed in the patient cohort after treatment with pioglitazone ($$P \leq 0.12$$) (Fig. 6b). One of the outcome parameters that was reported in the PIRAMID study to change significantly upon pioglitazone treatment is HbA1C,33 which describes the average blood glucose levels for the last two to three months. We observed higher HPSE activity levels in patients whose HbA1C levels exceeded $6.5\%$ (Fig. 6c), although no direct correlation could be observed between HPSE activity and HbA1C (data not shown). HPSE activity did show a moderate, near significant correlation with plasma creatinine levels (Fig. 6d).Fig. 6Plasma heparanase activity before and after treatment with pioglitazone in type 2 diabetes mellitus patients. ( a) Plasma heparanase activity of 38 type 2 diabetes mellitus patients before and after $\frac{16}{24}$ weeks treatment with pioglitazone depicted as individual values in a box plot to show the difference per quartile. ( b) Plasma heparanase expression levels of 38 type 2 diabetes mellitus patients before and after $\frac{16}{24}$ weeks treatment with pioglitazone depicted as individual values in a box plot to show the difference per quartile. ( c) Plasma heparanase activity categorised according to the presence of HbA1C above or below $6.5\%$ upon treatment with pioglitazone for $\frac{16}{24}$ weeks. ( d) Correlation between plasma heparanase activity and creatinine levels after $\frac{16}{24}$ weeks of treatment with pioglitazone ($$n = 31$$). Data are expressed as mean ± SEM.
## Discussion
In this study, we showed that PPARɣ regulates the expression of HPSE in cultured podocytes, cultured glomerular endothelial cells, healthy rats and in a rat model for human FSGS. In agreement with our previous studies, rats showed increased glomerular HPSE expression and reduced glomerular HS expression after induction of AN.31,38 Treatment with the PPARɣ agonist pioglitazone normalized glomerular HPSE and HS expression, whereas PPARɣ antagonism induced glomerular HPSE expression and reduced glomerular HS expression. Importantly, in line with the induction of proteinuria in vivo, we showed that PPARɣ antagonism induced transendothelial albumin passage in vitro in a HPSE-dependent manner. In addition, we showed that the PPARɣ antagonist GW9662 induced HPSE promoter activity, suggesting that the transcription factor PPARɣ directly suppresses HPSE transcription.
The present study shows that PPARɣ is a negative regulator, i.e. repressor, of glomerular HPSE. A previous study revealed that PPARɣ inhibited hepatocellular carcinoma migration by downregulating pro-metastatic genes, including HPSE by direct binding to the HPSE promoter region.14 We now show direct binding of PPARɣ to the HPSE promoter in mouse podocytes and glomerular endothelial cells. Adriamycin treatment significantly reduced the binding of PPARɣ to the HPSE promoter in podocytes, and a similar trend was observed for glomerular endothelial cells. PPARɣ agonism inhibits the decline in PPARɣ binding to the HPSE promoter upon adriamycin treatment. In addition, treatment of podocytes and glomerular endothelial cells with the PPARɣ antagonist GW9662 tended to reduce PPARɣ binding to the HPSE promoter. These data suggest that in podocytes and most likely also in glomerular endothelial cells, PPARɣ represses gene expression potentially through co-repressors such as the nuclear receptor corepressor (NCoR) or the silencing mediator of retinoid and thyroid hormone receptors (SMRT) via histone deacetylases and subsequent transcriptional repression of HPSE.39 Our in vitro results support the hypothesis that PPARɣ regulates HPSE gene expression. Although both mPC-5 and ciGEnC increased HPSE mRNA expression upon treatment with adriamycin, the HPSE mRNA expression from mGEnC-1 decreased upon adriamycin treatment after 16 h. Previously, we observed that the initial response of mGEnC-1 to adriamycin is an increased HPSE mRNA expression in the first hours after exposure, whereas HPSE mRNA expression is decreased after 16 h. However, the HS expression by mGEnC-1 is still decreased after 16 h of adriamycin treatment, corresponding to an increased albumin passage through mGEnC-1 monolayers.
PPARɣ agonists, like the TZDs pioglitazone and rosiglitazone, have already been clinically applied for a few decades. As they reduce insulin resistance, they are mainly used for the treatment of patients with type 2 diabetes.4,5 However, a number of studies also suggest that PPARɣ agonists have direct renoprotective effects.6,7,12,13 HPSE has been shown to be essential for the development of diabetic nephropathy, and type 2 diabetes patients show increased levels of HPSE activity.18,37 We showed that plasma HPSE activity in these pioglitazone treated patients was related to their HbA1c level and correlating with serum creatinine levels. Even though no clear effect on plasma HPSE activity could be observed upon pioglitazone treatment, a decrease of HPSE activity in the renal cortex cannot be omitted. Since treatment with pioglitazone and rosiglitazone both reduced proteinuria in patients with non-diabetic renal diseases,12,13,40 TZDs could be considered for treatment of proteinuria in non-diabetic renal diseases as well. A major drawback of TZDs is the development of severe side effects, such as fluid retention, edema formation, cardiac failure and an increased risk for bone fractures.41 *With this* study, we provide further mechanistic in vitro and rat in vivo evidence that TZDs have direct renoprotective effects, by repressing glomerular HPSE expression and activity.
In addition to HPSE, several mechanisms have been described that may explain the renoprotective effects of PPARɣ agonists. A previous study showed that the PPARɣ agonist rosiglitazone partially reduced proteinuria by preserving the expression of the slit diaphragm proteins nephrin, podocin, and CD2AP in rats with AN.9 More recently, we showed that PPARɣ agonists reduced the expression of the slit diaphragm protein transient receptor potential channel C6 (TRPC6) in podocytes.11 Previous studies showed that glomerular TRPC6 expression is increased in several proteinuric diseases, suggesting that TRPC6 plays a role in the development of proteinuric diseases.42 PPARɣ negatively regulated TRPC6 expression by direct binding to the TRPC6 promoter, and thereby reduced podocyte damage and the development of proteinuria in rats with AN.11 In addition to the slit diaphragm proteins TRPC6, nephrin, podocin and CD2AP, PPARɣ also reduced the expression of transforming growth factor-β (TGF-β), endothelin-1 and the renin-angiotensin-aldosterone system (RAAS), and increased the bioavailability of nitric oxide.43, 44, 45, 46, 47, 48 Interestingly, all these aforementioned factors are involved in the regulation of HPSE as well.38,49, 50, 51 Together, there appear to be several mechanisms underlying the renoprotective effects of PPARɣ agonists.
There are also some limitations in our study. First, the patient samples are from T2DM patients, while pioglitazone is known to have a glucose-lowering effect and lower blood glucose level may affect HPSE expression and/or activity as well.52,53 However, the animal model that was used in this study was not a diabetes model. Second, we only assessed HPSE expression levels but not the more relevant HPSE activity for the in vitro models because we were unable to measure this for the endothelial cells. Third, the route of administration of pioglitazone can be discussed; we have administered pioglitazone via i.p injection. Although pioglitazone is administered orally in patients, others have administered pioglitazone via i.p injection in animal studies as well.54 Fourth, the use of cell lines in mono-culture is not ideal, since it is well known that the cross-talk between podocytes and endothelial cells play a key role in disease manifestation.55, 56, 57 Future experiments should thus focus on the development of co-cultures or organ-on-a-chip technologies able to test the effects of e.g. PPARy agonism on such interactions. Finally, we only focused on the glomerular cells as possible source and target of HPSE, whereas it is well known that various cell-types, including non-glomerular endothelial cells and immune cells, can be a source of HPSE and various cell types and the extracellular matrix can be a target of HPSE.53,58 Our current study provides an additional mechanism for the renoprotective effects of pioglitazone and other PPARɣ agonists. By reducing glomerular HPSE expression and activity, glomerular HS expression is preserved and the development of proteinuria is prevented. In the past, AN was mainly regarded as a podocyte damage model for human FSGS. However, more recent studies showed that glomerular endothelial cells play a crucial role in the development of AN, as the glomerular endothelial glycocalyx thickness is reduced by $80\%$ in mice with AN, and HS expression on cultured mouse glomerular endothelial cells is reduced by Adriamycin.31,59 Additionally, a recent study showed that endothelial cell damage precedes podocyte damage in AN,60 further highlighting the importance of the glomerular endothelium in the development of AN. Our current data suggest that PPARɣ agonists have direct protective effects on both glomerular endothelial cells and podocytes, by reducing HPSE expression and transendothelial albumin passage.
In conclusion, our study suggests that PPARɣ agonists like pioglitazone reduce proteinuria by inhibiting glomerular HPSE expression, thereby providing an additional mechanism explaining the anti-proteinuric and renoprotective effects of thiazolidinediones in clinical practice.
## Contributors
TN, GK and JvdV designed the study; TN, GK, JAAMK, JLH, TR and JvdV obtained funding and supervised the study; THvK provided crucial reagents; MG, BB, MS, LG, RS, and MdG carried out experiments; MG, BB, MS, LG, and MMH drafted the paper and the figures; MG, MS, LG, BB, TN, GK and JvdV accessed and verified the data; JvdB, JAAMK, DHvR, RWvdM and HJL provided the patient materials. All authors revised and approved the final version of the manuscript.
## Data sharing statement
This study did not generate any new unique reagents, datasets or code. The main data supporting the findings of this study are available within the paper. Further information and requests for resources and reagents should be directed to the corresponding author.
## Declaration of interests
The authors declare that there are no conflicts of interest.
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|
---
title: Association of Dementia Risk With Focal Epilepsy and Modifiable Cardiovascular
Risk Factors
authors:
- Xin You Tai
- Emma Torzillo
- Donald M. Lyall
- Sanjay Manohar
- Masud Husain
- Arjune Sen
journal: JAMA Neurology
year: 2023
pmcid: PMC10043806
doi: 10.1001/jamaneurol.2023.0339
license: CC BY 4.0
---
# Association of Dementia Risk With Focal Epilepsy and Modifiable Cardiovascular Risk Factors
## Abstract
This cross-sectional study examines data from the UK Biobank to explore the association between focal epilepsy compared with stroke or migraine and the risk of developing dementia and how that risk is affected by modifiable cardiovascular risk factors.
## Key Points
### Question
To what extent does having focal epilepsy compared with stroke or migraine increase the risk of developing dementia, and how is it affected by modifiable cardiovascular risk factors?
### Findings
This cross-sectional study of 495 149 participants aged 38 to 72 years without dementia at baseline demonstrated that participants with epilepsy and high cardiovascular risk were more than 13 times more likely to develop dementia compared with control participants with low cardiovascular risk while participants with stroke and high cardiovascular risk were almost 6 times more likely to develop dementia. Having epilepsy was associated with higher incident dementia risk than a history of stroke.
### Meaning
This study found that epilepsy was associated with a significant risk of developing dementia, which was magnified substantially by cardiovascular risk.
### Importance
Epilepsy has been associated with cognitive impairment and potentially dementia in older individuals. However, the extent to which epilepsy may increase dementia risk, how this compares with other neurological conditions, and how modifiable cardiovascular risk factors may affect this risk remain unclear.
### Objective
To compare the differential risks of subsequent dementia for focal epilepsy compared with stroke and migraine as well as healthy controls, stratified by cardiovascular risk.
### Design, Setting, and Participants
This cross-sectional study is based on data from the UK Biobank, a population-based cohort of more than 500 000 participants aged 38 to 72 years who underwent physiological measurements and cognitive testing and provided biological samples at 1 of 22 centers across the United Kingdom. Participants were eligible for this study if they were without dementia at baseline and had clinical data pertaining to a history of focal epilepsy, stroke, or migraine. The baseline assessment was performed from 2006 to 2010, and participants were followed up until 2021.
### Exposures
Mutually exclusive groups of participants with epilepsy, stroke, and migraine at baseline assessment and controls (who had none of these conditions). Individuals were divided into low, moderate, or high cardiovascular risk groups based on factors that included waist to hip ratio, history of hypertension, hypercholesterolemia, diabetes, and smoking pack-years.
### Main Outcomes and Measures
Incident all-cause dementia; measures of executive function; and brain total hippocampal, gray matter, and white matter hyperintensity volumes.
### Results
Of 495 149 participants (225 481 [$45.5\%$] men; mean [SD] age, 57.5 [8.1] years), 3864 had a diagnosis of focal epilepsy only, 6397 had a history of stroke only, and 14 518 had migraine only. Executive function was comparable between participants with epilepsy and stroke and worse than the control and migraine group. Focal epilepsy was associated with a higher risk of developing dementia (hazard ratio [HR], 4.02; $95\%$ CI, 3.45 to 4.68; $P \leq .001$), compared with stroke (HR, 2.56; $95\%$ CI, 2.28 to 2.87; $P \leq .001$), or migraine (HR, 1.02; $95\%$ CI, 0.85 to 1.21; $$P \leq .94$$). Participants with focal epilepsy and high cardiovascular risk were more than 13 times more likely to develop dementia (HR, 13.66; $95\%$ CI, 10.61 to 17.60; $P \leq .001$) compared with controls with low cardiovascular risk. The imaging subsample included 42 353 participants. Focal epilepsy was associated with lower hippocampal volume (mean difference, −0.17; $95\%$ CI, −0.02 to −0.32; t = −2.18; $$P \leq .03$$) and lower total gray matter volume (mean difference, −0.33; $95\%$ CI, −0.18 to −0.48; t = −4.29; $P \leq .001$) compared with controls. There was no significant difference in white matter hyperintensity volume (mean difference, 0.10; $95\%$ CI, −0.07 to 0.26; $t = 1.14$; $$P \leq .26$$).
### Conclusions and Relevance
In this study, focal epilepsy was associated with a significant risk of developing dementia, to a greater extent than stroke, which was magnified substantially in individuals with high cardiovascular risk. Further findings suggest that targeting modifiable cardiovascular risk factors may be an effective intervention to reduce dementia risk in individuals with epilepsy.
## Introduction
Epilepsy is a common neurological condition characterized by unprovoked seizures. The incidence of epilepsy is highest in older life and progressively increases after 55 years of age.1,2,3 As individuals with epilepsy age, studies suggest an increased risk of cognitive impairment and potentially dementia.4,5 However, the extent to which epilepsy affects dementia risk and potential underlying mechanisms remains unclear. In addition, there are no specific clinical guidelines around mitigating the risk of dementia in epilepsy.
While recent evidence has highlighted a shared pathology link of tau accumulation in epilepsy and dementia,6,7,8 another intriguing line of inquiry is the role of modifiable cardiovascular risk factors that contribute to dementia risk in the general aging population.9,10 Established stroke is a risk factor for developing epilepsy in older adults11; however, the effect of upstream cardiovascular risk factors is less clear with conflicting findings.12,13 Similarly, while poststroke epilepsy is considered predictive of cognitive outcomes,14 how dementia risk in epilepsy may change according to an individual’s burden of modifiable cardiovascular risk factors in the absence of stroke remains unknown.
A recent systematic review5 identified disease duration, seizure frequency, and antiseizure medication use as potential predictors of cognitive impairment in epilepsy, although studies were limited by small samples of patients and controls and the lack of consideration for lifestyle and cardiovascular risk factors.15,16,17 Correspondingly, a meta-analysis examining dementia risk in epilepsy identified similar limitations and was unable to calculate period prevalence owing to insufficient pooled sample size.3 To guide clinical management, it is important to understand the epilepsy-related dementia risk compared with other neurological conditions to provide comparator context for clinical decisions.
In this study, we analyzed the risk associated with developing dementia across a range of neurological conditions in the UK Biobank prospective cohort. Specifically, our aim was to determine the extent to which focal-onset epilepsy is associated with risk of developing dementia compared with individuals with stroke or migraine, 2 other nondegenerative neurological conditions, as well as healthy controls. We hypothesized that epilepsy would be associated with higher dementia incidence than migraine and controls but less than stroke, which is strongly linked to vascular cognitive impairment and dementia. Further, we determined the extent to which having low cardiovascular risk is associated with reduced risk of dementia in epilepsy as this may help develop dementia risk reduction strategies for people with epilepsy.
## Methods
This cross-sectional study is based on data from the UK Biobank, a population-based cohort of more than 500 000 participants aged 38 to 72 years who underwent physiological measurements and cognitive testing and provided biological samples at 1 of 22 centers across the United Kingdom between 2006 and 2010.18 A subset of participants reattended for brain imaging between 2014 and 2020.19 All participants provided written informed consent. UK Biobank received approval from the North West Multicenter Research Ethics Committee. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
The primary study objective was to investigate the risk of incident dementia associated with having focal epilepsy compared with stroke or migraine and healthy controls at baseline study assessment. Participants with prevalent dementia at baseline assessment (<$1\%$) were excluded as were those with other neurological conditions, including a history of central nervous system infection, encephalitis, meningitis, amyotrophic lateral sclerosis, multiple sclerosis, Parkinson disease, or previous subdural or subarachnoid hemorrhage. Baseline diagnoses were identified using self-report and hospital inpatient records (UK Biobank codes are found in eTable 1 in Supplement 1).
## Dementia and Focal Epilepsy Diagnoses
All-cause dementia cases were identified during longitudinal follow-up from hospital inpatient records using codes from the International Classification of Diseases, Ninth Revision (ICD-9), and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), for Alzheimer disease and other dementia classifications or from death register linkage data as an underlying or contributory cause.
The epilepsy subgroup was restricted to focal-onset, nongenetic epilepsy at baseline based on ICD codes. We excluded individuals coded for a genetically associated epilepsy, including “generalized idiopathic epilepsy and epileptic syndromes” (G40.3), such as syndromes of juvenile myoclonic epilepsy and childhood absence epilepsies, and “other generalized epilepsy and epileptic syndromes” (G40.4), which includes epileptic encephalopathies such as Lennox-Gaustaut and West syndrome, because these can be associated with clear cognitive deficits related to an underlying genetic mechanism or developmental delay.20 To investigate the potential confounding effects of antiseizure medication, we compared the association between cognitive scores and number of antiseizure medications (list in eTable 2 in Supplement 1) for individuals with a diagnosis of focal epilepsy and those without a diagnosis but taking the medication for another reason. Information on seizure onset zone in the brain was not available; however, the majority of focal-onset, acquired epilepsy may be of temporal lobe origin.21
## Cardiovascular Risk Score
Cardiovascular risk was assessed based on a previously published score22 with a point given for a diagnosis or being treated for hypertension, high cholesterol, or diabetes (1 point for each condition), waist to hip ratio (1 point if greater than the sex-specific threshold set by World Health Organization guidelines),23 smoking pack-years (1 point if more than 20 pack-years), and APOE e4 allele status (2 points for 2 e4 alleles, 1 for a single e4 allele, and 0 for any other allele combination or when allele status was unclear). Participants were placed in low- (a score of 0), moderate- (1 and 2), or high-risk (≥3) groups based on quintile boundaries 1, 2 to 4, and 5, respectively.
## Cognitive Testing
UK Biobank cognitive testing was computer-based and performed at the initial baseline visit, repeated at the imaging visit, and taken via online questionnaire. Not all cognitive tasks were performed at each instance while some were repeated. We analyzed data from 5 tasks of working memory or speed of processing and used the first available time point data. This included a pairs-matching and snap reaction time, trail-making, tower-rearranging, and symbol-digit substitution tasks and has been described in detail elsewhere.24 Reliability and retest effects over time for these cognitive tasks have been previously assessed.25
## Main Covariates
All full models were adjusted for age (continuous), sex (female vs male), education (categorized as higher [college or university degree or other professional qualification], upper secondary [second or final stage of secondary education], lower secondary [first stage of secondary education], vocational [work-related qualifications], or other), socioeconomic status (categories derived from Townsend deprivation index26 quintiles 1, 2 to 4, and 5), and cardiovascular risk group.
## Brain Imaging Variables
Magnetic resonance imaging (MRI) data were acquired on a Skyra 3-T scanner (Siemens), including high-resolution, T1-weighted, 3-dimensional magnetization-prepared gradient echo structural images and T2-weighted fluid-attenuated inversion recovery images. Full imaging protocols and processing pipeline have been previously described.27 We used imaging summary statistics of total hippocampal, gray matter, and white matter hyperintensity volumes. These regions were chosen because previous epilepsy studies have found hippocampal atrophy28,29,30 and hippocampal tau deposition.6 Total gray matter volume is a useful measure of widespread, global change while white matter hyperintensity is a useful marker of vascular burden. Median absolute deviation was used to exclude outliers, and volumes were adjusted for potentially confounding baseline measures of age, age squared, head size, and imaging site.27
## Statistical Analysis
Confirmatory factor analysis (CFA) was performed on cognitive variables to produce a continuous, summary latent measure of working memory and reaction time, which we termed executive function for simplicity (this method has been previously described).22,24 In brief, cognitive variables were preprocessed to correct for heavily skewed distribution prior to CFA. Standard fit indices were measured with higher comparative fit index and Tucker-Lewis index considered better (>0.9 are commonly used as acceptable fit cutoffs) while lower root mean square error of approximation and standardized root mean square error residual are considered better (<0.06 and <0.08, respectively, are commonly used cutoffs for acceptable fit). Estimating a latent variable has the methodological advantage of controlling measurement error that can artificially reduce the relationship between measured variables in standard univariate analyses.31 Missing cognitive data were estimated using full information maximum likelihood, which gives unbiased parameter estimates and standard errors.
We examined mutually exclusive groups of focal epilepsy, stroke, and migraine. Combinations of conditions, such as having epilepsy and stroke, were not reported because of the small sample sizes. The association between executive function across age was examined for each group. We then controlled for age in 2 different ways for robustness: using a model-free, sliding window approach with fixed age-quantile widths moved along the age distribution (described previously24,32,33) or by including age as a covariate in a general linear model along with other baseline characteristics. We compared the difference in executive function between condition groups using analysis of variance with post hoc *Tukey analysis* to account for pairwise or multiple comparisons. P values were 2-sided with statistical significance set at $P \leq .05$ for all analyses. Brain measures were analyzed in the epilepsy subgroup using the same methods.
Hazard ratios (HRs) were calculated using Cox proportional hazards regression models with time to incident all-cause dementia as the dependent variable. We calculated HRs for mutually exclusive groups of focal epilepsy, stroke, and migraine compared with none of these conditions as the baseline. For our main model, we tested the dementia risk associated with having focal epilepsy, stroke, or neither condition, stratified by cardiovascular risk groups (9 categories with low cardiovascular risk and neither epilepsy or stroke as the baseline). The migraine subgroup had similar HRs to those for the control group from initial testing and was not included in this model. For the main exposures and covariates, there were less than $3\%$ missing or not known data, and complete case analysis was applied. Participants were considered at risk for dementia from baseline until the date of first diagnosis, death, loss to follow-up, or last surveyed hospital admission date (March 31, 2021, for England and Scotland and February 28, 2018, for Wales), whichever came first. These censoring dates were recommended by UK Biobank as the data were estimated to be more than $90\%$ complete in England, Scotland, and Wales.
Secondary data analyses examined dementia risk with different follow-up durations of 10 years and 5 to 14 years to consider earlier risk of developing dementia and potential reverse causality, respectively. Further sensitivity analysis considered dementia risk associated with stroke and epilepsy stratified by a cardiovascular risk score that did not include APOE e4 genetic information to consider only modifiable lifestyle risk. The association between dementia risk and epilepsy controlling for number of antiseizure medications was investigated within the epilepsy subgroup only. Analyses were done in MATLAB R2018a or in R version 4.0.3 using the lavaan34 or survival package.
## Results
The UK Biobank cohort comprised 502 536 participants at baseline. After excluding those who did not meet the inclusion criteria ($$n = 7216$$) and those with prevalent dementia at baseline ($$n = 120$$), our study included 495 149 individuals (eFigure 1 in Supplement 1). Participants had a mean (SD) age of 57.5 (8.1) years, and 250 752 ($54.5\%$) were female (Table). Over 5 803 006 total follow-up years (median, 12.0 years; IQR, 11.2-12.7), 6115 cases of all-cause incident dementia were observed.
**Table.**
| Characteristic | No. (%)a | No. (%)a.1 | No. (%)a.2 | No. (%)a.3 | No. (%)a.4 | No. (%)a.5 | No. (%)a.6 | No. (%)a.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Characteristic | Control group | Control group | Epilepsy | Epilepsy | Stroke | Stroke | Migraine | Migraine |
| Characteristic | No incident dementia | Incident dementia | No incident dementia | Incident dementia | No incident dementia | Incident dementia | No incident dementia | Incident dementia |
| No. of participants | 464 138 | 5471 | 4121 | 198 | 6679 | 337 | 15 835 | 174 |
| Age, mean (SD), y | 57.4 (8.1) | 65.3 (4.8) | 57.1 (8.1) | 63.5 (6.1) | 61.5 (6.9) | 65.4 (4.7) | 56.1 (7.8) | 63.7 (6.2) |
| Sex | | | | | | | | |
| Female | 250 752 (54.0) | 2619 (47.9) | 2071 (50.3) | 77 (38.9) | 2766 (41.4) | 119 (35.3) | 12 363 (78.1) | 117 (67.2) |
| Male | 213 386 (46.0) | 2852 (52.1) | 2050 (49.7) | 121 (61.1) | 3913 (58.6) | 218 (64.7) | 3472 (21.9) | 57 (32.8) |
| Educationb | | | | | | | | |
| Higher | 217 148 (46.8) | 1813 (33.1) | 1623 (39.4) | 55 (27.8) | 2257 (33.8) | 86 (25.5) | 7810 (49.3) | 53 (30.4) |
| Upper secondary | 58 464 (12.6) | 550 (10.1) | 547 (13.3) | 23 (11.6) | 807 (12.1) | 33 (9.8) | 1939 (12.2) | 17 (9.8) |
| Lower secondary | 25 227 (5.4) | 253 (4.6) | 202 (4.9) | 10 (5.1) | 302 (4.5) | 19 (5.6) | 919 (5.8) | 11 (6.3) |
| Vocational | 77 047 (16.6) | 746 (13.6) | 708 (17.1) | 23 (11.6) | 1045 (15.6) | 42 (12.5) | 2793 (17.6) | 22 (12.6) |
| Other | 86 252 (18.6) | 2109 (38.5) | 1047 (25.4) | 85 (42.9) | 2268 (34.0) | 157 (46.6) | 2374 (15.0) | 71 (40.8) |
| Socioeconomic status quintilec | | | | | | | | |
| 1 (Least deprived) | 93 326 (20.1) | 990 (18.1) | 641 (15.5) | 28 (14.1) | 959 (14.4) | 39 (11.6) | 3161 (20.0) | 31 (17.8) |
| 2-4 | 278 742 (60.1) | 3151 (57.6) | 2317 (56.2) | 96 (48.5) | 3629 (54.3) | 176 (52.2) | 9588 (60.5) | 91 (52.3) |
| 5 (Most deprived) | 91 495 (19.7) | 1323 (24.2) | 1166 (28.2) | 74 (37.4) | 2084 (31.2) | 122 (36.2) | 3061 (19.3) | 52 (29.9) |
| | 575 (0.1) | 7 (0.1) | 2 (0.05) | 0 | 7 (0.1) | 0 | 25 (0.2) | 0 |
| Cardiovascular risk group (score)d | | | | | | | | |
| Low (0) | 127 055 (27.3) | 495 (9.0) | 997 (24.2) | 21 (10.6) | 531 (8.0) | 13 (3.9) | 5490 (34.7) | 31 (17.8) |
| Moderate (1-2) | 258 527 (55.7) | 2695 (49.3) | 2309 (56.0) | 91 (46.0) | 2700 (40.4) | 97 (28.8) | 8585 (54.2) | 87 (50.0) |
| High (≥3) | 78 556 (16.9) | 2281 (41.7) | 815 (19.8) | 86 (43.4) | 3448 (51.6) | 227 (67.4) | 1760 (11.1) | 56 (32.2) |
At baseline assessment, 3864 participants ($0.78\%$) had a diagnosis of focal epilepsy only, 6397 ($1.3\%$) had a history of stroke only, and 14 518 ($2.93\%$) had migraine only. There were 134 249 ($27.1\%$) participants with low cardiovascular risk while 274 098 ($55.4\%$) and 86 802 ($17.5\%$) had a moderate and high cardiovascular risk, respectively (eTable 3 in Supplement 1).
## Executive Function Between Patient Groups and Controls
A continuous cognitive function latent variable of executive function was estimated from 5 cognitive tasks of working memory or speed of processing (model and fit indices are shown in eFigure 2 in Supplement 1). Executive function declined uniformly with age among all groups, consistent with previous findings (Figure 1).22,24 We controlled for age using a model-free residual method and showed that focal epilepsy was associated with lower executive function than controls and migraine (mean difference, −0.09; $95\%$ CI, −0.07 to −0.10; $t = 14.70$; $P \leq .001$, and mean difference, −0.08; $95\%$ CI, −0.10 to −0.10; t = −12.82, $P \leq .001$, respectively, using post hoc Tukey analysis). There was no significant difference between executive function in focal epilepsy or stroke (mean difference, 0.01; $95\%$ CI, −0.01 to 0.02; $t = 14.70$; post hoc Tukey $$P \leq .91$$) (eTable 4 in Supplement 1). The associations between executive function and neurological conditions remained consistent with a fully adjusted linear model accounting for other baseline characteristics (eTable 5 in Supplement 1).
**Figure 1.:** *Association Between Executive Function and Age in Participants With Epilepsy, Stroke, or Migraine and Control ParticipantsParticipants with epilepsy and stroke had lower executive function (z-scored values) across all ages compared with controls with no history of epilepsy, stroke, or migraine (A). The shaded areas represent standard error. When adjusting for age (B) using an age-residual approach, there was a group difference (F3,489 069 = 201.97, P < .001). Post hoc pairwise comparison showed that having epilepsy or stroke was associated with significantly lower executive function compared with controls (t = 14.70, P < .001, and t = 19.90, P < .001, respectively, using post hoc Tukey analysis) and participants with migraine (t = −12.82, P < .001, and t = −16.35, P < .001, respectively). There was no significant difference in executive function between participants with migraine and controls (Tukey analysis P = .91) or between participants with epilepsy or stroke (Tukey analysis P = .91). All groupings shown were mutually exclusive; ie, the epilepsy group had no history of stroke or migraine.*
## Dementia Outcomes
During the study follow-up, the adjusted HR for incident dementia was 4.02 ($95\%$ CI, 3.45-4.68) for participants with focal epilepsy (Figure 2), which was higher than stroke (HR, 2.56; $95\%$ CI, 2.28-2.87) and migraine (HR, 1.02; $95\%$ CI, 0.85-1.21). Within moderate and high cardiovascular risk groups, having focal epilepsy was associated with a higher risk of developing dementia compared with stroke (Figure 3). Of participants with high cardiovascular risk and focal epilepsy, 69 of 762 ($9.1\%$) developed dementia compared with 523 of 132 716 ($0.4\%$) control participants with low cardiovascular risk (HR, 13.66; $95\%$ CI, 10.61-17.60). When considering only individuals with high cardiovascular risk, those with focal epilepsy had an HR of 3.73 ($95\%$ CI, 2.94-4.76) for developing dementia compared with controls while participants with stroke had an HR of 1.90 ($95\%$ CI, 1.65-2.20) compared with controls. Risks of dementia within moderate and low cardiovascular risk groups are detailed in eTable 6 in Supplement 1).
**Figure 2.:** *Risk of Incident Dementia by Neurological Disease Status at BaselineThe control group had no migraine, stroke, or epilepsy. The model was adjusted for age, sex, education, socioeconomic status, and assessment center.* **Figure 3.:** *Risk of Incident Dementia Associated With Focal Epilepsy and Stroke According to Cardiovascular (CVS) RiskThe model was adjusted for age, sex, education, socioeconomic status, and assessment center.*
## Sensitivity Analysis
Similar patterns of association were observed with epilepsy and stroke when considering risk of incident dementia at 10 years after the baseline assessment and a follow-up period from 5 to 14 years, which was performed to mitigate potential for reverse causation or undetected dementia at baseline (eTable 7 in Supplement 1). There were 3804 cases of dementia identified for the 10-year follow-up period after baseline assessment and 5110 cases identified for the 5- to 14-year follow-up period. To examine modifiable risk factors more closely, we considered a cardiovascular risk score that did not include genetic APOE e4 genotype but instead controlled for the APOE e4 genetic risk status in the model. The magnitude of association between each condition and dementia risk was attenuated, but with a similar overall pattern, as participants with high cardiovascular risk and epilepsy had an HR of 7.53 ($95\%$ CI, 5.62-10.10) compared with the baseline group of low cardiovascular risk and no epilepsy (eFigure 3 in Supplement 1).
## Epilepsy Subanalyses
Considered an index of epilepsy disease severity, taking more antiseizure medications was associated with lower executive function in those with focal epilepsy (eFigure 4 in Supplement 1). To control for the possibility that the medications themselves might be affecting cognition, we also examined individuals taking antiseizure medications who did not have a diagnosis of epilepsy. In this group, the association was not present (evidenced by a significant interaction between having a diagnosis of focal epilepsy and number of antiseizure medications, $t = 2.19$, $$P \leq .03$$). The median age at focal epilepsy onset was 25.5 years, which indicated that seizure onset in this epilepsy cohort generally started during adulthood. Despite the use of more antiseizure medications being associated with a lower executive function, using more antiseizure medications was not associated with a higher risk of developing dementia (eTables 8 and 9 in Supplement 1). The risk of developing dementia associated with late-onset focal epilepsy (age ≥50 years) was comparable (HR, 2.82; $95\%$ CI, 1.97-4.04) with the risk associated with early-onset focal epilepsy (age <50 years: HR, 2.46; $95\%$ CI, 1.93-3.13) (eTable 10 in Supplement 1).
## Brain Structure Analysis
Focal epilepsy was associated with lower hippocampal volume (mean difference, −0.17; $95\%$ CI, −0.02 to −0.32; t = −2.18; $$P \leq .03$$) and lower total gray matter volume (mean difference, −0.33; $95\%$ CI, −0.18 to −0.48; t = −4.29; $P \leq .001$) compared with controls (Figure 4). However, there was no significant difference in white matter hyperintensity volume (mean difference, 0.10; $95\%$ CI, −0.07 to 0.26; $t = 1.14$; $$P \leq .26$$). Across age, the difference in hippocampal volume was more apparent in older individuals with focal epilepsy. A fully adjusted linear regression model accounting for other baseline characteristics and cardiovascular risk demonstrated the same pattern of associations (eTable 11 in Supplement 1).
**Figure 4.:** *Total Hippocampal Volume, Total Gray Matter Volume, and White Matter Hyperintensity Volume in Participants With Focal Epilepsy and Control ParticipantsTotal hippocampal volume was lower in older individuals with focal epilepsy while total gray matter volume was lower in individuals with focal epilepsy of all ages. When regressing out the effect of age, having epilepsy was significantly associated with lower total hippocampal (mean difference, −0.17; 95% CI, −0.02 to −0.32; t = −2.18; P = .03) and gray matter volume (mean difference, −0.33; 95% CI, −0.18 to −0.48; t = −4.29; P < .001). No significant difference in white matter hyperintensity was found between individuals with epilepsy and controls (mean difference, 0.10; 95% CI, −0.07 to 0.26; t = 1.14; P = .26).*
## Discussion
This study showed that having focal epilepsy was associated with worse cognitive function in mid- to late-life individuals compared with controls. Furthermore, by leveraging a large epilepsy cohort with longitudinal data on dementia outcomes, we showed a higher dementia risk in individuals with focal epilepsy compared with those with stroke, which was substantially worse in those with greater cardiovascular risk burden. Focal epilepsy was associated with widespread structural brain change reflected by lower total hippocampal and total gray matter volume.
Several studies have demonstrated worse cognition in epilepsy as individuals grow older,15,17,35,36,37 although these are mostly cross-sectional and have generally been small in sample size with fewer than a hundred individuals. One study38 showed objective executive function impairment in a larger epilepsy group ($$n = 257$$) prior to starting antiseizure medication. With a sample size an order of magnitude larger than previous studies, our current investigation identified worse cognitive function throughout mid- to late-life individuals with focal epilepsy compared with healthy controls, which was comparable with individuals who had a history of stroke. Taking more antiseizure agents was associated with worse cognition in those with focal epilepsy, which may reflect severity of disease in addition to potential for medication adverse effects. Our findings offer an important contribution to understanding the cognitive impact of epilepsy at a group level.
Few studies have suggested epilepsy as a risk factor for dementia39,40,41,42,43,44 while other investigations did not find an association.45,46,47 Stefanidou et al48 identified an increased dementia risk among 43 people with epilepsy in the Framingham Heart study (HR, 1.9; $95\%$ CI, 1.11-3.57) compared with controls, which was slightly lower than our findings. In such studies, cardiovascular risk factors may be controlled for in analyses models or not considered at all. To investigate the potential effect of cardiovascular burden, we stratified individuals based on a previously published cardiovascular risk score22 and found more than a 13-fold increased risk of dementia in individuals who have high cardiovascular risk and epilepsy compared with those with no epilepsy and low cardiovascular risk. This increased dementia risk was greater than that of stroke. The lack of association with number of prescribed antiseizure medications excludes an important potential confounder because some older antiseizure medications are also associated with increased vascular risk markers.49 Dementia risk associated with early-onset and late-onset epilepsy was comparable in our study, which is consistent with other investigations showing that measures of epilepsy disease duration did not correspond with worse cognitive impairment50 or tau pathology burden.6 The association between epilepsy and dementia may represent shared risk factors between epilepsy and a vascular dementia-like process51 or an interplay between mixed underlying pathology, which is increasingly reported in dementia.52,53 In either event, our findings highlight a key clinical message that cardiovascular risk factor modification may be critical for managing cognitive outcomes in focal epilepsy.
Epilepsy neuroimaging studies have observed widespread structural changes in addition to hippocampal atrophy.28,29,30 The present study confirms this finding while incorporating important covariates such as education, socioeconomic status, and cardiovascular risk factors, which are often not considered. Structural changes at the whole brain level may reflect widespread network effects of epilepsy regardless of the focal onset of seizures.54,55 We did not find a statistically significant difference in white matter hyperintensity burden between individuals with epilepsy and controls, although this trend was present. The finding of widespread structural changes in this cohort is important to understand the effects of epilepsy on the brain.
## Limitations
Our study has several limitations. Because of the observational nature of this cohort, the association of greater incident dementia in epilepsy cannot be taken as causal. Medical information was based on hospital records, death certificate data, or self-report, which may be incorrect.56 We identified individuals with nongenetic or focal-onset epilepsy through medical coding; however, this may be inaccurate, and we do not have information on seizure origin, laterality, frequency, or presence of hippocampal sclerosis, which may have an impact on cognitive performance. Such epilepsy characteristics were not captured by the UK Biobank, which was designed to recruit healthy individuals with no single disease in focus. Results of specific clinical investigations, such as electroencephalograms, were not available but would be a beneficial addition to future data releases of the study.
Data from all participants in the UK Biobank, aged 38 to 72 years, were used to include as many diagnoses of epilepsy, stroke, and migraine as possible. We recognize that the younger participants may have a higher chance of developing dementia in later years past the follow-up period of this study. Despite adjustment for potential confounders, the relatively long follow-up period and additional analysis of dementia incidence from 5 to 14 years, there may be unmeasured confounders and potential for reverse causality. While we considered all-cause dementia risk in our study, examining the relationship with dementia subtypes would be interesting; however, subtypes are currently poorly captured in the UK Biobank, and dementia cases are likely to be Alzheimer disease, vascular, or a mixed picture.57 The UK Biobank cohort is generally considered healthy and likely to be from less deprived areas58; therefore, the effects of cardiovascular risk may be greater in a more representative cohort. Similarly, individuals with focal epilepsy from this cohort are less likely to have drug-resistant, uncontrolled epilepsy that may result in worse cognitive outcomes.
## Conclusions
In this study, focal epilepsy was significantly associated with worse cognitive performance, higher incident dementia risk, and widespread brain differences. Cardiovascular risk was associated with a substantially increased risk of dementia in people with focal epilepsy. Interventions targeting modifiable risk factors may offer an effective management strategy in preventing dementia in individuals with epilepsy.
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|
---
title: 'mHealth and e-Learning in health sciences curricula: a South African study
of health sciences staff perspectives on utilisation, constraints and future possibilities'
authors:
- Habib Noorbhai
- Tinuade Adekunbi Ojo
journal: BMC Medical Education
year: 2023
pmcid: PMC10043831
doi: 10.1186/s12909-023-04132-4
license: CC BY 4.0
---
# mHealth and e-Learning in health sciences curricula: a South African study of health sciences staff perspectives on utilisation, constraints and future possibilities
## Abstract
### Background
Over the last decade, developments in e-Learning and technologies are creating the groundwork for health sciences and medical education. Literature demonstrates that we have yet to reach any form of consensus about which indicators are needed to assess and teach quality health sciences and medical education through technology or innovation. There is, therefore, a greater need for a tool or platform that is properly constructed, validated and tested within health sciences.
### Methods
This paper presents a study, which is part of a larger research project assessing staff and students’ perceptions of the importance and relevance of different aspects of e-Learning and mHealth in health sciences curricula at four universities in South Africa. The specific objectives of this study were to: (i) assess health sciences staffs’ perceptions and understanding of these two applications; and (ii) establish challenges and opportunities of e-Learning and mHealth applications in the health sector, as well as perceptions on the importance and relevance of these applications to their curricula and future practices. A combination of Focus Group Discussions (FGDs) and a key-informant interview was used. A total of 19 staff from four universities participated. Atlast.ti was used for the data analysis and the findings were coded using a primarily deductive thematic coding framework.
### Results
The findings revealed that not all staff members are equipped or trained with new applications or technologies, such as mHealth. Most participants believed that diverse technologies and tools could be integrated with mHealth and e-Learning. Furthermore, participants agree that a new multi-modal platform, in the form of a learning management system (LMS) with relevant applications (and possible plugins) integrated, tailored towards health sciences will benefit all stakeholders, and be valuable to higher education and health sectors.
### Conclusions
Digitalisation as well as digital citizenship is gradually being integrated into teaching and learning. It is imperative to adapt the health sciences curricula through constructive alignments and promote health sciences education in the current 4IR. This would allow graduates to be better prepared for digitalised practice environments.
## Background
The health sector has witnessed several challenges in identifying and treating diseases in Africa. Scholars have debated the constraints affecting the health sciences, including the health faculties and medical schools in higher learning institutions of developing economies (low, middle-income countries [LMIC]) on addressing the burden of diseases, preventive medicine, and treatment due to inadequate facilities. Since the commencement of the Coronavirus-19 (COVID-19) pandemic, South Africa has witnessed the evolution of different conditions such as human immunodeficiency virus/acquired immunodeficiency syndrome and tuberculosis, diabetes, maternal and child mortality, injuries and violence, and non-communicable diseases amongst many [1, 2]. Understanding the relationships between ecosystem health and human health is paramount despite this epidemic and the epidemiology of diseases globally. Most significantly, it is recorded that the practice of medicine is more than identifying and treating disease; it includes a significant preventative component which requires innovative health care approaches, especially in limited-resource settings [3]. However, teaching and learning for this context have become a challenge due to the already overloaded curricula and inadequate infrastructures [4].
In addition, most South African health sciences faculties within universities are resource-constrained and do not foster a dynamic e-Learning and mobile health (mHealth) culture within their curricula. mHealth focuses on obtaining information immediately from mobile phones to diagnose illnesses, track diseases and provide timely information to the public in underserved countries. In addition, mHealth can also be used as a form of education for health sciences students. Embracing such a culture would create more job creation opportunities within the healthcare landscape. Some universities in South Africa, such as the University of Johannesburg, have started to integrate the ethos of the Fourth Industrial Revolution (4IR) but are yet to teach e-Learning (fully) within their health sciences curricula (and many other faculty curricula).
This paper aims to present health science staffs’ understanding and perceptions of the use, constraints and future possibilities of e-Learning, mHealth in their curriculum, which could significantly impact their future healthcare learning and practice post-COVID-19. In addition, given the unsuccessful rate of e-Learning or slow uptake within the health sciences area, the article presents the findings from in-depth interviews and focus group discussions from health sciences staff at four South African universities regarding the use and related constraints of mHealth and e-Learning applications, as well as possibilities for a multi-modal platform and curricula, in order to optimise learning (for students and academic staff) and healthcare (for patients) in South Africa.
## The concept of e-Learning
There appears to be no widely accepted definition of e-Learning. However, there is a convergence of describing what it entails: learning using e-Learning infrastructure, made up of both hardware and software, via various electronic devices and communication applications, requiring web/internet access. This form of learning can either fully replace the physical classroom (distance learning) or augment classroom-based teaching and learning and has come to be known as blended learning [5].
## e-Learning in South Africa
The pandemic came with a significant shift in higher learning systems, demanding alternative and flexible teaching and learning approaches in the digitalised economy. This led to a call for easy access to online learning platforms that offer electronic assisted educational facilities [6]. In addition, most universities faced the challenges of integrating eLearning into the existing traditional offerings as a form of blended learning. As a result, most academics have found themselves within the digitalised space in which they are expected to adopt and implement different teaching and learning approaches. However, academic and administrative developments are expanding, including the changes that will facilitate South African universities’ strategic orientations and adoption of e-Learning.
The digital context in South *Africa is* characterised by initially slow uptake and limited device usage to rapid expansion from 2014 onwards. Market needs vary, and unique challenges regarding affordability about the costs of mobile technology and accessibility of the World Wide Web compared to data costs relating to other costs (electricity cost, transportation costs). The statistic shows that the number of internet users in South Africa from 2014 to 2018 increased from 9.7 million users to 20.3 million users [7]. However, these numbers grew to 38.13 million in 2021 and have increased in 2022. Initially, internet users ranged from the South African middle class to the top rank. However, all class sectors and societal structural levels have demanded flexible educational services [6]. The study relates with the ‘constructivist learning theory, which entails identifying learning processes within the classroom environment [8]. The idea signifies a cognitive activity that generates mental models that represent perceptions of reality. The theory focuses on answering questions on what people know and the reason for existence. The constructive approach assists academics in solving teaching, learning, and development [8]. This theory focuses on the belief that solving problems aids students and scholars in thinking, education, and development. The approach allows envisaging the different unique experiences and expertise of the staff interviewed and proposes solutions for future learning engagements [8].
## e-Learning and classroom training
“Your schedule, pace, and place are the e-Learning motto” [9]. This is unfortunately not the case with classroom training. Contemporary studies have shown that the advantages of e-Learning compared to classroom training are emerging as wide-ranging. For example, e-Learning students retain the material significantly more than those in face-to-face instructor-led classes [9]; perform better than those who take contact courses [10] and demonstrate better comprehension [11]. Qazi et al.[12], further cite four benefits of e-Learning. The first refers to the issue of ‘cost difference’; e-*Learning is* cost-effective and affordable to many students, especially since universities are giving subsidies for data cost. Secondly, e-*Learning is* flexible and available $\frac{24}{7}$, eliminating travel distance and enabling students to save costs. The third benefit entails reporting and monitoring; another benefit of e-*Learning is* the Learning Management System (LMS) which gives instant feedback and tracks the learner’s progress online without any additional administrative help. The fourth benefit involves consistency of e-Learning service delivery, where online learning students can access conveniently, as per demand, through varied learning resources [12].
Even so, quite a few higher education institutions have made limited use of e-Learning as part of their learning system. However, arguments have been made that the scarcity of teaching e-Learning at higher education institutions today could be insufficient capital, inadequate knowledge on access and usage, poor skills or any number of unknown factors withholding the adoption of e-Learning. Despite these challenges, there is a view that a combination of traditional classrooms and e-Learning creates entirely new experiences for learners, and new business opportunities for business owners by increasing the number of learners to educate and potentially addressing the low matriculation to degree qualification transition ratio [13]. The scholars agreed that the traditional form of learning and the e-Learning model benefits more shareholders within the educational setting and corporate environment [6].
The growth in e-Learning technology no doubt adds to globalisation as educational institutions are trying their utmost to break down geographical and social boundaries to offer distance learning education [14]. This leads to integrations of academic standards and views. Introduction of e-Learning to the business model provides diversity, flexibility and additional revenue streams representing a mixed or blended learning model. Students are expected to participate in different online activities in a hybrid learning or blended model. The online activities include discussions, online assessment, group work, and online projects replacing face-face teaching and learning [14].
e-Learning facilitates equitable and easy access to digital learning resources [14]. Despite being an additional learning resource that enhances access to learning tools, most families and teachers cannot afford the data necessary to sustain e-Learning activities [14].
## mHealth to fill curricula gaps in the health sciences
Developments in e-Learning technologies have set the motion for a revolution in education within the last decade [15]. Evidence of these is seen within the health sector, where both intern and qualified doctors are frequently using mobile phones to consult with their patients. This can also be referred to as education, involving access to health education from a distance, using Information Communication Technologies (ICTs). Health practitioners benefit from the health education system through electronic libraries, search engines, and online knowledge databases [15]. Noorbhai [16] acknowledges the challenge health practitioners go through to streamline important information on symptoms, exposures, treatments, and various prevention strategies within the health sector. He postulated that streamlining this information is even more difficult within the current evolvement of research in the public health sphere. According to him, mHealth could be one of the solutions to address this issue.
Scholars have suggested some developments of mHealth, digital applications, and telemedicine, which have gained prominence and been used within the health sector, especially in developed economies [17, 18]. It was also showed that further studies are required to investigate the effectiveness of mobile applications on patients’ care and healthcare professional services [19]. However, there is already an overall positive impression of perioperative mHealth applications. The importance of further research was also substantiated regarding the role of telemedicine education on health professionals’ training, application and knowledge, including health stakeholders’ attitudes and practices [20].
## Research rationale
This research study is part of a broader research project that aims to establish optimised e-Learning platforms for health sciences students and academic staff, as well as exploring the integration of m-Health to better prepare students for their future work environments and enhance healthcare for patients in South Africa. The end goal of the larger project is the creation of a well-constructed platform that is validated and tested.
Given the limited success rate of e-Learning (uptake) within the health sciences field, this article aims to fill this gap by establishing staffs’ understanding of e-Learning and mHealth, their use within their curriculum, constraints and possible future developments.
Despite mHealth technologies’ impact on health research, it is assumed that the exponential growth of technology has outpaced the science of mHealth [21]. Since some health science curricula do not include mHealth training, health stakeholders do not have sufficient time to research the necessary new approaches and mediums; hence, further research needs to understand the capabilities and needs of students and staff stakeholders in the 4IR. In addition, an mHealth application that conforms with a robust network and promotes healthcare technology and innovation within the health sector should be developed, especially since there is an unsuccessful rate of e-Learning within the health sciences [22]. In addition, research on how platforms and curricula are established, merged and evaluated should be conducted to optimise learning and healthcare with patients and enhance quality assurance with students’ involvement. Furthermore, the report submitted by the World Health Organisation (WHO) supported the need for transformation and capacity training to strengthen the health workforce. However, the change will be enhanced if strategies are initiated to complement policies, retain graduates, as well as give students working conditions that promote knowledge and skills development. The proposed multi-modal platform (or tailored learning management system for health sciences) when being created would need to consider practical strategies that address the promotion of disease prevention as well as health sciences education.
## Study design
The larger research project employs a modified Delphi method to realise five objectives in various phases. The main benefits of using a modified approach are that it enables contributions and building on previous work experiences in the field, irrespective of whether this has been published [23], as well as fostering co-operation [24]. This study reports on the first phase, entitled ‘Pre-survey FGDs (Expert Panel 1)’. The composition of the expert panel will be discussed below.
The qualitative method of FGD was deemed appropriate as a first phase to gather in-depth perspectives across institutional and disciplinary boundaries from experienced health sciences educators from which closed and open questions could be constructed for the subsequent phase. One interview was conducted with a key informant due to their unavailability to participate in FGDs. The shift to a quantitative method in the second phase, via a primarily inductively constructed survey questionnaire, would considerably increase the sample size of South African health sciences teaching staff. The benefit of employing qualitative methods prior to survey questionnaire design is that it contributes to reducing researcher bias inherent in closed questions, as well as building a community of practice in that staff in other universities and disciplines discuss how to make “tools of impact” that move beyond the university’s boundary within the health sciences.
The FGDs and interview addressed the following objectives within the larger project: (i) assess health sciences staffs’ perceptions and understanding of these two applications, as well their perceptions on the importance and relevance of these applications to their curricula and future practices, and ii) establish challenges and opportunities of e-Learning and mHealth applications in the health sector.
## Study participants
The expert panel is drawn from staff at four universities, some of whom are regarded among the top in South Africa, according to Times Higher Education rankings. They are the Universities of Cape Town (UCT), Witwatersrand (Wits), Johannesburg (UJ) and the Western Cape (UWC). The participants were from the following health professions/disciplines: medicine, physiotherapy, biokinetics, optometry and occupational therapy. The criterion for selection was that volunteer participants had to be a lecturer or teaching academic at the university within the health sciences.
Universities’ health science staff were contacted through internal networks and the Faculty Administration Offices. Permissions were obtained from the relevant personnel/research heads. The researchers ensured that all staff members understood the importance of voluntary participation and signed the consent form. All staff volunteers were included in the FGDs, and one was accommodated in an in-depth virtual individual interview. The line of questioning for the FGD and interview were the same.
## Study procedure
The total number of participants were $$n = 19$.$ Virtual FGDs consisting of four expert panels (3 - 5 participants per panel) were conducted via Microsoft Teams or Zoom. Duration of each was 45minutes to 1 hour. These were augmented by one interview with a key informant from UCT.
All FGDs and the interview were conducted in 2021, within months of each other.
## Data analysis
Atlast.ti was used to analyse the responses from both the FGDs and the interview. Deductive thematic coding was applied as researchers used pre-formulated guide questions drawn from the literature to facilitate discussion in the context of the range and diversity of experience with mHealth and eLearning in South African Health Sciences Education. Participant responses were coded and synthesised into categories derived from the pre-formulated guide questions.
## Results
The findings consist of quantitative and qualitative data. The quantitative data provides information regarding the number of codes, by institution, per guide question. They are presented in Tables 1 - 8 in the Appendix section. Sankey diagrams were used to indicate the density of responses per guide question, by institution. They are presented in Figures 1 - 5 in the Appendix section. Since this study is not focused on a comparative analysis between institutions, the thematic findings per guide question presented below will not be differentiated by institution.
## Perceptions and understanding of mHealth and e-Learning within health sciences education
Guide Question 1: What are your perceptions and understanding of mHealth and eLearning within Health Sciences Education?Researcher CategoriesCodesMerging databases or datasets into mobile technology;Patient-focussedmHealth: computational diagnostics; health-related information on digital applications: Examples given: Discovery applications recording health and testing fitness & testing hearing; electronic devices or applications to connect and share information and communicate; mobile devices and digital connected into one; differentiate mHealth from eHealth;mHealth is patient-focussed for interactive consultations and storage for identification of disease and treatments; eHealth is a broader practice such as Telemedicine or TelehealthMerging electronic devices for Teaching and Learning;Student-focussedmHealth and eLearning: applications for stimulating student learning – example given “virtual for rock”; distributed clinical practice learning and integration of theoryLimitation of e-Learning and necessity of integrationeLearning: restricted to 3D Visualisation; future need for haptic sensation; needs to integrate both into the curriculum Participants have varied understandings and numerous comments related more to benefits and usefulness than actual definitions or descriptions of the two modalities, as evident in the codes above and Sankey diagram, Fig. 1 in Appendix. Fewer participants commented on mHealth and most commented on both modalities and the need to merge them. The need to merge the two modalities was articulated mainly in relation to enhancing teaching and learning as well as facilitating clinical practice in remoter areas. Those with more experience of mHealth referred to the merging of databases or datasets with digital technology and provided numerous examples. One participant differentiated between mHealth and Telemedicine or Telehealth, recognising the latter as encompassing a wider practice than an individual health practitioner consulting their mobile device for a narrower purpose, such as assisting with diagnosis, or testing hearing. One participant articulated the limitation of e-Learning as being confined to 3D visualisation and future developments needing to include haptic sensation, signally the differential requirements of professions. It would seem that mHealth is currently seen as patient-focussed and e-Learning as student-focussed. The selected quotations illustrate to some extent the variability amongst participants: “mHealth primarily is mobile health technologies. It’s mainly used in terms of mobile computing or using mobile phones in terms of patient interactions, or different types of storage or is the identification of diseases or treatments”….
“e-*Learning is* more of an application than a teaching process … as educators, we are more in the realm of e-Learning, where students can be taught methods and application”...
“mHealth has got a bit of a software connotation for me. And the mobile and the digital sphere are all connected into one. e-*Learning is* more in the educational sphere, but it’s very much the same thing where it’s got a digital component and is associated with e-Learning”...
“mHealth is any based thing that you could do via your mobile phone and any other application that falls within the health sector. It is a vast scope that can be implemented within the Health Sciences education sphere”… “Mobile applications and our e-Learning management system to augment and facilitate improved learning of their theoretical and clinical skills and knowledge”...
## Health sciences engagement with mHealth and e-Learning
The second guide question: Do you think Health Sciences have been engaging effectively with mHealth and eLearning?Researcher CategoriesCodesLow effectivity due to limited or non-useDoes not exist for some disciplines; very limited use due to constraints; not relevant to South African Health; not compelling enough or applicable for all aspects of health careHigh effectivityLimited use but very effective: incorporation of videos for insightful learningEnables distributed accessWhen used, provides demographic reach; can be taken into rural clinical services and teaching; insufficient usage for many reasonsEnhance learningIntegration of theory into practice-based learningMulti-modal learningmHealth and e-Learning need to be merged and integrated into the curriculumReceptivenessReadiness for Multi-modal learningScepticismQuestion how useful it is for student learning Table 2 and Figure 2 in Appendix provide quantitative data. Most agreed that health sciences have been engaging effectively with e-Learning, and have experienced the benefits it has for student learning. mHealth was rarely used, and some reported no usage.
It is evident from the codes that the meaning of engaging effectively with either mHealth or e-Learning, or both, has not been clarified explicitly. However, extent of use is a proxy for indicating support or endorsing either or both modalities. It is evident that the potential benefits of both are understood in terms of increasing access, enhancing learning and the value of integration of both; but these benefits have not been realised due to absence of integration which is a function of multiple constraints.
“No, it’s not implemented as it should be, nor does it have much impact on the Health Sciences student education. The application should be one of the most reformed domains with four IR, starting with this student. And unfortunately, I think this, particularly within our context at our university, it’s not implemented” … “I have only taught health sciences; the applications have not been practical enough, not all students can pay for the services. However, if the proposed applications are developed, it will be suitable to get biokinetics applications to the rural areas; this application will be perfect. However, data will be a challenge” … However, some participants agreed that the two platforms have been effective and are beneficiaries of the advanced 4IR platforms:“To some extent, the videos I have used has been very effective. Examples are Pixar applications with excellent, insightful videos that enable students to have a virtual parameter on student learning. However, since the students have limited time in practical classes, it is essential to access these applications at the beginning level to understand the importance of practice”…
## Effectiveness of mHealth and e-Learning in health services
Guide question 3: How effective has mHealth and eLearning been in contributing to Health Services?Researcher CategoriesCodesHighly valuedmHealth: frequent use by professionals and students using their mobile devicesPandemic-induced appreciationPandemic-induced necessity of blended learning increased frequency of useLimited effectivity due to multiplicity of constraintsLimited effectivity due to lack of institutional response; limited student engagement; limited availability of technology and infrastructure in rural areas; limited value accorded by tutors and students due to constraints; ‘fear of the unknown’ As in the case of exploring effectiveness in health sciences education, what effectiveness means in relation to the health services is not discussed. Here too, extent of use is a proxy for indicating support or endorsing either or both modalities. Figure 3 in Appendix indicates that the majority of participants affirm neither platforms have been effectively engaged.
The great value attached to mHealth was conveyed by some participants:“Mobile health applications are frequently used by health professionals in the services … and students regularly use their mobile devices”….
Others commented that the two modalities used in combination was induced by the COVID-19 pandemic, and they found this experience beneficial and stressful at times.
Several participants considered the either or both to have been of limited effectiveness for a variety of reasons, as indicated in the codes above: “Most students have not had a smooth ride with eLearning as sometimes they report to rural clinics or health centres where there is no network to communicate or utilise e-Learning platforms”… ….”not compelling enough; there is no student engagement”….
## Challenges or factors confining health practitioners from utilising mHealth and e-Learning platforms
Guide Question 4: What challenges or constraints have been constraining health practitioners from utilising mHealth and eLearning platforms or why the unsuccessful rate of e-Learning within Health Sciences Education?Researcher CategoriesCodesTechnologicalLack of connectivity and infrastructure; poor WiFi; insufficient data; lack of equipment and devices; applications developed internationally – not for AfricaDigital literacy limitationsLack of technical skills amongst staff; technological generation gap between staff and students; some students lack digital skills; lack of Research Data Management skillsUnfocussed student learning behaviourLimited student engagement due to online distractionsSocio-economicUnder-resourced settings; digital divide; lack of fundingPolicy absenceAbsence of adoption and regulation Whether health practitioners in the health services or educators in health science faculties, similar constraints are reported. They range from limited or absent infrastructure and hardware, resulting in a digital divide that extends to skills of staff and students, which is largely a function of socio-economic inequality and absence of policy.
Furthermore, challenges vary according to discipline and types of activities engaged:“… in my profession, we do many simulations in practical activities. Now simulation is not something you can do on a smartphone. Still, we’re starting to also see the emergence of virtual reality in emergency care in general by allowing students to interact. We currently have a PC-based system, like a simulation system, presenting students with a case. The system enables the students to a range of treatment options and can practice their decision-making in a dual variety of things”… In addition to a paucity in technology, digital knowledge and skills, there is the complexity of contextual relevance:“There should be enough e-Learning and mHealth applications, but they are not available. Practitioners, as well as educators, are still lacking in this aspect. Secondly, I think there are all of these fantastic things and, like Frostine, applications in developed economies. Still, it’s not for us in Africa, so most are developed internationally. Also, lack of funding and ignorant on digital applications is a limitation confining the health sciences. Lastly, another rule might be changing the world, challenging successful implementation”…
## mHealth and e-Learning impact if integrated into health sciences curricula and future practices
Guide Question 5: Do you think mHealth and eLearning will be valuable if integrated into Health *Sciences curricula* and future practices? Reasons. Researcher CategoriesCodesHybrid approachOnly if it is a mixed approach that includes contact with patientsStrong supportPractitioners, staff and students will all benefit; access to care, teaching and learning in rural areas As evident in Figure 4 in Appendix, most participants agreed that mHealth and e-Learning would be valuable if integrated into health sciences curricula and future practices, as all stakeholders would benefit, and in particular, distributed patient care and learning would be enhanced. Several emphasised the necessity of a hybrid approach, given that in many instances, patient care requires face-to-face contact.
The following two quotes indicate support but uncertainty about outcomes and impact: “Until we implement all these technologies at our disposal and implement them within our teaching, we obviously won’t know the impact it has on the students. Both staff and students should be comfortable using these applications and get rid of the fear of the unknown. If mHealth and e-Learning are integrated into the curricula, teaching and learning will be more accessible in health sciences”… “Yes, it may add value, but how practical, I cannot say at this moment as some things could be good, not excellent or counter-productive, but until it is implemented, the outcome cannot be predicted”…..
Another participant went further, and observed: “yes, a unified platform will be a future and potentially where we will have to incorporate machine learning systems”…
## Transferable skills to be embedded with the future curriculum using e-Learning and mHealth
Guide Question 6: What transferable skills ban be embedded within the future curriculum using e-Learning and mHealth?Researcher CategoriesCodesSocialCommunicationStrengthening ResearchResearch Data Management; Data AnalyticsTechnicalTechnical/digital skillsAdvanced LearningLearning Design and 21st Century Learning SkillsAttitudinal or DispositionOpen-mindedness and willingness to embrace technology A range of transferable skills from social to attitudinal were identified as indicated in the codes above. Most frequently cited were communication and digital skills (for practitioners, staff and students), followed by those strengthening research and advance learning. One participant emphasised appropriate attitude given experience of colleagues not taking e-Learning seriously, viewing it more as support in current circumstances (pandemic context) which may be phased out in future.
## Devices used when accessing technology platforms or learning management systems
Guide Question 7: Which device do you most often use when accessing technological platforms or learning management systems; and which device do you prefer using in your spare time when not working/studying; and which device for study or work?Researcher CategoriesCodesMobile DigitalLaptop (most frequently cited); mobile phone (used to a lesser extent); Laptop combined with mobile phone; i-Pads; TabletsLearning Management SystemsBlackboard; Sakai and VULA; Canvas; Moodle It was evident from the discussions and Table 6 in Appendix that the majority of participants used laptops most frequently, whether for work or study as well as for accessing their LMS. Furthermore, LMS varied between institutions with some participants being familiar with more than one LMS.
Most participants reported that all first-year student receive tablets for their academic work and e-books, which has made education much easier for the students. The main reason for this policy is that less privileged students cannot afford a laptop. Hence the tablets are seen as a mode of communication that enhances student learning.
## Effective mediums and platform options that allow the bridging or optimal balance of mHealth and e-Learning to advance teaching and engagement for health sciences education
Guide Question 8: What mediums and platforms would be most effective in bridging or providing optimal balance between mHealth and eLearning to advance teaching and engagement for Health Sciences Education?Researcher CategoriesCodesIntegration of LMS with relevant applicationsIntegrating Teams Classroom and LMS; integrating applications and LMS; integrating applications to access videosHybridContact learning combined with Multi-modalFit-for-purposeVariable platforms each with own strengths and weaknesses to be evaluated for integration decision-making; infrastructure needs to support learning pedagogiesEarly exposure to Multi-modal platformsMulti-modal needs to be started early in students’ learningPolicyRequire explicit goal of optimal balance of mHealth and e-Learning pedagogies Figure 2 in Appendix presents the codes that emerged from the FGD and interview findings.
Variability was evident among the participants. Some focussed on integration of LMS with applications, others emphasised the necessity of a hybrid approach that incorporates face-to-face learning and patient care; yet others recommended a fit-for-purpose approach: “There are different innovative applications integrated into teaching and learning within the institutions. Educational learning management systems, Dictionary App, developing a textbook on a mobile app amongst many. Students need this on their learning and practical activities, which will be great if added to the platform that is to be developed. Some current applications are developed to check for eye defects”… “*It is* essential to build the infrastructure that supports these learning pedagogies. There are specific platforms such as VULA, Blackboard, Canvas, Viewmodels, and several others. Each of these platforms or systems comes in with its positives and its limitations. So the question is about how to strike a balance”… Another participant commented that. “platforms such as telemedicine, the 4IR, and the AI are all already with us and not just the future”…
and that students should have early exposure as well as work with those platforms relevant to the field in which they will practice. This will also require a policy framework. Fit-for-purpose and digital literacy were emphasised by most participants.
## Perspectives on optimising both learning and healthcare in South Africa through multi-modal platforms
Guide Question 9: Do you believe a multi-modal platform and curricula that have been evaluated and developed will optimise both learning (for students and academic health staff) and healthcare (patients) in South Africa?Researcher CategoriesCodesContingent support;Strong clinical focusIf learning can be translated into clinical outcomesHybridIf the platform communicates across patients, students and e-Learning-type activities and provides for face-to-face; Multi-modal is necessary for categories of healthcare that require face-to-facePerformativity and Quality AssuranceExisting platforms require effective functioning; phones to access/interface with LMS is vitalEfficiencyExisting platforms that can be integrated into Teaching and Learning; one app to serve integrating function; will save time for future cliniciansRelevance, appropriateness, affordabilityNew applications need to be low tech, low grade, mobile, user-friendlyPolicy for implementationNeeds to be guiding principles and framework on what to do; people need to be brought together to understand what needs to be doneDigital Policy Guidance; Adequate high-level resourcingRole of government and telecommunications organisations necessary to advance integration into both Teaching/Learning and ServiceConditionalConcept of self-triage requires change in e-Learning and acceptance by health practitioners The majority of participants were supportive of a multimodal platform (see Table 8 in Appendix), as well as conveying an understanding or appreciation of what will be required evident in the codes above. The benefit of efficiency was foregrounded and applied to various dimensions of the patient-care-teaching-learning spaces. Additionally, several observed that performativity, quality assurance, relevance, appropriateness and affordability were necessary considerations to achieve those efficiencies. Two participants experienced in integrated LMS and applications multi-modalities further endorsed the view of others that a policy for implementation of integrated multimodalities is a necessity, and requires adequate resourcing at a high or macro level that includes government and telecommunications companies. In summary, they indicate that all actors, stakeholders, hardware, software and infrastructure need to be integrated under one POLICY platform.
One participant’s comment highlighted the scale and depth of change required, attitudinally, in their referral to “the concept of self-triage (patient autonomy and participation via mHealth) requires change in eLearning + acceptance by health practitioners” which echoes an earlier comment regarding the types of transferrable skills required: “open-mindedness and willingness to embrace technology”.
Further selected quotations that convey some of the codes captured above:“Yes, I certainly do believe multi-modal will directly link to all the various issues in the health sector. For example, health practitioners can design and develop video-based learning and live interaction sessions or content that everyone can do in their own time. And they can develop a multi-modal where you get feedback from systems either through AR or VR that allows health practitioners to reduce the number of contact and practical sessions will be an added advantage. However, I don’t think this can be achieved soon. It will take a while before it can be accomplished” … Another participant agreed that,“It would be ideal if multi-modal platforms were integrated to make a smoother learning environment. But, still, at the same time, I think it’s essential that we don’t put too much into applications and platforms for the sake of having all this technology” … However, the participant indicated that students might already feel overwhelmed and bombarded with so much that if more platforms are added for the sake of adding in, it might be too much on them and lose its effectiveness. Hence, educators and the people that designed these platforms must decide what needs to go specifically and make sure that anything that does go into such a platform or the curriculum is beneficial to the students. In addition, students must be already exposed to these new applications before they graduate to make life easy for them. However, a participant had a different opinion by stating. “Despite embracing all the technology in the world, if students are not taught soft skills and how to care for patients, they will not have in-depth experience on improved patient care experience. Hence, it would be great to integrate both models and see their value to teaching and learning in the health sciences” … In conclusion, Figure 5 in Appendix, conveys that the FGDs and interview commenced with majority support for mHealth and e-Learning despite various understandings of each, and by the end of the FGDs, the majority extended their support specifically for integrating the modalities as they considered them beneficial to patient-care, student learning and staff teaching. A minority were sceptical but not opposed given the level of resourcing, planning and coordination required; and some emphasised the necessity of multi-modal including face-to-face contact with patients given the sensitivity and complexity of some of the consultations.
## Discussion
Given the limited uptake of e-Learning within the health sciences area in South Africa, this study aimed to fill this gap by researching staffs’ perceptions and understanding of e-Learning and mHealth, the value accorded the two applications when combined into a multi-modal platform, as well as constraints and possibilities for the future. Findings relating to perceptions of all the staff members on e-Learning and mHealth indicate all participants are either aware of both mHealth and e-Learning or familiar with both. However, most believe that e-Learning has been embraced more than mHealth. While supportive, some expressed reservations about how effective e-Learning was for students’ learning given the challenges discussed below.
Most participants agree that mHealth applications are fully embraced within the health sector which requires reciprocal responses from the educators and trainers. The analysis corresponds with the literature that “mHealth Education” or “mHealthEd” are a new set of mobile devices and applications that are used as support systems for optimising upskilling among individuals to enhance quality patient care, and if embraced, can assist health practitioners in testing, supporting and supervising health care workers, as well as the provision of health care information to individuals. Hence academic journals and a growing literature on mobile applications role in managing chronic conditions and preventive medicine, is paramount [25, 26].
On the theme of challenges and factors confining health practitioners from utilising mHealth and e-Learning platforms, a variety of perspectives emerged: resource inequalities, the digital divide, appropriateness to the discipline, as well as varied skill levels between staff and students which could all impact student motivation. Their comments concur with those of Regmi and Jones’ systematic review [27], and that of Ortega, Villalta, Rodriguez, Arpi et al. [ 28]. More specific to under resourced settings were lack of technical skills, connectivity issues, inadequate educational training, insufficient data for students, lack of Wi-Fi stability or infrastructure, lack of skills or education, the generational gap, all of which underscore the concept of the digital divide. These findings concur with those of Garad, Al-Ansi and Qamari [5].
One of the objectives for mHealth and e-*Learning is* to beneficially impact learning via integration into the health sciences curricula and future practices. All participants agree that both applications are the future and will be a valuable contribution in optimising the teaching and learning curricula in health sciences. The scepticism of one participant is related to whether or not adequate resourcing will be available in a fit-for-purpose approach to integrating the modalities. Most believe that transferable skills such as research data management skills, communication skills, technical skills and data analytics skills are essential tools needed to understand and operate the two applications. The latter finding is also widely reported in the literature [5, 11].
Participants reported varying Learning Management Systems (LMS) used amongst the universities, for example, Blackboard, Moodle, Sakai, Vula and Canvas. Common to all was the use of laptops and mobile devices. This finding of LMS being augmented with mobile digital devices concurs with the literature [11, 5]. It was further suggested that effective mediums and platform options that allow the bridging or optimal balance of mHealth and e-Learning should be developed to advance teaching and engagement for health sciences education. Some participants provided examples of how they were trying to achieve this, for example, the addition of videos for first years to the LMS, integration of the Dictionary App, diagnostic applications, and textbooks on mobile devices. Others also referred to Telemedicine. If there was to be effective and efficient service delivery to all health practitioners and patients within the country, collaboration and policy was necessary to develop a multi-modal platform that took account of performativity, quality assurance, relevance, appropriateness and affordability.
These findings concur with the literature and highlight the need for urgent technological innovation within health sciences to bridge the gap left by COVID-19 on utilising e-Learning and mHealth to optimise the health sciences curricula. e-Learning has been beneficial during the pandemic, as confirmed by Ortega, Villalta, Rodriguez, Arpi et al. [ 28], but at the same time, there has been a challenge in terms of lab work, practicals or clinical-based competencies wherelearning could not be addressed effectively. Similar findings relating to STEM disciplines were reported by Al-Ansi [29]. Evidence has shown progress in developed economies on mHealth and e-Learning that would be useful if integrated within various curricula so that health practitioners (both staff and students) do not miss out on their clinical competency training.
It is, thereby, suggested that a multi-modal platform would enable an offering in an offline mode in the rural areas, where there is not adequate Wi-Fi connection. Ideally, the platform would work offline and access all various patient records on the system. The unified approach might prove to be helpful. However, every participant agreed that the adoption would take time due to the various challenges that persist within different universities and the socio-economic landscape.
## Limitations of this study (as a component of the larger research project)
This study has a number of limitations. During the pandemic, it was logistically challenging to have all FGDs and the interview conducted in person. Having it in person would have allowed the researchers to elucidate additional cues from the answers given by participants. In addition, the COVID-19 lockdown prevented more FGDs and interviews taking place, due to some of the restrictions imposed for staff being involved in research, at other universities. However, this did not impede on the required sample number of staff in this study, in order to derive meaningful insight and understanding of the topic at hand.
## Conclusions
The study demonstrated that most of the participants agreed that mHealth and e-Learning could be integrated for health sciences education and health care professionals. This can also benefit students if exposed to mHealth (or Extended Realities - XR) in universities and adopted in the clinical setting. With the new generation of health professionals, some skills such as diagnosing and treating an array of diseases has become complex and challenging. Most participants believed that these broad spaces could integrate mHealth and e-Learning. However, among the various challenges addressed are the social-economic difficulties that produce a digital divide, such as insufficient data for students or lack of Wi-Fi stability or infrastructure, load (electricity) shedding in South Africa, poverty as well as digital illiteracy. Therefore, it is essential to upskill both students and staff on digital literacy. Suggestions were further raised on the transferrable skills for healthcare professionals transitioning into an industry where data analytics and data management are being managed as well as gaining a comprehensive understanding of analysing data. Lastly, staff members agree that a new multi-modal platform (in the form of a learning management system with relevant and appropriate applications) tailored towards health sciences will benefit all stakeholders and be valuable to higher education and health sectors. Change is inevitable and change should be adopted within health sciences. Digitalisation and digital citizenship are gradually being integrated into teaching and learning. Now is the time to streamline and adapt health sciences curricula to promote health sciences education in the current 4IR.
## Appendix
Table 1Perceptions and understanding of mHealth and e-Learning within Health Sciences Education○ Perceptions and understanding of mHealth and e-Learning within Health Sciences EducationCode Themes = 18○ Perceptions on mHealth and e-Learning within Health Sciences EducationCode themes = 8TotalsFGD UJ Staff - 23092021314FGD at UWC 04102021202UCT staff 07102021112UJ Staff FGD 26082021314Wits FGD Staff - 27102021347UCT Staff Interview 13102021202Totals14721 Table 1 below and the Sankey diagram below (Fig. 1) reflect the theme’s emerging codes. In addition, there were four perceptions codes from FGD in UJ, two from UWC, two from UCT, another four from UJ FGD, seven from WITS and two from the UCT staff interviews. All these codes were analysed and integrated into the discussions below.
From the focus group discussions, there were 16 quotations on mHealth and e-Learning perceptions within health sciences and eight perceptions on its relevance within health sciences education.
Fig. 1Perceptions and understanding of mHealth and e-Learning within Health Sciences Education Table 2 and Fig. 2 reflects Health Sciences effectiveness in mHealth and e-Learning; in which four codes themes arise from the focus group with UJ staff, two from UWC, one from UCT Staff Interview, three from UJ staff FGD, five code themes came from Wits staff members and two from UCT staff interviews. Figure 2 below shows the number of codes received by the staff focus group discussions on each quotation (Fig. 3) (Table 3).
Table 2Health Sciences Engagement with mHealth and e-Learning and the value of a Multi-Modal Platform○ Health Science Effectiveness on mHealth and e-LearningCodes = 25○ Impact of new Multi-Modal Platform and Curricula on Health SciencesCodes = 18TotalsUJ FGD Staff – 230920214610UWC FGD Staff - 04102021213UCT staff interview 07102021112FGD UJ Staff 26,082,021336Wits FGD Staff - 27102021549UCT Staff Interview 13102021224Totals161734 Fig. 2Effective Platforms, Devices and Mechanisms in Accessing mHealth and e-Learning Fig. 3Perspectives on mHealth and e-Learning Measures and Value to Health Services Table 3Challenges or factors that have been confining health practitioners from utilising mHealth and e-Learning platforms○ Challenges or Factors Confining Health Practitioners from utilising mHealth and e-Learning PlatformsCode themes = 55FGD UJ Staff - 23092021Online distractions, No student engagement, Loadshedding, Wi-Fi network, Generational Gap. Transcripts of FGD at UWC 04102021Internet access, No student engagement, Insufficient data. UCT staff 07102021Lack of technical skills, digital divide, connectivity issues, lesser resourced settings, inequality, inadequate educational training, insufficient data for students, lack of Wi-Fi stability of infrastructure, lack of skills or education. UJ Staff 26082021Insufficient preparation from both tutors and students, poor backgrounds, lack of accessibility to data,Adoption and regulation. Wits FGD Staff - 27102021Online distractions, no student engagement, loadshedding, Wi-Fi network, Generational Gap. UCT Staff Interview 13102021No digital training, connectivity issues and systemic, digital divide, load-shedding.
Table 4mHealth and e-Learning impact (if integrated into health sciences curricula and future practices)○ mHealth and e-Learning Measures to Health Sciences. Code themes = 7○ mHealth and e-Learning Value in Health Sciences Curricula. Code Themes = 22○ Other Perspectives on multi-modal creationCode Themes = 17TotalsFGD UJ Staff – 23,092,0210213UWC FGD Staff - 0410202123611UCT Interview staff 071020211124UJ Staff 26,082,0211225Wits FGD Staff - 27,102,02133410UCT Staff Interview 13,102,0210224Totals7131737 In Table 4, 12 codes emerged: two from UJ staff, three from UWC, one from UCT interview, two from UJ FGD, three from WITS, and two from UCT.
Fig. 4Impact of New Multi-modal Platforms and Health Sciences Effectiveness on mHealth and e-Learning From Fig. 4, several codes emerged from the staff FGDs and interviews. On the concept of the impact of the new model on themes. Four themes emerged from Wits FGD, three codes from UJ Staff FGD, two from UCT FGD, one from UWC interview, one from UCT Staff and six themes from UJ FGD. However, from the concept of health science effectiveness on mHealth and eLearning, five codes emerged from Wits FGD, three code themes from UJ Staff FGD, two codes from UCT FGD, four codes on UJ FGD and one code from the UWC interview (Table 5).
Table 5Sequence of transferable skills embedded with the future curriculum using e-Learning and mHealthTransferable skillsFGD UJ Staff - 23092021Technological skills, Communication skills. Transcripts of FGD at UWC 04102021Education and Training skills, communication skills. UCT staff 07102021Research Data Management skills, communication skills, technical skills, data analytics skills. UJ Staff 26082021Communication skills, technological skills, learning design and twenty-first-century learning skills scores, and twenty-first-century learning skills scores. Wits FGD Staff - 27102021Statistical analysis, data analysis, E-portfolio of learning, communication, constructive writer and critical thinker. UCT Staff Interview 13102021Data Management Skills.
Table 6Devices used in accessing technological platforms or learning management systems○ Device Used in Accessing Technology Platforms for Study or WorkCode themes = 37Types of Devices and PlatformsTotalsFGD UJ Staff - 23,092,0218Laptops, Mobile, Sakai, Moodle8Transcripts of FGD at UWC 041020213Vula, Canvas, Mobile, Laptops3UCT staff 071020212Vula, Canvas, Mobile, Laptops2UJ Staff 260820212Blackboard, Mobile phones, Laptops2Wits FGD Staff - 271020217Sakai, Moodle, Mobile Phones, Laptops7UCT Staff Interview 13,102,0212Vula, Canvas, Mobile, Laptops2Totals2424 Table 6 presents different devices and the number of codes that emerged from each theme in accessing technology platforms or learning management systems in the four universities. For example, UJ staff members are familiar with Blackboards, mobile phones and laptops. However, some members are familiar with Vula and Sakai platforms. UCT and UWC staff members are familiar with Vula and Canvas platforms, while Wits uses Sakai and Moodle alongside mobile phones and laptops.
Table 7Effective Mediums and Platform Options to Advance mHealth and e-Learning○ Effective Mediums and Platform Options to Advance mHealth and e-LearningCode themes =15○ Effective Platform Options for Optimal Balance on mHealth and e-LearningCode Themes = 18TotalsFGD UJ Staff - 23092021033Transcripts of FGD at UWC 04102021347UCT staff 07102021123UJ Staff 26082021112Wits FGD Staff - 27102021213UCT Staff Interview 13102021123Totals81321 Table 7 reflects the number of codes that emerge from each focus group discussion. Three codes emerged from effective mediums and platform options to advance mHealth and e-Learning. Seven came from FGD at UWC. Three appeared from UCT, two came from the second FGD in UJ, three emerged from WITS FGD and UCT staff interviews.
Table 8Perceptions of e-Learning and mHealth (as Multi-modal)○ Impact of new Multi-modal Platform and Curricula on Health SciencesCode themes - 17○ Other Perspectives on Multi-modal creationCode themes - 17TotalsFGD UJ Staff – 23,092,021617Transcripts of FGD at UWC 04102021167UCT staff 07102021123UJ Staff 26,082,021325Wits FGD Staff – 27,102,021448UCT Staff Interview 13,102,021224Totals171734 Table 8 presents the findings from the perceptions of eLearning and mHealth. It translates the codes emerging from the Sankey diagram. The number of codes emerging highlights the understanding of the staff members in the universities sampled on eLearning and mHealth, specifically the multimodal system.
Fig. 5Perceptions of e-Learning and mHealth (as Multi-modal) From the above figure (Fig. 5), seven themes came from the Wits FGD on the perceptions of mHealth and eLearning, while the UJ staff FGD has 4 codes, and the UCT FGD had 3 codes. The second UJ FGD had 4 codes, the UCT staff interview had 2 codes and the UWC FGD also had 2 codes emerging on the understanding of mHealth and eLearning. The codes are translated in Table 8 above.
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|
---
title: 'Uptake of hepatitis C direct-acting antiviral treatment in China: a retrospective
study from 2017 to 2021'
authors:
- Xinyu Du
- Jiarun Mi
- Hanchao Cheng
- Yuanyuan Song
- Yuchang Li
- Jing Sun
- Polin Chan
- Zhongdan Chen
- Simon Luo
journal: Infectious Diseases of Poverty
year: 2023
pmcid: PMC10043849
doi: 10.1186/s40249-023-01081-4
license: CC BY 4.0
---
# Uptake of hepatitis C direct-acting antiviral treatment in China: a retrospective study from 2017 to 2021
## Abstract
### Background
Direct-acting antivirals (DAAs) for hepatitis C treatment in China became available since 2017. This study expects to generate evidence to inform decision-making in a nationwide scale-up of DAA treatment in China.
### Methods
We described the number of standard DAA treatment at both national and provincial levels in China from 2017 to 2021 based on the China Hospital Pharmacy Audit (CHPA) data. We performed interrupted time series analysis to estimate the level and trend changes of the monthly number of standard DAA treatment at national level. We also adopted the latent class trajectory model (LCTM) to form clusters of the provincial-level administrative divisions (PLADs) with similar levels and trends of number of treatment, and to explore the potential enablers of the scale-up of DAA treatment at provincial level.
### Results
The number of 3-month standard DAA treatment at national level increased from 104 in the last two quarters of 2017 to 49,592 in the year of 2021. The estimated DAA treatment rates in China were $1.9\%$ and $0.7\%$ in 2020 and 2021, which is far below the global target of $80\%$. The national price negotiation at the end of 2019 resulted in DAA inclusion by the national health insurance in January 2020. In that month, the number of treatment increased 3668 person-times ($P \leq 0.05$). LCTM fits the best when the number of trajectory class is four. PLADs as Tianjin, Shanghai and Zhejiang that had piloted DAA price negotiations before the national negotiation and that had explored integration of hepatitis service delivery with prevention and control programme of hepatitis C within the existing services demonstrated earlier and faster scale-up of treatment.
### Conclusions
Central negotiations to reduce prices of DAAs resulted in inclusion of DAA treatment under the universal health insurance, which are critical elements that support scaling up access to hepatitis C treatment in China. However, the current treatment rates are still far below the global target. Targeting the PLADs lagged behind through raising public awareness, strengthening capacity of the healthcare providers by roving training, and integrate prevention, screening, diagnosis, treatment and follow-up management of hepatitis C into the existing services are needed.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40249-023-01081-4.
## Background
Hepatitis C is one of the major global public health threats. Currently, $0.7\%$ of the world’s population (56.8 million people) are infected with hepatitis C virus (HCV), which remains a leading cause of chronic hepatitis, cirrhosis, liver failure and hepatocellular carcinoma [1], accounting for more than 0.3 million lives lost each year [2]. The introduction of direct-acting antivirals (DAAs) with high cure rates, shorter treatment durations, fewer side-effects as compared to previous medicines, revolutionized the treatment of HCV infection, and provide an unprecedented opportunity for widespread scale-up and elimination [3, 4].
In 2016, the World Health Organization (WHO) set goals for HCV elimination, defined as a $65\%$ reduction in mortality and a $90\%$ reduction in incidence of new infections by 2030 [5, 6]. Reaching these goals will require a large scale-up of HCV testing to diagnose and treat $80\%$ of all the people living with HCV. In 2015, only $7.4\%$ (1.1 million) of the diagnosed HCV-infections started treatment. The global cumulative number of persons treated for HCV was 5.5 million, only about half of them had received DAA treatment, and there were more new HCV infections than patients who had started on treatment. Recent new data show that 9.4 million people diagnosed with HCV infections had been treated using DAAs globally between 2015 and 2019 [7]. Although a number of developing countries have been making substantial progress and move towards elimination of hepatitis C, there has been a dearth of national or provincial level information on HCV care in China [8–11].
China is one of the countries with the largest number of people living with chronic HCV (estimated 7.6 million) in the world [12], only $25\%$ of them are diagnosed and $1\%$ treated [1]. In response to the goal of viral hepatitis elimination as a public health threat by 2030, China implemented a series of national policies to improve the availability and affordability of DAAs and scale-up of DAA treatment at both national and provincial levels. Some provincial-level administrative divisions (PLADs) and cities had already been exploring public funding of DAAs even before the national initiative and changes in local policies including piloting innovative health insurance financing models. Other PLADs initiated pilots where the primary care services provided hepatitis C care through improving the family doctor contract services mechanism, as well as integration of hepatitis C management into the existing disease prevention and control programs (Additional file 1: Annex 1). Several DAAs including locally developed ones were fast-tracked for market entry in 2017. Eight DAAs are currently funded by the national health insurance following central price negotiations since 2019. The cost of DAA treatment in China decreased to USD 200–1500 for a 12-week course of cure, and the individual out-of-pocket expenditure is within USD 150–450 per 12-week course of cure [13].
To understand the number of DAA treatment at either provincial level or the national level, we used the terms of "Hepatitis C” or “HCV” and "treatment" to search PubMed, Embase, and CNKI databases from January 1, 2017, to December 31, 2022 in English and Chinese. We only identified a very limited number of studies reporting the number of treatment of hepatitis C in China either at national or local levels. China started to monitor the prevention and treatment of hepatitis C in sentinel hospital since 2016, which has been expanded to 58 secondary and tertiary hospitals in 13 PLADs [14–16]. At the provincial level, only Tianjin City reported the number of DAA treatment in designated hospitals [17]. There is no information on the number of people living with HCV under or received DAA treatment at national levels due to the absence of a national patient monitoring or registry system. Studies that monitor the treatment of hepatitis C were all in specific hospitals or within limited areas [18–20].
To the best of our knowledge, this is the first comprehensive analysis attempted to estimate the number of DAA treatment at national level, and to explore the potential enablers of the scale-up of DAA treatment at provincial level in China. We evaluated the level and trend changes of monthly number of standard DAA treatment before and after the first batch of DAAs funded by the national health insurance following price negotiation in January 2020. We also compared the quarterly number of 3-month standard DAA treatment across PLADs from quarter 1 (Q1) 2020 to quarter 4 (Q4) 2021, and analysed the enablers of scale-up the DAA treatment at local level. The study expects to generate evidence to inform decision-making in a nationwide scale-up of DAA treatment in China, and contribute to the achievement of the 2030 goal.
## Data source
This study extracted longitudinal monthly procurement data of all marketed DAAs in China (Additional file 1: Annex 2) from QuintilesIMS Health (IQVIA)’s China Hospital Pharmacy Audit (CHPA). CHPA collects medicines procurement data from health facilities with more than 100 beds in 31 PLADs of China (including county hospitals in rural areas). IQVIA’s medicines procurement data is an established system and is widely used in research for pharmaceutical policy and market analyses [21–29]. Eleven DAAs and their combinations were identified from CHPA between July 2017 (when the first DAA was approved for market in Chinese mainland) to December 2021. We transformed the procurement data of DAAs into the number of standard DAA treatment of hepatitis C following the pharmaceutical manufacturers' instructions approved by the national regulatory authority [30] and the National Guidelines for Hepatitis C Prevention and Treatment [31]. We obtained the COVID-19 epidemic data and other data from the website of National Health Commission [32], China Statistical Yearbook 2021 [33], China Health Statistical Yearbook 2021 [34] and China Health Account Report 2021 [35].
## Measurement
We calculated the monthly number of standard DAA treatment at national level for the time series statistical analysis. Considered that the course of the standard DAA treatment is 12-week, we also calculated the quarterly number of 3-month standard DAA treatment at both national and provincial levels (not included Tibet) to demonstrate the number of patients completed the 12-week standard treatment.
Provincial level monthly number of standard DAA treatment = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sum_{$i = 1$}^{n}}DAA \, combination_{i}$$\end{document}∑$i = 1$nDAAcombinationi, n is the number of DAA combinations identified from CHPA in specific month.
Provincial level quarterly number of 3-month standard DAA treatment = (∑c January–March)/3 or (∑c April–June)/3 or (∑c July–September)/3 or (∑c October–December)/3.
National level data = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sum_{$i = 1$}^{n}provincial \, level$$\end{document}∑$i = 1$nprovinciallevel, $$n = 30$$, is the number of PLADs included in this study.
## General conditions for conversion of monthly DAA volumes to monthly number of standard DAA treatment
If the monthly procurement volumes of individual components of a DAA combination match with each other, the monthly number of standard treatment with that DAA combination was estimated based on the monthly procurement volume of any one of individual components. If the monthly procurement volumes of individual components of a DAA combination do not match with each other, the monthly number of standard treatment with that DAA combination was estimated based on the highest monthly procurement volume of individual component. The gap volumes of the other components were deducted from their consumption volumes in the next month.
## Specific conditions for conversion of monthly DAA volumes to monthly number of standard DAA treatment
Daclatasvir (DAC), asunaprevir (ASV) and sofosbuvir (SOF) were the first batch of DAAs widely used in China. The monthly number of treatment with DAC + ASV was estimated based on the monthly procurement volume of ASV, and the surplus of DAC was used to estimate the number of standard treatment with SOF/DAC. The surplus of SOF was used to estimate a small number of treatment in case SOF was in combination with pegylated interferon alfa and ribavirin (PR) or ribavirin (RBV). The dosages of SOF in both treatments are the same. Fixed-dose combinations including SOF all have matched volumes of individual components.
The annual reported incidence of hepatitis C and total population in each PLAD describe provincial epidemiological and demographic characteristics. The per capita gross domestic product (GDP/capita), per capita total health expenditure (THE/capita), and the proportionate population covered by the urban employee health insurance in each PLAD reflect provincial socio-economic development status. The proportionate health expenditure of public health institutions represents public inputs to public health institutions (including Centers for Disease Control and Prevention, etc.). And the proportionate patient out-of-pocket (OOP) expenditure reflects individual financial burden of healthcare (The sources of these provincial data were listed in Additional file 1: Annex 3).
## Study design
We firstly drew the time series chart of the quarterly number of 3-month standard DAA treatment of hepatitis C at national level from Q3 2017 to Q4 2021, and the cumulative histograms for each of the DAA combinations (Fig. 1). We also drew the time series charts of the monthly number of standard DAA treatment at national level from July 2017 to December 2021 (Additional file 1: Annex 4), as well as the quarterly number of 3-month standard DAA treatment for each of 30 PLADs from Q1 2020 to Q4 2021 (Additional file 1: Annex 5). Based on the national level monthly data from July 2017 to December 2021, we performed interrupted time series (ITS) analysis to estimate the immediate level changes and afterwards trend changes of the monthly number of standard DAA treatment in the month that the first batch of DAAs were funded by the national health insurance in January 2020, and in the month that the first domestically developed DAA was funded by the national health insurance in March 2021, respectively. Based on the quarterly panel data of number of 3-month standard DAA treatment of hepatitis C of 30 PLADs from Q1 2020 to Q4 2021, we adopted Latent Class Trajectory Model (LCTM), a specialised form of finite mixture modelling to simplify heterogeneous PLADs into more homogeneous clusters, and identify latent classes of PLADs following similar progressions of determinants over time [36]. We listed the PLADs in each of the identified trajectories class, and their respective characteristics (epidemiological and demographic, socioeconomic development, public input in public health institutions, and the individual financial burden of healthcare) in a heat map, in order to identify any common characteristics within the class and differences across classes. We also linked the pilot explorations of some areas with the clustering results, and analysed the potential factors that might affect the scale-up of DAA treatment. Fig. 1Number of 3-month standard DAAs treatment and reported cases of hepatitis C (Q3 2017–Q4 2021). ASV asunaprevir, CLP coblopasvir, DAC daclatasvir, DNV danoprevir, DSV dasabuvir, GLE/PIB Glecaprevir/Pibrentasvir, GZR/EBR Grazoprevir/Elbasvir, OPr Ombitasvir/Paritaprevir/Ritonavir, PR pegylated interferon alfa/ribavirin, r ritonavir, RAV ravidasvir, RBV ribavirin, SOF sofosbuvir, SOF/LDV sofosbuvir/ledipasvir, SOF/VEL sofosbuvir/velpatasvir, SOF/VEL/VOX sofosbuvir/velpatasvir/voxilaprevir
## Statistical analysis
We fitted the following segmented liner regression model with the monthly number of standard DAA treatment at national level from July 2017 to December 2021. The two policy intervention time points were January 2020 when the first batch of DAAs funded by the national health insurance, and March 2021 when the first locally developed DAA was funded by the national health insurance.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{t} = {\beta }_{0} + {\beta }_{1}{X}_{1} + {\beta }_{2}{X}_{2} + {\beta }_{3}{X}_{3} + {\beta }_{4}{X}_{4} +{ \beta }_{5}{X}_{5} + {\beta }_{6}{X}_{6} + {\varepsilon }_{t},$$\end{document}Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+εt,X1 is a time variable, denotes the number of months from 1 to 54 (from July 2017 to December 2021). X2 and X4 are policy variables. Before January 2020, X2 = 0; after January 2020, X2 = 1. Between January 2020 and March 2021 X4 = 0; after March 2021, X4 = 1. X3 and X5 are time variables, denote the number of months after January 2020 and after March 2021 respectively. Yt denotes the monthly number of standard DAA treatment. Considering the possible impact of coronavirus disease 2019 (COVID-19) pandemic, we included the national monthly number of new confirmed COVID-19 cases in the model as a control variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{6}$$\end{document}X6. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1} \, \text{denotes the baseline trend}. { \beta }_{2}$$\end{document}β1denotes the baseline trend.β2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{4}$$\end{document}β4 denote the immediate level changes of the monthly number of standard DAA treatment in January 2020 and March 2021, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{3}$$\end{document}β3 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{5}$$\end{document}β5 denote the trend changes of the monthly number of standard DAA treatment between January 2020 and March 2021, and after March 2021, respectively.
To identify the PLADs with similar trends of the quarterly number of 3-month standard DAA treatment, we adopted LCTM to identify the potential trajectory categories of the quarterly number of 3-month standard DAA treatment of 30 PLADs from Q1 2020 to Q4 2021. We added the quadratic term of "quarter" in the model, in order to fit the model for non-liner trajectories better. We calculated the posterior probability of the trajectory of each PLAD. The model with the lowest Akaike information criterion (AIC) value was selected as the best fitted one. Meanwhile, we secured that the posterior probability should be greater than 0.7, and the number of PLADs in each trajectory class to total number of PLADs [30] should be greater than $2\%$ [37].
For all analyses, a two-tailed P value < 0 0.05 was considered statistically significant. We performed all statistical analyses with software R 4.2.0 (Lucent Technologies, Jasmine Mountain, USA) and Microsoft Excel 2019.
## Number of DAA treatment at national level
The number of reported cases captured by the National Infectious Diseases Reporting System has been always keeping at around 60,000 per quarter, except the first quarters of 2020 and 2021 during the COVID-2019 pandemic. From July 2017 to December 2021, the total number of 3-month standard DAA treatment in China was 79,321, including 28,102 in 2020 and 49,592 in 2021. Before 2020, the monthly number of DAA treatment was noted to be low. However, since January 2020, the numbers of treatment accelerated rapidly. Three combinations of DAA regiments including sofosbuvir/ledipasvir (SOF/LDV), sofosbuvir/velpatasvir (SOF/VEL) and elbasvir/grazoprevir (GZR/EBR) acounted of almost all (nearly $100\%$) of the HCV treatment since January 2020 when the regiments began to be publicly funded (that is, included under the national health insurance coverage). Treatment numbers of the pan-genotype combination (SOF/VEL) is consistently the highest. By Q4 2021, the market share of the domestically developed coblopasvir (CLP) was no more than $7\%$, even though it was publicly funded since March 2021 (Fig. 1).
## Level and trend changes of the number of monthly DAA treatment at national level
The ITS analysis results are shown in Table 1 and Additional file 1: Annex 4 where from January 2020 when the first batch of DAA regiments were included under the national health insurance. This resulted in a 3668 person-times increase in standard DAA treatment compared to before (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document}β2, $P \leq 0.05$). Thereafter, between January 2020 and March 2021, numbers of treatment increased at 551 person-times/month (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{3}$$\end{document}β3, $P \leq 0.001$). We noted that the market entry of the first domestically developed CLP which was included under national health insurance did not substantially change the numbers of treatment in March 2021 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{4}$$\end{document}β4, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${ \beta }_{5}$$\end{document}β5, $P \leq 0.05$), when it was introduced. Table 1ITS regression results of the monthly number of standard DAA treatment at national level (July 2017–December 2021)EstimateStd. errort-valueP-value\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{0}$$\end{document}β0− 6.08743.66− 0.0080.99\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1}$$\end{document}β110.8341.890.260.80\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document}β23667.721411.202.600.01\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{3}$$\end{document}β3550.94153.323.59 < 0.001\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{4}$$\end{document}β4− 526.131642.81− 0.320.75\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{5}$$\end{document}β5− 186.72266.08− 0.700.49\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{6}$$\end{document}β6− 0.040.03− 1.210.23The value of Durbin-Watson statistic is 1.92Values in bold are significant ($P \leq 0.05$)ITS: interrupted time series
## Trajectory categories of the quarterly number of 3-month standard DAA treatment at provincial level
As presented in Additional file 1: Annex 6, LCTM fits the best when the number of trajectory class is four. We defined the four trajectory classes as class 1, class 2 and class 3, and an outlier. The predicted trajectories of the four classes were presented in Fig. 2. Class 1 includes the PLADs of Anhui, Beijing, Guangdong, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangsu, Liaoning, Shandong, Shaanxi, Shanghai, Tianjin, Xinjiang, Yunnan, Zhejiang, and Chongqing (in dark-red color). Class 2 includes the PLADs of Fujian, Gansu, Hainan, Jiangxi, Ningxia, Shanxi, and Sichuan (in red color). Class 3 includes the PLADs of Heilongjiang, Qinghai, Jilin, and Inner Mongolia (in light-red color). And the outlier is Guangxi (in green color) PLAD. As showed in Additional file 1: Annex 5, the number of treatment before 2020 in the PLADs of class 1, to class 2, to class 3 demonstrate from earlier growth, to later growth, and very late growth, respectively. The outlier PLAD (Guangxi) was always near to 0 before January 2021, and kept at a very low level until Q2 2021, when the other PLADs already grew to a high level. The growth of the outlier PLAD emerged in Q3 2021. As presented in Fig. 3, the four PLADs in class 3 and the outlier PLAD have the similar characteristics of lower economic development level but higher hepatitis C incidence rate (as documented in the National Notifiable Infectious Disease Reporting System).Fig. 2Trajectory class-specific mean predicted trajectoryFig. 3Heat map of demographic, socioeconomic and epidemic characteristics of 30 PLADs [2020]. The darker the color, the larger the number; PLAD provincial-level administrative division, GDP gross domestic product, THE total health expenditure, HEPH% proportionate health expenditure of public health institutions, OOP% proportionate out-of-pocket expenditure
## Discussion
2017 is the first year that the first DAA combination (DAC tablet and ASV capsule) were marketed for treatment of adult chronic hepatitis C in Chinese mainland [38]. DAA options gradually increased afterwards. China updated the national guideline for the prevention and treatment of hepatitis C in 2019, which recommended interferon-free pan-genotypic DAAs. However, scale-up of DAA treatment was not seen until January 2020, when the first batch of DAA combinations were publicly funded (included under national health insurance coverage) following national price negotiations, which reduced the prices by over $85\%$. After January 2020, the number of DAA treatment began to increase significantly at national level and in the PLADs in the high-level treatment trajectory class. This implied a significant positive impact of the affordability of DAA on the scale-up of DAA treatment.
Domestically developed DAAs started to be marketed in Chinese mainland since 2018. Among them, CLP was immediately funded by the national health insurance in March 2021. However, public funding of CLP did not have significant impact on the scale-up of DAA treatment. The reason might be that the local developer of CLP was not able to market it as a pan-genotype combination with SOF, the corner-stone of pan-genotype DAA combinations. Although the local producer of CLP already registered generic SOF with the Chinese drug regulatory authority, they are still not able to formally market it. This is because of patent protection of the originator until 2024. Currently, none of the domestically developed DAAs can be marketed as a pan-genotype combination with SOF, and the national health insurance is limited in the indication of domestically developed DAAs to specific genotype instead of pan-genotype. Genotyping of the HCV virus is not publicly funded in most areas of China. Compared with imported pan-genotype combinations, the price advantage, as well as the overall market competitiveness of domestically developed DAAs may be lesser. Compared with the global lowest price of the generic DAAs in other middle-income countries like Egypt and Malaysia, where the 12-week treatment cost has been below USD 40 [13], the current prices of DAAs in China are still too high. For the patients with low ability-to-pay, especially those entitled to lower health insurance benefits, may still have specific economic problems and therefore have reduced willingness for treatment [39]. Marketing the domestically developed pan-genotype DAA combinations immediately after the patent expiring of sofosbuvir could be a good bargaining leverage to further reduce the prices of originated DAAs. In addition, improving the safety net for those who have poor ability-to-pay will contribute to universal access to DAA treatment.
Previous studies estimated that there are about 4–5 million chronic HCV infections who need treatment in China [15]. Based on the number of 3-month standard DAA treatment estimated from this study, the DAA treatment rates in China was $1.9\%$ and $0.7\%$ in 2020 and 2021 respectively, which were approximate the $1\%$ treatment rate estimated by previous studies [1]. These numbers are far below the 2030 targets ($80\%$ eligible received treatment) [40]. The highest number of 3-month standard DAA treatment is only one fourth of the reported national notifiable HCV cases during the same quarter. Compared to low- and middle-income countries such as Georgia and Egypt, where the treatment rates of active hepatitis C infections have reached a high level ($79\%$ and $92\%$ of the people with active HCV infection initiated treatment in 2018–2019, respectively) [9, 10], there is still a long way for China to go.
Affordability is important for further scale up DAA treatment, but it is not the only issue. Countries such as Australia which provide universal health coverage including HCV treatment have shown that while subsidized treatment is necessary, it is insufficient to reach the elimination targets [41]. Although new hepatitis C prevention and treatment guideline recommend DAA as the first-line treatment option for hepatitis C in 2019 [31], we noted a number of PLADs with lower economic development level and high burden of hepatitis C in class 3 and the outlier PLAD showing low levels of treatment uptake. This might be associated with the lack of knowledge and awareness of hepatitis C in the less developed areas among the public service providers [42]. As well, insufficient availability of DAAs in hospitals in these areas due to delayed implementation of the national policy may result in delayed scale-up of treatment [43]. For the PLADs lagged behind, raising public awareness, strengthening capacity of the healthcare providers through roving training by the trainers of new guidelines from the advanced area are urgently needed.
PLADs in class 1 demonstrated earlier increase in the number of DAA treatment even before 2020. This is mainly because of the pilots of local price negotiations and local insurance funding of DAAs before the national initiative. These PLADs piloted multiple projects to explore pathways to integrate hepatitis C management program into the existing services. Tianjin, for example, started to have all the national guideline recommended DAAs funded by local health insurance in 2018, and implemented an innovative capitated payment for outpatient care of hepatitis C [44]. The family doctor contract gatekeeping system in Shanghai was improved through the health system reform since the early 2010s, and Shanghai was the first area to incorporate hepatitis C management into the scope of family doctor contract services. Primary care centers provide free physical examination, chronic disease monitoring, prescription filling, health consultation, and infection prevention guidance. Shanghai also integrated hepatitis C with the prevention and control program of HIV/AIDS, which provides voluntary counseling and testing, free rapid screening, health consultation, referral to specialized hospitals for diagnostic test and treatment, and follow-up visits. Some districts of Shanghai provide free RNA test for the HCV-antibody positives detected from the free rapid screen [45, 46]. Ningbo City of Zhejiang Province built a ‘four-in-one’ initiative for hepatitis C management in 2019, which enhanced the role of primary care institution, and its cooperation with the Center for Disease Prevention and Control, insurance designated hospitals, and the health administrative department. Ningbo established a feasible referral and follow-up mechanism based on the functioning primary care services to form the closed-loop management of hepatitis C [47]. These local pilots before 2020 in the ‘pioneering’ PLADs in the high-level treatment trajectory class led them being at the forefront of rapidly scaling up the DAA treatment, when the policy environment changed. In the other PLADs within the same trajectory class, although the number of DAA treatment was relatively low before 2020, these PLADs responded quickly when the DAAs started to be publicly funded.
WHO highlighted the need to bring hepatitis care closer to primary care and communities so that people have better access to treatment and care [48]. The pilots in Ningbo of Zhejiang and Shanghai focusing on supporting comprehensive primary care response for HCV treatment well follow the above direction pointed out by WHO, and should be expanded to other areas of China. This direction has been practiced in many countries that have substantially increased DAA treatment access by building on existing community-based services and promoting simplified service delivery models. Decentralizing testing and treatment to lower levels of care, integrating with other services, and task-sharing with delivery of care and treatment by non-specialists and nurses are among the key strategies [49] and have been implemented successfully in low- and middle-income countries such as Egypt, Georgia, Cambodia and Malaysia. In 2019, Egypt implemented their universal hepatitis C program that improved awareness among citizens, provided free and accessible HCV testing, infection control, treatment and follow-up. The free screening program integrated non-communicable disease and viral hepatitis, and included tests for diabetes, obesity and blood pressure, as well as hepatitis B vaccination. The HCV testing covered 62 million adults and 15 million adolescents, and $92\%$ of the 1.15 million HCV seropositive people who have completed testing have received DAA treatment [9]. Georgia conducted free HCV antibody testing for blood donors and HIV-infections, and integrated screening, care and treatment in the primary care settings and harm-reduction centers throughout the country. The integration allowed patients and harm reduction beneficiaries to receive hepatitis C care and treatment services in familiar and convenient locations [10]. Medicines Sans Frontieres established a simplified management model of hepatitis C based on the community level health facilities in Cambodia since 2016. The integrated model has transferred many clinical tasks of hepatitis C from doctors to nurses and pharmacists, which provided testing for about 135,000 people and treatment for 18,000 people by the end of 2020 [50]. As a middle-income country, Malaysia integrated its viral hepatitis programme into the national HIV programme since 2017 [51]. Simplifying the treatment process, integrating hepatitis C prevention and treatment so that general practitioners at the primary care clinics can test, confirm and treat, have supported the country’s progress towards elimination by 2030. Spain provided patients with hepatitis C in primacy care together with the one-step diagnosis to effectively detect hidden infections and increased the number of treatments [52]. South-West England integrated an algorithm into primary care information technology systems to identify individuals with high-risk markers of HCV, and rolled out this alongside educational and training packages for staff. The system is expected to be scale up across the UK [53]. Studies in the United States proved that as patients were typically more engaged with their primary care provider, hepatitis C primary care treatment program were in a good position to identify and treat hepatitis C, and especially an effective way to treat HCV infection in underserved communities [54–59]. These successful experiences of integration of prevention, screening, diagnosis, treatment and follow-up management of hepatitis C into the existing services provide important lessons for China to further scale up DAA treatment.
The findings of this study are novel and meaningful for decision-makers to recalibrate the public policy towards elimination of hepatitis C. While the study has several limitations. First, the hospital medicines procurement data may not accurately reflect the actual clinical use due to that the remaining medicines purchased in the current month may be used in the next month, which may lead to underestimation of the actual consumption and overestimation of the previous month. Adopting the quarterly data could address this problem to some extent. Second, CHPA only collects data from hospitals with more than 100 beds, and medicines used in primary care and retail pharmacies are not included in the statistics. Considering that the diagnosis and treatment of hepatitis C in China are mainly carried out in specialized or general tertiary hospitals, CHPA data at this time can reflect the overall consumption of DAAs. Third, this study assumes that all patients followed the 3-month consecutives standard DAA treatment as recommended by the national guideline, which does not consider non-standard therapy in the real-world setting. Fourth, although ITS regression analysis results show that the impact of the COVID-19 outbreak on the number of monthly DAA treatments was not statistically significant, further in-depth study is needed. Last, the analysis was based on the number of treatment measurements of 30 PLADs over eight quarters. Limited numbers of observations may also restrict the statistical power of LCTM analysis. If patient information is accessible, comprehensive in-depth regression analysis of the factors that affect the DAA treatment of hepatitis C could be performed.
## Conclusions
Central negotiations to reduce prices of DAAs resulted in inclusion of DAA treatment under the universal health insurance, which are critical elements that support scaling up access to hepatitis C treatment in China. However, current treatment rates are still far below the global target. Targeting the PLADs lagged behind through raising public awareness, strengthening capacity of the healthcare providers by roving training, and integrate prevention, screening, diagnosis, treatment and follow-up management of hepatitis C into the existing services are needed.
## Supplementary Information
Additional file 1. Supplementary tables and figures.
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|
---
title: 'Response to COVID-19 recommended preventive behavioral messages among Guraghe
zone communities, South Ethiopia: using constructs of the Extended Parallel Process
Model (EPPM)'
authors:
- Abdurezak Kemal
- Kenzudin Assfa
- Bisrat Zeleke
- Mohammed Jemal
- Musa Jemal
- Shemsu Kedir
- Amare Zewdie
- Samuel Dessu
- Fedila Yassin
- Adane Habtie
journal: BMC Infectious Diseases
year: 2023
pmcid: PMC10043852
doi: 10.1186/s12879-023-08087-1
license: CC BY 4.0
---
# Response to COVID-19 recommended preventive behavioral messages among Guraghe zone communities, South Ethiopia: using constructs of the Extended Parallel Process Model (EPPM)
## Abstract
### Introduction
The World Health Organization declared COVID-19 is a pandemic disease. Countries should take standard measures and responses to battle the effects of the viruses. However, little is known in Ethiopia regarding the recommended preventive behavioral messages responses. Therefore, the study aimed to assess the response to COVID-19 recommended preventive behavioral messages.
### Methods
Community-based cross-sectional study design was carried out from 1 to 20, July 2020. We recruited 634 respondents by using a systematic sampling method. Data were analyzed using Statistical Package Software for Social Sciences version 23. Association between variables were explored using a bivariable and multi variable logistic regression model. The strength of the association is presented using odds ratio and regression coefficient with $95\%$ confidence interval. A p-value of less than 0.05 was declared statistically significant.
### Results:
Three hundred thirty-six ($53.1\%$) of respondents had good response to recommended preventive behavioral messages. *The* general precise rate of the knowledge questionnaire was $92.21\%$. The study showed that merchant was 1.86 (p ≈ 0.01) times more likely respond to COVID-19 recommended preventive behavioral messages than government-employed. Respondents who scored one unit increase for self-efficacy and response-efficacy, the odds of responding to COVID-19 recommended preventive behavioral messages were increased by 1.22 ($p \leq 0.001$), and 1.05 times ($$p \leq 0.002$$) respectively. Respondents who scored one unit increase to cues to action, the odds of responding to COVID-19 recommended preventive behavioral messages were $43\%$ ($p \leq 0.001$) less likely.
### Conclusion
Even though respondents were highly knowledgeable about COVID-19, there is a lower level of applying response to recommended preventive behavioral messages. Merchant, self-efficacy, response efficacy, and cues to action were significantly associated with response to recommended preventive behavioral messages. Like merchants, government employer should be applying preventive behavioral messages and also, participants’ self and response efficacy should be strengthened to improve the response. In addition, we should be changed or modified the way how-to deliver relevant information, promoting awareness, and also using appropriate reminder systems to preventive behavioral messages.
## Introduction
The World Health Organization declared COVID-19 is a pandemic disease [1]. Countries should take standard measures and responses to tackle the effects of the viruses. The global community is struggling to decrease and finally halt the spread of Covid-19 through improving the knowledge and practice of COVID-19 prevention methods, testing, and screening [2, 3].
Among the general public, we focus on practicing protective behaviors which intended for behavioral response. To oblige human-to-human transmission, public health interventions, such as hand hygiene and wearing masks, are performed [4, 5]. To prevent transmission of the virus during infectious disease pandemics following proper handwashing hygiene is an activity that is often recommended by health authorities [6], preventive behaviors that was frequently researched [7], and also have confirmed to affect the feast of pandemics [8] significantly. Furthermore, the protective effect of wearing facial masks to reduce respiratory virus transmission is widely supported in the literature [9, 10].
The Korean government were recommended practicing hand hygiene and wearing facial masks. During pandemic frequently occur avoidance behaviors due to fear of transmission were canceling or postponing social events, reducing the use of public transport, keeping children out of school, and avoiding crowded places [11–13]. Individual avoidance behaviors that limit contact with others are forms of social distancing known as ‘informal social distancing’ [14]. Concerning to the behavioral response, we mainly focus on practicing recommended behaviors that is practicing hand hygiene and wearing facial masks, as well as social distancing that is reducing the use of public transport, avoiding crowded places, and postponing or canceling social events.
Risk Perception Attitude framework hypothesizes that an individual’s efficacy beliefs act as a key factor along with perceived risk in driving behavioral changes which is derived from the Extended Parallel Process Model [15]. Conceptually, efficacy beliefs comprise (a) beliefs about the effectiveness of the recommended response in discouraging, prevention of a health risk that is response efficacy or outcome expectations, and (b) an individuals’ perceived ability to exert personal control that is self-efficacy [16].
Ethiopia became a Covid-19 affected country on March 13, 2020. The government of Ethiopia took several progressive measures to combat the Covid-19 pandemic. Commonly known recommended preventive behavioral messages of COVID-19 in Jimma University Medical Center were properly washing hands with soap and water ($95.5\%$), not touching eye-nose-mouth with unwashed hands ($92.7\%$), and avoiding crowded places ($90.3\%$) [17]. Furthermore, COVID-19 recommended preventive behavioral messages in Addis Ababa residents were $85\%$ and $83\%$ hand washing and social distancing respectively [18]. Despite all these important public health containment measures, the outbreak still has the potential for greater loss of life in Ethiopia if the community is unable to shape the regular behavioral and sociocultural norms that would facilitate the spread of the disease [19]. Studies were conducted in Knowledge, Attitude and Practice (KAP) on COVID-19, little is known in Ethiopia regarding response to the recommended preventive behavioral messages using the constructs of the Extended Parallel Process Model. Therefore, the aim of this study was to assess the response to COVID-19 recommended preventive behavioral messages based on this model.
## Study design and setting
This community-based cross-sectional study design was carried out from 1 to 20 July 2020 in Guraghe zone town administrative communities. The zone's capital city is Wolkite town, which is 158 km far from South of Addis Ababa. It comprises five town administrations; which are Wolkite, Butajira, Gunchire, Bu’i and Emdibir towns. They comprise 23 kebeles which are Wolkite have 7 kebeles, Gunchire have 4 kebeles, Butajira have 6 kebeles, Emdibir have 2 kebeles and Bu’i have 4 kebeles in total with a different number of population size.
## Study population
All adults of Guraghe zone town administrations; whose age greater than or equal to 18 years old were our source population. The study population was all sampled individuals living in the selected kebeles during the data collection period. The inclusion criteria were all selected individuals above 18 years old, and permanent residents of the selected kebeles. The *Exclusion criteria* were individuals in the selected kebele, who are patients with mental disorder unable to communicate properly, acute sick-looking persons who are unable to communicate during data collection, and under 18 years old.
## Sample size and sampling procedure
The sample size was calculated using single population proportion formula (n = (Zα/2)2[(PQ)/ (w)2]) with $95\%$ confidence interval, $5\%$ margin of error and proportion of response to Covid-19 recommended preventive behavioral messages of $50\%$ since there was no previous study done in the area.
The design effect was determined to be 1.5 for having two stages in selecting study subjects. Hereafter, the calculation using Epi info version 3.5.3 yielded 576. The tolerable non-response rate is $10\%$. The final sample size became 634. Multi-stage systematic sampling technique was used to select study participants. In the first stage, we randomly select three town administrations namely Gunchire, Butajira and Wolkite of the Guraghe zone from a total of five. Next, we select respective kebeles that is 2 kebeles from Gunchire town, 3 kebeles from Butajira town and 4 kebeles from Wolkite town using a simple random sampling procedure. The final sample size was allocated proportionally to their total households in respective kebeles. “ Kebele” is the lowermost administrative body in Ethiopia which includes at least 1000 households or population of 5000 people.
Then a systematic sampling technique was applied with sampling interval of every 8th household from selected town kebeles. We interviewed the most decision-makers in the household either father or mother. If father or mother was not avail during data collection time, one eligible respondent was selected based on the lottery method from more than one eligible group. If eligible respondents are not found in the selected household, the immediate next house was visited. Closed house or unavailable respondents at the time of data collection continent time was rearranged to revisit them again without reluctance until found the respondent.
## Data collection procedure and data quality control
The data collection tools were semi-structured questionnaires, which are adapted from WHO and similar literature conducting on COVID-19 [15, 20, 21]. It was initially prepared in English then translated into Amharic by those proficient in the language and checked for consistency. Data collection was conducted using a face-to-face interview with selected households through applying the recommended preventive behavioral messages.
Before the main data collection was started pre-testing of the instrument on $5\%$ (32 household) of the respondents conducted in Werabe town for clarity and flow of the questionnaire. Based on the finding appropriate correction was taken (including estimation of the time needed for data collection and those questions found to be unclear or confusing was modified). Reliability was checked using Cronbach alpha. From all reliability scores, the minimum reliability score was perceived efficacy which was 0.72.
Six data collectors who have health professional background was recruited and trained by an investigator with an assistant for two days. Two BSc Public Health professional were recruited as supervisors to monitor the progress of data collection and maintaining data quality. The supervisors were oriented on how to solve problems and ambiguities on the questions. The principal investigators were communicated with supervisors daily and followed the data collection closely. The collected data were checked for completeness and consistency.
## Study variables and operational definition
The dependent variable was response to COVID-19 recommended Preventive Behavioral Messages. The independent variables were sociodemographic and economic factors, knowledge, perceived susceptibility, perceived severity, self-efficacy, response efficacy, cues to action, message exposure, and recall.
## Response to recommended preventive behavioral messages
Involves eight items of preventive behavioral messages were computed. Responses were recorded with Yes/No form. Individual scores for each preventive action were summed up and a composite score of response to message was created. Finally, the response was dichotomized based on the mean response score of the respondents. Those scored above the mean was considered as having good response and the rest as having poor response to COVID-19 recommended preventive behavioral messages.
## Knowledge about COVID-19
These tools were adapted from WHO resources, which encompasses 14 questions. All the questions elicited a “yes/no” answer. Overall, three labels of knowledge status were created based on the number of correct responses that are; low (< = 8 of 14), moderate (9–10 of 14), and high (> = 11 of 14 items [22].
To measure knowledge on the COVID-19 virus, 14 items were presented. The mean ± Standard Deviation score was 12.91 ± 1.36 for the respondents with, a range score of 7–14. *The* general correct answer rate of the knowledge questionnaire was $92.21\%$ ($\frac{12.91}{14}$*100) while the range of correct answer rates for all respondents were between 50 to $100\%$ (Table 3).Table 3Knowledge of COVID-19 among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)Knowledge of symptomsFrequency (Percentage)CorrectIncorrectThe main clinical symptoms of COVID-19 are fever, fatigue, dry cough, and myalgia622 ($98.3\%$)11 ($1.7\%$)Unlike the common cold, stuffy nose, runny nose, and sneezing are less common in persons infected with the COVID-19 virus571 ($90.2\%$)62 ($9.8\%$)Knowledge of high risk and prognosisNot all persons with COVID-19 will develop severe cases. Only those who are elderly, have chronic illnesses and obese are more likely to be severe cases599 ($94.6\%$)34 ($5.4\%$)There currently is no effective cure for COVID-19, but early symptomatic and supportive treatment can help most patients recover from the infection623 ($98.4\%$)10 ($1.6\%$)Knowledge about Mode of transmissions and infectiousnessThe COVID-19 virus spreads via respiratory droplets of infected individuals595 ($94.0\%$)38 ($6.0\%$)Eating or contacting wild animals would result in the infection by the COVID-19 virus491 ($77.6\%$)142 ($22.4\%$)Persons with COVID-19 cannot infect the virus to others when a fever is not present*165 ($26.1\%$)468 ($73.9\%$)Knowledge about ways of preventionProper washing hand with soap and water is one method of preventing COVID-19630 ($99.5\%$)3 ($0.5\%$)One way of prevention of COVID-19 is not touching the eye, nose by unwashed hands627 ($99.1\%$)6 ($0.9\%$)To prevent the infection by COVID-19, the individuals should avoid going to crowded places such as train stations and avoid taking public transportations617 ($97.5\%$)16 ($2.5\%$)People who have contact with someone infected with the COVID-19 virus should be immediately isolated in a proper place610 ($96.4\%$)23 ($3.6\%$)Ordinary residents can wear general medical masks to prevent the infection by the COVID-19 virus592 ($93.5\%$)41 ($6.5\%$)Isolation and treatment of people who are infected with the COVID- 19 virus are effective ways to reduce the spread of the virus546 ($86.3\%$)87 ($13.7\%$)Children and young adults do not need to take measures to prevent the infection by the COVID-19 virus*52 ($8.2\%$)581 ($91.8\%$)*Correction rate calculated from ‘no’ response for false statements
## Perceived severity
It was measured through three items adapted from the Risk Behavior Diagnosis (RBD) scale using a five-point Likert scale. The scales are range from 1 to 5 that is from strongly agree to strongly disagree. The response was summed up and the total score was computed with possible values ranging from 3 to 15 then the score was treated as a continuous variable.
## Perceived susceptibility
It was measured through three items adapted from the Risk Behavior Diagnosis scale using a five-point Likert scale. The scales are range from 1 to 5 that is from strongly agree to strongly disagree. The response was summed up and the total score was computed with possible values ranging from 3 to 15 then the score was treated as a continuous variable.
## Perceived self-efficacy
It was measured using four items adapted from the Risk Behavior Diagnosis scale using a five-point Likert scale. The scales are range from 1 to 5 that is from strongly agree to strongly disagree. The response was summed up then the total score was computed with possible values ranging from 4 to 20. The score was treated as a continuous variable.
## Perceived response-efficacy
It was measured using four items adapted from the Risk Behavior Diagnosis scale using a five-point Likert scale. The scales are range from 1 to 5 that is from strongly agree to strongly disagree. The response was summed up and the total score was computed with possible values ranging from 4 to 20 then the score was treated as a continuous variable.
## Message exposure and recall
This tool adapted from previously published research on HIV is contextualized to fit into this specific study [15]. It was measured using six items including a preferred source of information, preferred channels, and frequently heard the message, the specific message heard of /seen, preferred message appeals.
## Cues to action
This tool adapted from previously published research on breast self-examination is contextualized to fit into this specific study [21]. It was measured using six items with yes or no responses. The score was summed up and treated as a continuous variable.
## Perceived threat
The score of perceived susceptibility and perceived severity was summed up to form a score of perceived threat.
## Perceived efficacy
The score of perceived self-efficacy and perceived response efficacy was summed up to form a score of perceived efficacies.
## Data processing and analysis
The completed data were checked by the principal investigator in daily basis for completeness and consistency, then coded, and entered into *Epi data* 3.3.1 software and exported to SPSS version 23 statistical software for data analysis. Descriptive statistics were used to describe the study variables and presented using text, frequency, and proportions, and mean with standard deviation. In addition, variables with dichotomous outcomes were assessed using chi-square statistics. To identify factors associated with response to COVID-19 recommended preventive behavioral messages, a logistic regression model was used.
The bivariable logistic regression model was used for each explanatory variable to identify candidate variables with p-value < 0.25 for multivariable logistic regression. Adjusted odds ratio (AOR) with $95\%$ CI was estimated to identify the associated factors. Finally, statistical significance was declared at p-value < 0.05 in multivariable logistic regression. Test for model fitness was done by using the Hosmer–Lemeshow model test. Hosmer and Lemeshow’s goodness-of-fit test was chi-square of 12.94 with a p-value of 0.11. Multi collinearity of the independent variables was checked by a variance inflation factor (VIF).
## Sociodemographic and economic characteristics of the participants
Six hundred thirty-three respondents were approached in this study, which indicated the response rate of $99.8\%$. The mean ± Standard Deviation age of the respondents was 34 ± 12.34 years. More than half of 325 ($51.3\%$) were male respondents. More than two-fifth 278 ($43.9\%$) of respondents were Muslim religion followers, 389 ($61.5\%$) were married and 247 ($39\%$) were joined higher education. The majority of the respondents; 322 ($69.1\%$) were government employees in their occupation. The median (Interquartile Range) of the respondents’ monthly income was 2000 [3200] Ethiopian Birr (Table 1).Table 1Socio-demographic and economic characteristics of response to COVID-19 Preventive Behavioral Messages among Gurage Zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)VariablesResponse/categoriesFrequency (n)Percentage (%)Age (years) < = 2928344.730–3918128.640–498813.9 > = 508112.8SexMale32551.3Female30848.7Marital statusSingle24438.5Married38961.5ReligionMuslim27843.9Orthodox22034.8Protestant9214.5Catholic436.8EducationCannot read and write375.8Read and write12419.6Primary education9014.2Secondary education13521.3Higher education24739.0OccupationMerchant14122.3Government employed22235.1Farmer487.6Daily laborer487.6Housewife7912.5Student9515.0Monthly income (Ethiopian Birr) < = 49911718.5500–149916526.11500–299910015.83000–499917427.5 > = 50007712.2
## Preventive behavioral messages exposure to COVID-19
All of the respondents had exposed to COVID-19 preventive behavioral messages in the last six months. Television screen was the major channels to receive the information 570 ($90.0\%$) and also preferred channel to see or hear message by respondents 573 ($90.5\%$). From frequently heard of/seen message components, 626 ($98.9\%$) respondents were exposed to properly washing hands with soap and water followed by not touching eye-nose-mouth with unwashed hands 587 ($92.7\%$). Factual through education was preferred a message appeal among 447 ($70.6\%$) respondents (Table 2).Table 2Preventive behavioral messages exposure and recall to COVID-19 among Gurage zone town administration communities, South Ethiopia July 2020 ($$n = 633$$)QuestionsCategoriesResponseYes (%)No (%)From where you received the information about COVID-19 preventive behavioral messages in the last 6 monthsHealth institutions435 ($68.7\%$)198 ($31.3\%$)Radio447 ($70.6\%$)186 ($29.4\%$)Television570 ($90.0\%$)63 ($10.0\%$)Parents/spouse249 ($39.3\%$)384 ($60.7\%$)Friends311 ($49.1\%$)322 ($50.9\%$)Religious institutions400 ($63.2\%$)233 ($36.8\%$)Posters144 ($22.7\%$)489 ($77.3\%$)Leaflets/brochures23 ($3.6\%$)610 ($96.4\%$)Social media63 ($10.0\%$)570 ($90.0\%$)Preferred channels to hear/seeTelevision573 ($90.5\%$)60 ($9.5\%$)Radio421 ($66.5\%$)212 ($33.5\%$)Peer discussions274 ($43.3\%$)359 ($56.7\%$)Posters155 ($24.5\%$)478 ($75.5\%$)Leaflets/brochures34 ($5.4\%$)599 ($94.6\%$)Social media68 ($10.7\%$)565 ($89.3\%$)Specific messages frequently heard of/seenProperly washing hands with soap and water for a minimum of 30 s626 ($98.9\%$)7 ($1.1\%$)Not touching eye-nose-mouth with unwashed hands587 ($92.7\%$)46 ($7.3\%$)Avoiding walking to crowded places without face mask/social distancing549 ($86.7\%$)84 ($13.3\%$)Staying at home except for necessary issues431 ($68.1\%$)202 ($31.9\%$)Preferred message appealsDramatic/funny303 ($47.9\%$)330 ($52.1\%$)Factual through education447 ($70.6\%$)186 ($29.4\%$)Fear arousal messages108 ($17.1\%$)525 ($82.9\%$)Two-sided messages351 ($55.5\%$)282 ($44.5\%$)One sided message35 ($5.5\%$)598 ($94.5\%$)Positive message82 ($13.0\%$)551 ($87.0\%$)Negative message37 ($5.8\%$)596 ($94.2\%$)
## Multi-dimensional knowledge status of COVID-19
Multidimensional knowledge (MDK) status encompasses symptoms, risk factors and prognosis, transmission modes, and preventive methods analysis of knowledge of COVID-19 indicated that $1.4\%$, $4.6\%$, and $94\%$ of the communities were low, moderately, and highly knowledgeable respectively (Fig. 1).Fig. 1Pie charts show multi-dimensional knowledge status about COVID-19 among Guraghe zone town administration communities, South Ethiopia July 2020
## Risk perception and efficacy of the respondents
A detailed description of perception items intended to response to COVID-19 recommended preventive behavioral messages among Gurage zone town administration communities, South Ethiopia, July 2020 (Table 4).Table 4Perceived threat of COVID-19 among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)QuestionsResponse CategoriesPerceived susceptibility to COVID-19Strongly DisagreeDisagreeUndecidedAgreeStrongly AgreeIt is likely that I will get COVID-1920 ($3.2\%$)170 ($26.9\%$)32 ($5.1\%$)374 (59.1)37 ($5.8\%$)I am at risk for getting COVID-1942 ($6.6\%$)193 ($30.5\%$)70 ($11.1\%$)302 ($47.7\%$)26 ($4.1\%$)*It is* possible that I will get COVID-1915 ($2.4\%$)105 ($16.6\%$)33 ($5.2\%$)427 ($67.5\%$)53 ($8.4\%$)Perceived severity of COVID-19I believe that COVID-19 is severe3 ($0.5\%$)26 ($4.1\%$)6 ($0.9\%$)320 ($50.6\%$)278 ($43.9\%$)I believe that COVID-19 has serious negative consequences3 ($0.5\%$)23 ($3.6\%$)8 ($1.3\%$)330 ($52.1\%$)269 ($42.5\%$)I believe that COVID-19 is extremely harmful2 ($0.3\%$)37 ($5.8\%$)11 ($1.7\%$)322 ($50.9\%$)261 ($41.2\%$)Perceived response efficacy for Covid-19 preventive behavioral messages Properly washing hands with soap and water could prevent risk of COVID-1907 ($1.1\%$)298 ($47.1\%$)328 ($51.8\%$)0 Not touching eye-nose-mouth with unwashed hands could prevent risk of COVID-192 ($0.3\%$)10 ($1.6\%$)5 ($0.8\%$)303 ($47.9\%$)313 ($49.4\%$) Avoiding walking to crowded places without face mask could prevent risk of COVID-19014 ($2.2\%$)2 ($0.3\%$)319 ($50.4\%$)298 ($47.1\%$) Staying at home could prevent risk of COVID-195 ($0.8\%$)25 ($3.9\%$)18 ($2.8\%$)348 ($55\%$)237 ($37.4\%$)Perceived Self-efficacy to perform Covid-19 preventive behavioral messages *It is* easy for me to engage on proper hand washing with soap and water which prevent risk of COVID-191 ($0.2\%$)40 ($6.3\%$)29 ($4.6\%$)397 ($62.7\%$)166 ($26.2\%$) I am able to adapt for not touching eye-nose-mouth with unwashed hands which prevent risk of COVID-198 ($1.3\%$)65 ($10.3\%$)71 ($11.2\%$)400 ($63.2\%$)89 ($14.1\%$) I am confident enough for avoiding crowded places could prevent risk of COVID-1952 ($8.2\%$)185 ($29.2\%$)149 ($23.5\%$)214 ($33.8\%$)33 ($5.2\%$) Staying at home is easy for me which could prevent risk of COVID-19258 ($40.8\%$)203 ($32.1\%$)79 ($12.5\%$)84 ($13.3\%$)9 ($1.4\%$)
## Mean, Standard Deviation and reliability Scores of constructs of EPPM
Regarding risk perceptions, respondents had a Perceived threat mean score of 23.05 (± 3.870) and a perceived efficacy mean score of 30.49 (± 3.630). Cronbach’s α score for all the constructs was greater than 0.7 (Table 5).Table 5Respondents mean, standard deviation and reliability scores of constructs of the Extended Parallel Process Model among Gurage Zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)VariablesNumber of itemsResponse RangeRespondent’s RangeMean (± SD)Cronbach’s αPerceived threat60–306–3023.05 (± 3.87)0.81Perceived susceptibility30–153–1510.13 (± 2.71)0.85Perceived severity30–153–1512.93 (± 2.07)0.91Perceived efficacy80–4017–4030.49 (± 3.63)0.72Perceived response efficacy40–208–2017.61 (± 2.20)0.87Perceived self-efficacy40–204–2012.88 (± 2.83)0.72
## Response to COVID-19 recommended preventive behavioral messages and risk perception (Perceived Threat and Perceived Efficacy)
From 425 respondents who had lower perceived threat, 194 ($45.6\%$) had good response to preventive behavioral messages and the rest had poor. Regarding perceived efficacy from 324 respondents who were contained in low perceived efficacy category 136 ($42\%$) had good response to preventive behavioral messages while the rest had poor response (Table 6).Table 6Shows chi-square association between response to COVID-19 disease preventive behavioral messages with risk perceptions among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)Perceived threat- efficacy interactionCategoriesResponse to preventive behavioral messages/practicePoor (%)Good (%)Perceived threatLow threat231 (54.4)194 (45.6)High threat105 (50.5)103 (49.5)Perceived efficacyLow efficacy188 (58.0)136 (42.0)High efficacy148 (47.9)161 (52.1)
## Cues to action to prevent Covid-19 disease
To measure cues to action variable score to prevent COVID-19, six questions were raised. The mean ± Standard Deviation score was 8.04 ± 1.02 for the respondents with, a range score of 6–12 (Table 7).Table 7Cues to action to prevent COVID-19 disease among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)ItemsFrequency (Percentage)YesNoHave you ever seen /heard about a person who follow recommended preventive behavioral messages for COVID-19 in last one month?591 ($93.4\%$)42 ($6.6\%$)Have you ever seen /heard of person who exposed to COVID-19 in the last one month?546 ($86.3\%$)87 ($13.7\%$)Have you ever heard through mass media about recommended preventive behavioral messages for COVID-19 during last one month?608 ($96.1\%$)25 ($3.9\%$)Have you ever heard through Health care providers about the recommended preventive behavioral messages for COVID-19 during last one month?495 ($78.2\%$)138 ($21.8\%$)Have you ever heard through security workers about the recommended preventive behavioral messages for COVID-19 during last one month?262 ($41.4\%$)371 ($58.6\%$)Do you have a family member who exposed to COVID-19?2 ($0.3\%$)631 ($99.7\%$)
## Response to COVID-19 recommended preventive behavioral messages
Regarding the measure of recommended preventive behavioral messages for COVID-19, eight items were presented. The mean ± Standard Deviation score was 4.38 ± 2.43 for the respondents with, a range score of 0–8. Predominantly undertaken behavioral items which prevent the risk of COVID-19, were engagement on proper handwashing with soap and water, used cover/elbow during coughing/sneezing, avoided walking to crowded places without wearing face mask, and usage of alcohol/sanitizer for rubbing and no contact with surfaces. While 366 ($53.1\%$) of respondents had good response to COVID-19 recommended preventive behavioral messages, around $46.9\%$ of respondents scored below the mean makes them poor response category (Table 8).Table 8Response to COVID-19 recommended preventive behavioral messages for the last consecutive days among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)Behavioral variablesFrequency (Percentage)Of the last consecutive few-weeks did you…Yes (%)No (%)Engage on proper hand washing with soap and water, which prevent risk of COVID-19563 ($88.9\%$)70 ($11.1\%$)Stopped shaking hands while giving greeting304 ($48\%$)329 ($52\%$)Avoided proximity including while greeting (at least two-foot jump distance in between)175 ($27.6\%$)458 ($72.4\%$)Practiced not touched eye-nose-mouth with unwashed hands304 ($48\%$)329 ($52\%$)Avoided walking to crowded places without wore face mask402 ($63.5\%$)231 ($36.5\%$)Practiced staying at home except for necessary issues175 ($27.6\%$)458 ($72.4\%$)Used cover /elbow during coughing/sneezing454 ($71.7\%$)179 ($28.3\%$)Used alcohol/sanitizer for rubbing and no contact with surfaces397 ($62.7\%$)236 ($37.3\%$)Response to recommended Preventive Behavioral messages (8 items)Good response336 ($53.1\%$)Poor response297 ($46.9\%$)
## Factors associated with response to COVID-19 recommended preventive behavioral messages
On bivariable logistic regression analysis, variables candidate for multivariable analysis were obtained from sociodemographic and economic factors; age, religion, education, occupation, and income. Knowledge and constructs of EPPM were also candidate for multivariable analysis (Table 9). On multivariable logistic regression analysis, merchant was 1.86 times more likely to respond to COVID-19 recommended preventive behavioral messages than government-employed [AOR = 1.86 (1.17, 2.96), p value = 0.01]. Respondents who scored one unit increase for self-efficacy and response-efficacy, the odds of responding to COVID-19 recommended preventive behavioral messages were increased by 1.22 and 1.05 times respectively. Respondents who scored one unit increase to cues to action, the odds of responding to COVID-19 recommended preventive behavioral messages were $43\%$ less likely (Table 10).Table 9Bivariable logistic regression for response to COVID-19 recommended preventive behavioral messages among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)VariablesCategoriesResponse to COVID-19 recommended preventive behaviorCOR ($95\%$ CI)p-valuePoor behaviorGood behaviorAge < = 29141 ($49.8\%$)142 ($50.2\%$)10.15*30–3998 ($54.1\%$)83 ($45.9\%$)0.84 (0.58,1.22)0.3640–4945 ($51.1\%$)43 ($48.9\%$)0.95 (0.59,1.53)0.83 > = 5052 ($64.2\%$)29 ($35.8\%$)0.55 (0.33,0.92)0.02SexMale170 ($52.3\%$)155 ($47.7\%$)1Female166 ($53.9\%$)142 ($46.1\%$)0.94 (0.69,1.28)0.69Marital statusSingle134 ($54.9\%$)110 ($45.1\%$)0.89 (0.64,1.22)0.46Married202 ($51.9\%$)187 ($48.1\%$)1ReligionMuslim148 ($53.2\%$)130 ($46.8\%$)1Orthodox110 ($50.0\%$)110 ($50.0\%$)1.14 (0.80,1.62)0.47Protestant51 ($55.4\%$)41 ($44.6\%$)0.92 (0.57,1.47)0.71Catholic27 ($62.8\%$)16 ($37.2\%$)0.68 (0.35,1.31)0.24*EducationCannot read and write23 ($62.2\%$)14 ($37.8\%$)0.41 (0.20,0.83)0.01*Read and write71 ($57.3\%$)53 ($42.7\%$)0.50 (0.32,0.77)0.00*Primary education60 ($66.7\%$)30 ($33.3\%$)0.33 (0.20,0.56) < 0.001*Secondary education83 ($61.5\%$)52 ($38.5\%$)0.42 (0.27,0.64) < 0.001*Higher education99 ($40.1\%$)148 ($59.9\%$)1Occupation of the respondentGovernment employed92 ($41.4\%$)130 ($58.6\%$)1Merchant87 ($61.7\%$)54 ($38.3\%$)0.44 (0.29,0.68) < 0.001*Farmer34 ($70.8\%$)14 ($29.2\%$)0.29 (0.15,0.57) < 0.001Daily laborer31 ($64.6\%$)17 ($35.4\%$)0.39 (0.20,0.74)0.004Housewife49 ($62.0\%$)30 ($38.0\%$)0.43 (0.26,0.73)0.002Student43 ($45.3\%$)52 ($54.7\%$)0.86 (0.53,1.39)0.53Income < = 49973 ($62.4\%$)44 ($37.6\%$)0.59 (0.33,1.05)0.07*500–149988 ($53.3\%$)77 ($46.7\%$)0.85 (0.50,1.47)0.561500–299954 ($54.0\%$)46 ($46.0\%$)0.83 (0.46,1.51)0.543000–499983 ($47.7\%$)91 ($52.3\%$)1.07 (0.63,1.83)0.81 > = 500038 ($49.4\%$)39 ($50.6\%$)1Source of informationβ = 0.031.034 (0.95,1.12)0.42KnowledgePoor27 ($71.1\%$)11 ($28.9\%$)0.440 (0.21,0.90)0.03*Good309 ($51.9\%$)286 ($48.1\%$)1Perceived susceptibilityβ = 0.341.11 (1.04,1.17)0.10*Perceived severityβ = 0.051.05 (0.97,1.14)0.20*Self-efficacyβ = 0.221.24 (1.17,1.32) < 0.001*Response efficacyβ = 0.041.01 (0.44,1.12)0.11*Cues to actionβ = 0.670.51 (0.43,0.62) < 0.001*COR crude odds ratio, CI confidence interval*Candidate variables for multiple regression which their p-value < 0.25 in the bivariable resultsTable 10Multivariable logistic regression analysis for response to COVID-19 recommended preventive behavioral messages among Gurage zone town administration communities, South Ethiopia, July 2020 ($$n = 633$$)VariablesCategoriesCOR ($95\%$ CI)AOR ($95\%$ CI)p-valueOccupation of the respondentGovernment employed1Merchant0.44 (0.29,0.68)1.86 (1.17, 2.96)0.01*Farmer0.29 (0.15,0.57)0.53 (0.25, 1.14)0.10Daily laborer0.39 (0.20,0.74)0.77 (0.37, 1.61)0.48Housewife0.43 (0.26,0.73)1.14 (0.61, 2.14)0.68Student0.86 (0.53,1.39)1.72 (0.98, 3.04)0.06Cues to actionβ = 0.570.51 (0.43,0.62)0.57 (0.47, 0.69) < 0.001**Self- efficacyβ = 0.201.24 (1.17,1.32)1.22 (1.14, 1.30) < 0.001**Response-efficacyβ = 0.131.01 (0.44,1.12)1.05 (1.03,1.09)0.00*Hosmer and Lemeshow’s goodness-of-fit test was chi square of 12.94 with P-value of 0.11AOR Adjusted odds ratio*Variables statistically significant at p-value < 0.05**Variables statistically significant at p-value < 0.001
## Discussion
This study was assessed response to COVID-19 recommended preventive behavioral messages which is one of the main implications for minimizing the rapid spread of the coronavirus. The COVID-19 is coming from a relatively new virus that has had overwhelming effects within a short period meanwhile it was first detected in December 2019. Predominantly undertaken behavioral items, which could prevent the risk of COVID-19, were engagement on proper handwashing with soap and water, used cover/elbow during coughing/sneezing, avoided walking to crowded places without wearing face mask and usage of alcohol/sanitizer for rubbing and no contact with surfaces.
*In* general, in this study the behavioral messages variable score was encompassing majorly performed preventive methods which were classified as good and poor response to COVID-19 recommended preventive behavioral messages. Even though the respondents were highly knowledgeable regarding prevention methods for infection of COVID-19, $53.1\%$ of the respondent’s score were above the mean or good response to COVID-19 recommended preventive behavioral messages. This implies that there is a minimum level of response to COVID-19 recommended preventive behavioral messages for this specific study area than Jimma University Medical Center visitors and Addis Ababa residents [17, 18].
This finding is lower than the study done in Jimma University Medical Center visitors; properly washing hands with soap and water ($95.5\%$), not touching eye-nose-mouth with unwashed hands ($92.7\%$), and avoiding crowded places ($90.3\%$) were commonly known recommended preventive behavioral messages of COVID-19 [17]. Furthermore, the study done in Addis Ababa residents showed that some of the recommended preventive behavioral messages of COVID-19 were $85\%$ and $83\%$ hand washing and social distancing respectively [18]. To close the clear gap between what was known and practicing the recommended behavioral messages; different stakeholders initiating start from individual to the large public should be struggling to minimize this pandemic disease.
These discrepancies may be raised from the time when data collection was conducted. As a result of redundantly hearing and seen about COVID-19; there was ignorance and lack of commitment for applying the recommended preventive behavioral messages. In Jimma University Medical Center visitors and Addis Ababa residents’ data collection was conducted early in the pandemic, so that they should be seriously applied concerning to preventive practice. Moreover, *Ethiopia is* known for modest coverage and shortage of water supply and handwashing facilities, a high rate of overcrowded living conditions, frequent social and religious ceremonies, and a high unemployment rate calling for crucial efforts were reason for not to be sustain in coherent manner regarding response to recommended preventive behavioral messages.
The study showed that merchant was 1.86 times more likely to respond to COVID-19 recommended preventive behavioral messages than government-employed [AOR = 1.86 (1.17, 2.96), p value = 0.01]. In line with this study, a study done in Jimma University Medical Center visitors working for private and government businesses were positive predictors of avoidance of shaking hands for the greeting which is majorly applied preventive practice for COVID-19 [17]. In line with this study, a study done in Addis Ababa found that occupation was found to be associated with the practice of precautionary measures against COVID-19 [18]. This implies that mainly merchants are more cautious for these catastrophic diseases than government employer because most of the time they have close contact to different people so that follow strict instructions.
A one unit increase for the score of self-efficacies, the odds of responding to COVID-19 recommended preventive behavioral messages were increased by 1.22 times. In line with this study, a study done in Jimma University Medical Center visitors stated that visitors who felt self-efficacious to successfully control COVID-19 were more likely to avoid handshaking to combat COVID-19. It depicts that these people were more concerned about contacts and adapted to hygienic precautions [17]. To increase the magnitude of preventive practice, the study participants have a capacity on engaging in proper handwashing with soap and water, the ability to adapt to not touching eye-nose-mouth with unwashed hands, being confident enough for avoiding crowded places, and easy for me to stay home.
A one unit increase for the score of response efficacy, the odds of responding to COVID-19 recommended preventive behavioral messages were increased by 1.05. This implies that the score of response efficacy was mainly handled with performing the recommended preventive behavioral messages. So that to increase applying preventive behavioral messages, the communities should be strengthening through properly washing hands with soap and water, not touching eye-nose-mouth with unwashed hands, avoiding walking to crowded places without a face mask, and also staying at home.
Respondents who scored one unit increase to cues to action, the odds of responding to COVID-19 recommended preventive behavioral messages were $43\%$ less likely. This might be redundantly hearing or seeing extreme fear arousal messages, and also gathering misinformation leads to ignorance of the messages (defensive avoidance) or just not response at all to recommended COVID-19 preventive behavioral messages [15]. To increase the level of response to recommended preventive behavioral messages, the communities should give emphasis to the recommended preventive behavioral messages and also strengthening through avoiding ignorance, misinformation and filling extreme fear for COVID-19 pandemic.
## Limitations of the study
Though this study was mainly emphasized on preventive behavioral messages, difficult to assess the respondents’ actual behavior during once upon a time that is during data collection period. Even though many variables scored as continuous one that considered us our strength, it was faced challenge to discuss with similar categorical variables. Furthermore, unique constructs of the model that was response efficacy and cues to action were difficult for discussion with other literature. We were incapable to directly compare the perceived risk and efficacy of COVID-19 for the same respondents. Therefore, it is difficult to compare the absolute values of the perceived risk and efficacy. Our study aimed to identify factors affecting response to COVID-19 recommended preventive behavioral messages, rather than testing theoretical hypothesis of Extended Parallel Process Model.
## Conclusion
Even though respondents were highly knowledgeable about Covid-19, there is a lower level ($53.1\%$) of response to recommended preventive behavior messages for this catastrophic disease. Merchant, self-efficacy, response efficacy, and cues to action were significantly associated with response to recommended preventive behavioral messages. Like merchants, government employer should be applying preventive behavioral messages and also, participants’ self and response efficacy should be strengthened to improve the response. In addition, we should be changed or modified the way how-to deliver relevant information, promoting awareness, and also using appropriate reminder systems to preventive behavioral messages.
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---
title: 'The role of digitalization in business and management: a systematic literature
review'
authors:
- Esther Calderon-Monge
- Domingo Ribeiro-Soriano
journal: Review of Managerial Science
year: 2023
pmcid: PMC10043855
doi: 10.1007/s11846-023-00647-8
license: CC BY 4.0
---
# The role of digitalization in business and management: a systematic literature review
## Abstract
Digitalization is a powerful engine for economic growth in the world. In 2018, digitally transformed firms represented 13.5 billion US dollars of global GDP and, towards the end of 2023, they are expected to represent 53.3 billion US dollars, over half of the general nominal GDP (Statista, Nominal GDP driven by digitally transformed and other enterprises worldwide 2018–2023. https://www.statista.com/statistics/1134766/nominal-gdp-driven-by-digitally-transformed-enterprises/, 2022). The main objective of this study is to provide information (highlighting principal research topics and research agendas) from the literature on state-of-the-art digitalization within firms through a Systematic Literature Review (SLR). In all, 119 review articles on the most mature functional areas of the firm are analyzed: management, marketing, and finance and accounting, published in the WOS over the period 2018-April 2022. In this study, key relevant tendencies are identified in the most mature areas of the firm, which are the impact of digital technologies on the analysis of consumer behavior; digitalization and green innovation within organizations; and blockchain technology applied to financial services. The main contributions of this work are as follows: [1] to provide the most complete and up-to-date review of digitalization from a global perspective, summarizing the current state of knowledge within an integrated framework; [2] to reduce the complexity of digitalization by offering structure and clarity; and [3] to offer links between digitalization and established points of view in the literature on management, marketing, finance, and accounting. The novelty of this paper is centered on a joint analysis of digitalization, digital transformation, and digital technologies, taking into account the most mature functional areas of the firm.
## Introduction
Digitalization and the innovation that it drives are changing organizations, institutions, and society in general (Kraus et al. 2021). Centering attention on organizations, digitalization is provoking disruptive changes within firms and their immediate business environment, accelerating the obsolescence of the current business model (Wirtz et al. 2022). Digital technologies play a central role in the creation and the reinforcement of disruptions that take place in society and the levels of industry (Aström et al. 2022). Faced with these disruptions, organizations design strategic responses and they use digital technologies to alter pathways to value creation in which they had previously trusted for continued competitiveness. To do so, they should implement structural changes and overcome the obstacles to Digital Transformation (DT) that hinder their efforts (Hess et al. 2016). These disruptions trigger strategic responses among the organizations that occupy a central place in the literature on DT.
Digital technologies also alter consumer expectations and behavior in disruptive ways (Vial 2019). When employing these technologies, the consumer assumes the role of an active participant in the dialogue between an organization and its stakeholders (Yeow et al. 2017). Consequently, the clients no longer see themselves as so externally dependent on a firm with which they can negotiate (Sia et al. 2016) and their expectations multiply with regard to the services that the firm could offer to them. The implementation of Information Technology (IT) and organizational transformation (Gray et al. 2013) shifts the attention of the firm and its supply chain and directs it towards customers with digital connections.
Digital technologies can also be disruptive when altering the competitive landscape. The use of platforms (i.e., P2P in finance) have redefined existing markets giving rise to the sharing economy (Richter et al. 2017) and facilitating the exchange of digital goods and services. Competition is no longer only physical and is converted into a virtual domain where information flows are quicker and less restrictive than in the physical world and the former barriers to entry become less meaningful. This behavior can be seen in the music industry where physical goods have been replaced by subscription services for music offered by firms that had never before formed part of the industry. Companies can therefore use digital technologies both to launch new processes and to improve internal processes with their supply chains and their environment, as much as to develop new business models (Bouncken et al. 2021).
Given the growing importance of digitalization and its effects on firms, the main objective of this study is to improve our understanding of digitalization in the most mature areas of the firm, in order to identify gaps and future lines of investigation. To do so, we performed a Systematic Literature Review (SLR) for two reasons: [1] to use an adapted protocol to guide the data curation and analysis process, and to ensure objectivity (Kraus et al. 2022); and [2] a high-quality review paper has to follow an established methodology for the systematic selection and analysis of papers, and should periodically cover different fields to identify the latest developments (Snyder 2019). As with the recent SLR of Chaudhary et al. [ 2021], and others listed in the bibliography of this paper, this study encompasses three distinct stages: specifying the objective of the research; outlining the research protocol; and, finally, reporting the findings. Having specified the main objective of this study, the following Research Questions (RQ) are set out below:RQ1: How has the literature on digitalization developed up until 2022 in relation to different areas of the firm: management, marketing, and finance and accounting?RQ2: What are the principal research topics in relation to digitalization within different areas of the firm: management, marketing, and finance and accounting?RQ3: *What is* the research agenda in relation to digitalization within different areas of the firm: management, marketing, and finance and accounting?
These research questions are addressed through the SLR approach of Tranfield, Denver, and Smart [2003] to identify and to consolidate existing knowledge for its critical analysis and to generate ideas (Ribeiro-Navarrete et al. 2021). It is an established approach for the generation of robust, reliable, and replicable findings (Chaudhary et al. 2021). In addition, it is well suited to our objective, because its purpose is to consolidate the development of knowledge by identifying gaps for future research in mature areas (Vrontis et al. 2020; Chaudhary et al. 2021). We therefore chose management, marketing, and finance and accounting as mature areas of the firm where digitalization should be reviewed.
After applying the aforementioned research protocol that will be explained in Sub-Sect. 3.1, we will respond to the RQs. In response to RQ1, we generated descriptive statistics from peer-reviewed research articles selected by outlining the research context. We approached RQ2 by employing content analysis to outline key themes that emerged from the articles that were reviewed. Themes that researchers conducting SLR have recently often identified through the use of content analysis (Skare et al. 2022), to understand the intellectual structure of the field (Chaudhary et al. 2021). Finally, we responded to RQ3 by discovering gaps and avenues for future research.
The contribution of this paper to the literature is through a systematic review of the findings of 119 review articles published in academic journals on digitalization within each of the aforementioned areas. In particular, the theoretical contributions of this work are as follows: [1] to provide the most complete and updated review of digitalization from a global perspective, summarizing the current state of knowledge within an integrated framework; [2] to reduce the complexity of digitalization by offering structure and clarity; and [3] to offer links between digitalization and established points of view in the literature on management, marketing, finance and accounting. In addition, this literature review will help scholars to develop novel empirical studies of interest in subsequent investigations (Post et al. 2020), and to propose new routes and opportunities within this field.
## Theoretical framework
Digital tools such as Social, Mobile, Analytics, and Cloud (SMAC) technologies are driving digitalization (Teubner and Stockhinger 2020), and offering opportunities to change the way in which firms work (Aström et al. 2022). In accordance with Chan et al. [ 2022], social networks give market visibility to a firm and establish links with their actors; mobile networks also connect different actors within the business eco-system, and offer learning and continuous access to information at any time and place. The cloud brings accessibility, storage, and relevant information exchange, work-flow monitoring, and remote collaboration. Finally, analytics facilitates understanding of business and client needs together with the identification of opportunities and market trends, and the recommendation and provision of services and personalized communications. Digital technologies are therefore easily available to the firm and can improve its effectiveness in profitable ways (Chan et al. 2022), provided their introduction is accompanied by innovative business models or transformations of the traditional model (Aström et al. 2021). Nevertheless, investigation must continue on the impact of new technologies for the decision-making process of the firm (Troise et al. 2022) and consumer privacy (Quach et al. 2022).
Although SMAC technologies drive digitalization, it is not merely a technical advance, but it is also an economic and a social one too (Legner et al. 2017). Digitalization refers to the interaction between digital technologies and both the social the institutional processes converting these technologies into infrastructural technologies and impacting on society and the economy (Teubner and Stockhinger 2020), thereby advancing communication, mobility, speed, virtualization, the disappearance of frontiers, interconnections, market transparency, and competition. However, the technical process of coding analogic information in a digital format, which means that the digitalized content is programmable, traceable, and communicable is known as digitization (Yoo et al. 2010a, b). It is a technical phenomenon and must not be confused with digitalization, because it includes fewer integral changes. Nevertheless, digitalization is found at some point between digitization and DT. DT implies immense organizational changes driven by digital technologies and, in consequence, profound alterations in business strategies and routines (Alzamora et al. 2021). However, digitalization is associated with important changes within sociotechnical structures (Yoo et al. 2010a, b), which are reconfigured through questioning the assumptions underlying the design and the use of digital technologies (Thorseng and Grisot 2017). Therefore, DT is naturally connected to the topic of organizational change, viewed as a ‘difference in form, quality, or state over time in an organizational entity’ (Van de Ven and Poole 1995, p. 512). For greater knowledge of DT in the field of management, see Hanelt et al. [ 2021].
When digital technologies are installed in an organization, they interact with organizational and managerial characteristics, specifically with the strategy and the legacy of an organization, as well as with the resources, the processes, the values, and the culture of an organization (Dewan et al. 2003), without forgetting positive attitudes towards change and technology (Dery et al. 2017). All these organizational antecedents are integrated and interact with environmental antecedents and the characteristics of the country, the industry, and the consumers. These characteristics include the legal conditions and the infrastructure of a country, as well as its regulatory frameworks and interventions (Cortet et al. 2016), and the dynamics of an industry driven by technology, which includes the changing technological panorama of a country (Alos-Simo et al. 2017). The above in no way ensures the success of many digital transformations within firms. In a recent study, Witschel et al. [ 2022] demonstrated that the innovation of the business model is an effective way of continuing to be competitive in the digital era. To do so, these authors defended the role that dynamic capabilities play in the innovation of the business model, as well as contextual factors, leadership and business mentalities. Moreover, Wen et al. [ 2022] affirmed that manufacturing firms with greater viability are more adaptable to DT and tend to implement differentiated competitive strategies, for which reason they concluded that the effect of incentivizing innovation is greater for firms of higher viability. The antecedents of DT (digital orientation, digital intensity, and digital maturity) were also analyzed to understand their influence on the financial success of firms (Nasisri et al. 2022). These authors maintained that digital orientation and digital intensity in themselves contributed nothing to the financial success of firms; and digital maturity acted as a mediator between digital orientation and the financial success of firms, and between digital intensity and the financial success of firms. DT, therefore, presented important opportunities for firms, but also for entrepreneurs. In the case of digital entrepreneurship, Chatterjee et al. [ 2022] provided evidence that perceived utility, perceived ease of use, and willingness to introduce strong and significant changes all affected digital entrepreneurship.
In addition, a DT is shaped by the characteristics of the consumer, in particular, the demand of the digital consumer. Consumers place increasing trust in digital technologies throughout their daily lives and personal interactions (Brynjolfsson et al. 2013) and expect ubiquitous access to virtual resources (Benlian et al. 2018). Both the appearance of digital technologies and their diffusion have led to greater data availability (on these aspects, see Vial 2019, p.123), which has in turn increased the importance of automated learning and the analysis of data for organizations (Weichert 2017). These data enable firms to offer services that better respond to the needs of their clients and to complete processes in more efficient ways for their own competitive advantage. Thus, some firms use social networks such as Twitter and Facebook to institute customer care operations and then use the data that are generated from these interactions to appeal to client sentiment in real time. Finally, the digital industrial transformation, known as the fourth industrial revolution or Industry 4.0 implies a change of paradigm from a hyperconnected and centralized manufacturing ecosystem to a decentralized one (Li et al. 2019). Under Industry 4.0, intelligent physical objects, decentralized subsystems, and even human components are perfectly integrated within an interoperable, hyperconnected, and decentralized production system (see Hodapp and Hanelt 2022) that is capable of adapting in real time and in an autonomous manner to environmental change (Ardito et al. 2019; Sanchez et al. 2020). Khan and Javaid [2022] considered that the IoT was a critical component of Industry 4.0, which improved product manufacturing efficiency, because it was done with fewer errors and costs. Somohano-Rodiríguez et al. [ 2022], analyzing the role that enabling digital Industry 4.0 (I4.0) technologies played in SME innovations, found that strategic planning advanced I4.0 and that enabling Information and Communication digital technologies promoted innovation more intensely than enabling digital technologies for integration and advanced robotics. Bhatia and Kumar [2022] analyzed the critical success factors of I4.0, highlighting “data governance” as the most critical factor.
The scope of the DT under Industry 4.0 expands beyond individual firms, involving the vertical integration of production systems and the horizontal integration of partners in a value chain (Tiwari 2020). Thanks to unprecedented commercial exploitation of social networks, Industry 4.0 also implies the integration of the client (Ghobakhloo 2020). Specifically, the findings of Kurniawan et al. [ 2022] made it clear that DT in the waste sector, not only promoted the recovery of non-biodegradable waste resources for a circular economy, but it also meant that the local community could perform online transactions of recycled goods through mobile apps. The industrial reports reveal that digitalization under Industry 4.0 offers numerous economic benefits for manufacturing, product quality, savings on operating costs, and performance in general (Dalenogare et al. 2018). Finally, if the adoption of I4.0 is a challenge for firms, the consideration of Industry 4.0 in the industry of sustainability is much more difficult (Verma et al. 2022).
## Research methodology
Bearing in mind the earlier definition of digitalization, DT, Industry 4.0, and digital technologies, this SLR of digitalization and its effects on the firm is focused on three large mature areas: management, marketing, and finance, and accounting. The main reason for this study with its SLR methodology is because it has been verified that past reviews have employed various methodologies on different digital technologies, aspects of DT, digitalization, and Industry 4.0. However, no SLR by firm area has been found that presents a picture of existing research, research gaps, and future lines of research on digitization. Therefore, the novelty of this study is [1] in going beyond another review of some aspect of digital technologies, digitization or DT; and [2] in preparing an over-arching SLR of previously published review articles -rather than empirical articles- that integrates the above-mentioned mature areas of the firm.
Its methodological approach is explained and defined in what follows. Having proposed the main goal and the RQs in the introduction, first, the identification of the data set is defined. The Web of Science database (WoS) was chosen for the literature search, because it was considered to provide both adequate and comprehensive collections of relevant academic literature (Raff et al. 2020). Second, it was decided to use PRISMA as a systematic review protocol describing the rationale, hypothesis, and planning methods of the review (Shamseer et al. 2015). The application of the PRISMA protocol has helped to create a dataset of papers. Third, content-based research was conducted to analyze the content of the papers, and to identify the main bibliographic information in each paper and the relationship with different management, marketing, finance and accounting topics. Data processing helps to identify two kinds of findings. First, the descriptive statistics that link digitalization and different topics related to management, marketing, and finance and accounting (publications by years and citations, contribution by journals, country analysis, and so on). Second, the research trends and thematic area are defined through a co-concurrence analysis of the keywords using VOSviewer software.
Co-occurrence analysis of the keywords is a technique for analyzing the primary content of selected publications (Guo et al. 2019) with which essential words may be viewed in a search field along with co-occurrences (Secinaro et al. 2022). According to Skare et al. [ 2022], the co-occurrence analysis of the keywords also involved an analysis of the article abstracts to identify clusters of terms. A cluster is a non-overlapping set of interlinked terms. The co-occurrence of terms in the abstracts formed various clusters. The strength of the links between terms based on co-occurrence frequency was rated.
Vosviewer software can be used to construct relations and to visualize literature based on information in the academic literature. It can display the development and the emerging publication trends of a discipline over time. VOSviewer5 software identifies predominant terms and the co-occurrence across all articles during the analysis (Biggi and Giuliani 2020). The output of the process is the clustering of terms into dominant themes.
A detailed description of the research analysis and results is provided in the following sections.
## The PRISMA protocol and the systematic literature review (SLR) approach
A Systematic Literature Review (SLR) was conducted to understand the relevant trend of studies on digitalization, adopting a consolidated approach: the PRISMA protocol (Moher et al. 2015; Nyagadza 2022). An SLR was performed to identify and to select research studies related to a specific research question for their evaluation and summary, in a fair, rigorous, and transparent manner (Chaudhary et al. 2021), so as to identify potential avenues for further research, and to highlight the boundaries of existing knowledge (Tranfield et al. 2003). Furthermore, SLR is suitable for our study, because previous research is summarized in that sort of study in a novel perspective (Chaudhary et al. 2021). Systematic reviews, as the name suggests, typically involve detailed and comprehensive planning and a search strategy designed a priori, so as to reduce bias (Chaudhary et al. 2021) by identifying, appraising, and summarizing all relevant studies on a particular topic (Uman 2011).
The PRISMA protocol provides a flow diagram that helps with the steps of the SLR process: identification, screening, eligibility, and inclusion. The first purpose of this method is to plan, to identify, and to evaluate studies to extract, and to summarize data from the literature (Tranfield et al. 2003), ensuring that the bibliographic research is at once objective, transparent, and replicable. The reason for choosing this (PRISMA) protocol between other standards and guidelines (Snyder 2019) is that its analytical methodological approach is very clear and easy to understand (Moher et al. 2015). The protocol is shown below in Fig. 1.
Fig. 1PRISMA protocol
## Identification
The initial search on different databases showed that most relevant literature was listed in the previously mentioned WoS. In addition, the WoS Core Collection was chosen to remove the ‘noise’ that could lower the accuracy of the results. No references published in other databases such as Conference Proceedings Citation Index-Science, Emerging Sources Citation Index, and Book Citation Index-Science were considered in this study. WoS Core™ is an internationally recognized database that reflects levels of activity in scientific research and has been widely used in many studies (Thompson and Walker 2015; Yu and Liao 2016; Emmer 2018; Muhuri et al. 2019). In addition, Journal Citation Reports (JCR) is one of the most widely used publications for the evaluation of journal impact using a database of citations. Complete and neutral data are searched for in the database, which can provide a reasonable foundation for data analysis. Besides, the SSCI databases are included in WoS, making the analytic results more accurate and comprehensive (Zhou et al. 2019). The search was conducted in the WoS database on 10, 11, and 13 May, 2022.
As the purpose was to conduct a comprehensive search, in order to establish the current body of literature and knowledge in digitalization in different areas of business, management, and economics, several keywords were included in the search: digital*, “accounting”, “auditing”, “banking”, “insurance”, “taxation”, “financial structure and capital”, “marketing”, “business strategy”, “corporate governance”, “entrepreneurship”, “human resources management”, “innovation management”, “risk management”, “organization”, “production”, and “logistics”. In addition, “information systems” were included, because there might be publications within the area of business and management; and “health economics”, given its relation with the economy.
The selection of keywords and precise search strings are important and difficult tasks during the analysis of the literature (Kraus et al. 2020). The articles are identified based on the combination of keywords that could help researchers focus on the targeted literature, which are Digital* and all those topics related with Business and Management: digital* AND accounting [3,331], digital* AND auditing [332], digital* AND banking [886], digital* AND insurance [343], digital* AND taxation [71]; digital* AND “financial structure and capital”[1]; digital* AND marketing [5,901]; digital* AND “business strategy” [193]; digital* AND “corporate governance” [73], digital* AND entrepreneurship [1,232]; digital* AND “human resources management” [19]; digital* AND “innovation management” [84]; digital* AND “risk management” [224]; digital* AND organization [5,697]; digital* AND production [3,535]; digital* AND logistic [1,100], digital* AND “information systems” [2,771] and digital* AND “health economics” [83]. These combinations of keywords were entered in the topic section of the WoS database. In each case, the number of articles appears between parentheses.
## Screening
The following step was to revise whether each publication found after the previous step was accessible, leading to the removal of 5 articles. Then, a reading of the abstracts of the articles found under the categories “Health Economics” and Information Systems”, led to the removal of a further 83 articles.
## Eligibility criteria
Before conducting the search, a set of inclusion and exclusion criteria were decided upon. In an preliminary phase, the inclusion criteria were publication in peer-reviewed journals, type of article, review articles and early access [25,023], documents included in the WoS were: Business, Management, Business Finance, Economics, Operations Research Management Science and Computer Science Information Systems [9,867], English language [9,316], literature from 2018-April 2022 [5,925], because an analysis of the publications from 1956-April 2022 indicated that most of them had been published between 2018 and 2021. In each case, the number of articles appears between parentheses.
Regarding the exclusion criteria, all articles that were not review articles [5,629] were removed, because the review articles were seen to predominate over the period under study. Finally, 83 duplicated articles were removed, leaving a total de 213 articles.
## Inclusion
The next step was quality assessment including scanning the abstract and selection of papers, leaving a final sample of 119 articles (see Appendix 1). In all, 94 articles were removed for different reasons: empirical papers with a literature review, review articles that were not an SLR, and articles not directly addressing topics related to management, business, and economics.
The articles under the different categories of the WoS classification, in the areas of management, marketing, finance and accounting were grouped together to achieve the tasks described above, as may be observed in Table 1, so as to have as many articles as possible to analyze. Table 1Number of review articles under different categories of the WoSCategoriesN. review articlesAreasN. review articlesAccounting3Accounting7Auditing4Insurance2Finance10Banking5Risk management3Marketing26Marketing26Corporate governance2Management76Human resource management3Business strategy5Innovation1Logistics11Production8Entrepreneurship17Organization29 Total 119 Total 119 The specific questions listed in Table 2 were answered to perform the analysis of the articles. Table 2Research questions and content analysisQuestionsSpecific questionsContent analysisRQ1: How has the literature on digitalization developed up until 2022 in relation to different areas of the firm: management, marketing, and finance and accounting?RQ1.1. Which articles are crucial in the development of digitalization? Calculating publications and citations RQ1.2 What are the production levels and the impacts of authors, journals, and research areas? Calculating the ranks RQ2: What are the principal research topics in relation to digitalization within the different areas of the firm: management, marketing, and finance and accounting?Q2.1 What are the major research subjects concerning digitalization? *Cluster analysis* RQ3: *What is* the research agenda in relation to digitalization within the different areas of the firm: management, marketing, and finance and accounting?RQ3. What are the major research gaps concerning digitalization?
## How has the literature on digitalization developed up until 2022 in relation to different areas of the firm: marketing, management, and finance and accounting?
In this section, the main analyses of the corpus are presented, as described in Sect. 3, starting with the distribution of the papers on a yearly basis, the scientific journal of publication, and the country. These analyses are performed, in order to understand how the interest in digitalization related to business economics has been gaining importance over time.
In Fig. 2, a substantial increase of review articles may be observed as from 2020, whereas before that year, the number of publications was less frequent. This item of information was the principal reason for this SLR on review articles and not on articles. An analysis of the periods of study of the review articles supports the earlier results, because $27.87\%$ of the review articles under analysis were undertaken within a time frame up until 2020, $21.31\%$ within a time frame up until 2019, and $15.57\%$ of the SLR, up until 2021.Fig. 2Number of articles by year (2018–2022) Although the corpus included articles from 76 different journals, $50.82\%$ of the papers in the corpus were published in 50 journals and another $49.18\%$ were published in 26 journals, reflecting high fragmentation within the literature. Figure 3 reports the journals that have published at least two papers on the topics of our study. The fragmentation observed in the literature is owing to 31 journals that are closely linked to the disciplines of accounting and finance, management, business and marketing, while the others are related with transversal and multi-disciplinary issues. Thus, some papers of the corpus have been published in journals that are focused more on operations (i.e., Annals of Operations Research), transportation (i.e., Research on Transportation Economics), and sectors (Engineering construction and Architectural Management).Fig. 3Number of articles by publication (2018–2022) In Fig. 4, it may be seen that Europe leads the number of published review articles with almost $50\%$ of the publications shared between England, Italy, Germany, and France. The Australian continent follows on with $14.75\%$ between Australia and New Zealand, and finally North America with $13.12\%$ between the USA and Canada. $8.20\%$ of the corpus under analysis was published in India. Fig. 4Number of articles by country (2018–2022) The references from each of the 119 articles under consideration in this study were analyzed to respond to questions RQ1.1 and RQ1.2. In addition, the references of each of the 119 articles with a number of citations ≥ 100 were selected, as the purpose was to highlight those articles that have been cited most of all among the articles under analysis and the most influential articles. A total of 764 references with 100 or more citations were extracted from the 26 articles on marketing, of which only $8.25\%$ had been cited two or more times in the articles under study.
In Fig. 5, the most frequently cited marketing publications may be seen, highlighting the article of Lemon, K. and Verhoerf, P., titled Understanding customer experience throughout the customer journey published in 2016 in the Journal of Marketing. However, the most influential article with a total of 3,670 citations (367 citations per year) was published in 2012 by the authors Venkatesh, V., Tong, J and Xu, X in MIS Quarterly titled Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. In Table 3, other articles may be seen that were also influential in the field of marketing and digitalization. Fig. 5Most frequently cited authors of review article published in the field of marketing and digitalization It may be seen that Peter *Verhoef is* one of the most cited authors within the field of digitalization and marketing. He has three articles among the most frequently cited in the articles under analysis each of which has a high number of citations forming a network with Katherine Lemmon. Table 3Top 10 Marketing references with the highest average number of citations per yearYearReferencesCitations among review articlesCitationsAverage citations per year2012Venkatesh et al.23,670367.002003Tranfield et al.54,518237.792008Vargo and Lusch33,317236.932016Lemon and Verhoef61,251208.502002Webster and Watson22,733136.652020Davenport et al.3249124.502015Verhoef et al.3865123.572020Paul and Criado3247123.502015Ostrom et al.2740105.712009Verhoef et al.31,19091.54 In Fig. 6, the most productive source in marketing may be seen in the field of digitalization. All journals are specialized in marketing except the Journal of Business Research that is more generalist. Fig. 6Top 10 most productive sources Of the 79 articles on management, 2,606 references with 100 or more citations were found, of which $18\%$ were cited at least twice in the articles under analysis.
In Fig. 7, the most frequently cited articles with at least 5 citations from the corpus may be seen. The article that stands out with $9\%$ of all citations is [11] “The New Organizing Logic of Digital Innovation: an Agenda for Information Systems Research” by the authors Yoo, Y. J. Henfridsson, O. and Lyytinen, K., written in 2010, and published in Information Systems Research. These authors stand out for having created a research network on organization and digitalization. Fig. 7Most frequently cited authors of review articles published in the field of management and digitalization In Table 4, the most influential articles in the field of digitalization and management can be seen. With almost 754 citations, Barney, J. [1991] is the most influential author with his article Firm resources and sustained competitive advantage published in the Journal of Management. Nevertheless, it is also worth mentioning the article titled Literature review of industry 4.0 and related technologies written by E. Oztemet, and S. Gursev [2020] in the Journal of Intelligent Manufacturing with 218 citations by year. Table 4Top 10 management-related references with the highest average number of citations per yearYearReferencesCitations among review articlesCitationsAverage citations per year1991Barney723,365753.711997Teece et al.913,822552.882007Teece75,022334.802000Sanhe and Venkataraman55,772262.322020Oztemet and Gursev5436218.002014Lasi et al.71,599199.862017Zhong et al.6855171.002018Jacobides et al.5629157.252014Porter and Heppelmann81,004125.502017Hofmann and Rusch5625125.00 In Fig. 8, the most productive source of management in the field of digitalization can be seen. Most journals are within the field of management. The Strategic Management Journal stands out amongst the others as the journal that has published most of all on digitalization and management. Fig. 8Top 10 most productive sources Finally, of the 17 articles on finance and accounting, 100 of the 543 references were cited, of which $17.31\%$ were cited at least twice in the articles analyzed in this area.
In Fig. 9, the most widely cited publications can be seen in digitalization, and finance and accounting, highlighting the work of the Massaro, M., Dumay, J., and Guthrie, J. [2016] titled “On The Shoulders of Giants: *Undertaking a* Structured Literature Review in Accounting” in Accounting, Auditing & Accountability Journal. Fig. 9Most frequently cited authors of review articles published in the field of finance-accounting and digitalization Among the most widely cited references, the most influential is one from the authors Gossling, S., Scott, D., and Hall, C.M. [2021] titled Pandemics, Tourism and Global Change: a Rapid Assessment of Covid-19, published in the Journal of Sustainable Tourism with a total of 1,187 citations (1,187 in one year). This article is within risk management in the area of finance. In Table 5, it may be seen that there are other references that even though less cited among the corpus of finance and accounting under analysis have a larger number of citations. No cooperative network with a higher number of citations has been observed. No cooperative research network was observed among the authors under analysis. Table 5Top 10 finance-accounting and digitalization-related references with the highest average number of citations per yearYearReferencesCitations among review articlesCitationsAverage citations per year2021Gossling et al.21,1871,1872021Wen et al.22412412014Mollick21,546193.252020Hall et al.2345172.52021Neuburger and Egger21441442021Kaushal and Srivastava21411412021Zheng et al.21391392020Higgins and Desbiolles22741372020Zenker and Kock22601302020Jiang and Wen2234117 In Fig. 10, the most productive sources in finance and accounting in the field of digitalization can be observed. Most of the journals are not within the field of finance and accounting. The Journal of Finance stands out as the specialized journal in which most papers on digitalization and finance and accounting have been published. Fig. 10Top 10 most productive sources
## Principal topics and analysis of research trends within marketing, management, and finance and accounting
A co-occurrence analysis was completed to respond to RQ2 and to establish the topics of interest of both the set of articles on digitalization and each functional area of the firm: accounting and finances, marketing, and management. The analysis was performed in two ways, to obtain results of greater reliability: based on keywords and based on titles and abstracts. Both gave the same results for the co-occurrence analysis in the 3 areas under analysis. The analysis based on keywords, titles, and abstracts is therefore described below.
First, the groups formed on the basis of titles, abstracts, and keywords were sought in the set of articles for their identification (VanEck and Waltman 2010). VOSviewer 1.6.15 (VanEck and Waltman 2010) was employed for this analysis. A minimum number of five occurrences to consider a term is suggested for this tool. However, in this analysis, the number of occurrences was varied as a function of the number of articles that were analyzed. *The* generic terms such as “article”, “review”, “systematic review”, “systematic literature review” were removed during the data cleaning process, as it was a review of a sample of articles. In addition, similar terms were grouped under one single term (van Eck and Wallman 2010, 2020), such as “Covid-19”, “Covid” and “pandemic”, or B2B and “business-to-business”. In the following figures, the clusters obtained for each area are shown. The nodes represent keywords or concepts, while their size reflects their frequency (van Eck and Waltman 2010, 2020). VOSviewer represented each group of keywords with a different color. This analysis was repeated within each and every area.
## Marketing
In the area of marketing, we set a minimum of 2 occurrences for the analysis of concurrence, for a term to be considered in the programme, because we had a relatively low number of [26] publications. The most frequency cited topics gave us an idea of the structure of the topics that have been investigated most of all and that have contributed to the development of digitalization in marketing. In Fig. 11, the 5 clusters obtained from the previously detailed co-occurrence analysis are shown. Cluster 1 has “performance” as a highlighted node and groups other keywords such as “digital marketing”, “co-creation”, “customer engagement”, “adoption”, “customers”. Thus, the cluster is related with digital marketing performance and is centered on adoption and engagement from the perspective of the consumer. Cluster 2 has “social media” as a highlighted node and groups together other keywords such as “management”, “digital technologies”, “travel”, purchase intention”, “acceptance”, “Word-of-Mouth” (WOM), and “strategy”. This cluster is related with the acceptance and management of social media as a marketing strategy, directed at WOM and purchase intention of the consumer. The most frequently cited and therefore the most investigated topics were WOM, purchase intention, and digital technologies. Cluster 3 has “knowledge” as a highlighted node and groups other keywords such as “big data”, “consumer behavior”, “Covid 19”, “business”, and “decision-making”. This cluster was related with knowledge of consumer behavior through big-data technology, above all during the Covid-19 pandemic, to facilitate decision-taking within firms. Cluster 4 has “impact” as the highlighted node and groups other keywords such as “relationship management”, “engagement”, “campaign”, and “digital technologies”. This cluster is related with the impact of mass-media campaigns through digital technologies measured through engagement and managerial relations. Most present-day studies have examined the acquisition of a mobile device in the context of publicity and the marketing of services.
Finally, Cluster 5 has “customer journey” as a highlighted node and it groups together other key words such as “customer experience”, “online”, “innovation”, and “dominant logic”. This cluster is related with the customer journey via online outlets for the purchase of a product and the product experience.
In short, social networks have been turned into the most influential channel of digital marketing, creating an imminent need for the specialists in digital marketing to exploit transformative marketing even more so. Compiling data from social networks for the analysis of consumer opinion (Micu et al. 2018) has created additional opportunities to explore programs created on social media platforms to compile, to analyze, and to understand consumer data. Fig. 11The result of the co-occurrence analysis in the field of marketing and digitalization The progress of marketing throughout this period where digitalization has dominated can be summarized in the following way. When consumers identified search engines as a means of searching for information, studies on marketing through search engines emerged (Dou et al. 2001), giving rise to a new area of investigation: digital marketing. In turn, the studies within this area accelerated the growth of the “e-WOM” or electronic Word-of-Mouth, between 2000 and 2004. Two articles by Dellarocas [2003], and Godes and Mayzlin [2004] may be highlighted. The latter author discovered the interaction between offline and online environments, giving rise to three concepts that were to be the source of research over the coming decade: Zero-Moment-Of-Truth (ZMOT) (Lecinski 2011), webrooming (Flavian et al. 2016), and single-channel retailing (Blom et al. 2017).
In the retail trade, as the level of consumer engagement with online retailing formats has progressed, the focal interest of research has shifted towards the online experiences (Novak et al. 2000) and their value and costs. The quality of the virtual experience with electronic services has led researchers to investigate the concept of the quality of the electronic service, analyzed from the point of view of the client (Yi and Gong 2008), the system (Bronner and Kuijlen 2007), and the retailer (Venkatesan et al. 2007). Consumer privacy has been turned into one of the central research topics, with the compilation, storage, and distribution of consumer data (Norberg et al. 2007). With the growth of single-channel retailing, studies on consumer responses to the delivery of value went beyond decision making. Retail commerce understood as an extension of brand communities, investigative attention has been centered on brand attachment, love and hate, and different emotional responses of consumers to marketing stimuli (Bleier et al. 2019; Petit et al. 2019).
The above-mentioned lines of investigation have led researchers to center their attention on the concept of value or the delivery of value to facilitate persuasion, and consumer satisfaction and loyalty. Analyzing the concept of value and its meaning for the consumer in the digital environment (Gallarza et al. 2011), taking a step forward, marketing research began to analyze another two value components: utilitarian and hedonic values (Childers et al. 2001).
Digital advertising emerges and more specifically, the effectiveness of publicity, measured through clickstream data that has become a new line of investigation (Tucker 2014). The rapid improvements to digital publicity gave rise to new techniques such as retargeting through online offers (Berger and Milkman 2012), and other topics such as user-generated content, online reviews (Floyd et al. 2014), online offers, and WOM marketing (King et al. 2014).
Up until 2014, topics such as multi-channels and mobiles remained emergent yet unexplored investigative areas (Lamberton and Stephen 2016). It was after the article of de Verhoef et al. [ 2015] when single-channel marketing captured growing interest in the investigation (with early examples such as Yoo and Lee 2011; Dinner et al. 2014;). With regard to mobile telephony, Atkinson [2013] produced the earliest study on the use of QR codes for purchases. As consumers gained greater familiarity with digital technologies and started to interact with digital channels in different ways, investigations began into the analysis of their emotional responses to marketing, and into consumer commitment towards digital channels and digital mediums. A new tool appeared for the marketing specialists: “User-Generated Content” (UGC) as an extension of eWOM that had been one of the previously analyzed topics. The works on UGC were gradually diversified towards online review (Bilro et al. 2019; Van Laer et al. 2019), eWOM (Chu and Kim 2018), and the content generated in online brand communities (Hollebeek et al. 2017).
In the field of consumers, the investigation has been centered on such topics as consumer participation in social networks and the co-creation of value. Particularly, collaborative consumption and social exchanges have been present since the first decade of the new Millennium, with the first investigations on renting loans and the exchange of different articles using digital platforms (Mohlmann 2015; Hwang and Griffiths 2017; Dellaert 2019). As digital forums are consolidated, digital products are also created such as social network accounts, games, music, videos, and avatars, giving rise to a new investigative topic: digital music and the psychological property of digital goods among consumers (Bardhi and Eckhardt 2017). This topic is an extension of the virtual experiences analyzed earlier.
## Management
We had 76 publications within the area of management to conduct the co-occurrence analysis. As it is a greater number of articles than in the case of marketing, the terms were required to appear in at least 4 publications. The 4 clusters that were obtained may be observed in Fig. 12:Cluster 1 has “management” as a highlighted node and groups together other keywords such as: “performance”, “information”, “knowledge” “business”, “organizations”, “communities”, and “Covid-19”. This cluster is related with the management and the performance of information to convert it into knowledge for organizations, businesses, and communities. Cluster 2 has “innovation” as a highlighted node and groups together other keywords such as: “digital transformation”, “dynamic capabilities”, “information technology”, “big data analytics”, information systems”, “value creation”, “firm performance”, “competitive advantage” and cyber-physics. This cluster is related with innovation in everything related with DT that affects dynamic capabilities, information technology, information systems, value creation, competitive advantage, and firm performance. Briefly, it could be said that it is related with malleable organizational designs (Hanelt et al. 2021), so that firms that are easily influenceable and flexible can continue adapting to their environments. Cluster 3 has “technologies” as the highlighted node and groups together other keywords such as “internet”, “supply-chain management”, “impact”, “logistics”, “entrepreneurship”, “business model”, and digitalization”. This cluster is related with Internet technologies and digitalization that impact on logistics, and on supply chain management, as well as those that set up business models and favor entrepreneurship. It could be said to be related with the ecosystems of digital business models (Hanelt et al. 2021).Cluster 4 has “Industry 4.0” as the highlighted node and groups together keywords such as “big data”, “challenges”, and “future”. This cluster is related with big data and its appearance in the firm, as a future challenge.
In short, the closeness between the nodes indicates that all the topics are interrelated, even though they belong to different clusters. Sustainability may be highlighted as the most distant topic. Fig. 12The result of the co-occurrence analysis of review articles in the field of management and digitalization *It is* difficult to describe the progress of research over time into digitalization in the field of management, because consideration must be given to each aspect of the organization of a firm: supply chain management, logistics, manufacturing, human resources management, knowledge management, business management, entrepreneurship. We have therefore centered on the more general lines of research.
The firm has to have the capability to detect and to take advantage of interruptions, through strategic responses, and to reorganize its business model components, when faced with DT. Therefore, dynamic capabilities are a topic of investigation, which has been centered on: [1] adapting these capabilities to support DT; [2] integrating the capabilities into the reality of digital platforms and ecosystems; and [3] sustaining them through a micro-analysis from the perspective of the Chief Digital Officer.
One pattern that is observed in the DT are the malleable organizational designs based on digital technologies and agile structures for rapid adaptation to environmental opportunities and threats. Data-based operations and decision making are upheld by automated virtual commercial processes, as well as technology-centered management. Finally, the results of the digital business models centered on client and digital experience, and intelligent connected and personalized products can be related with the mechanism of instantaneous launches, because it is a matter of dynamically adaptable and scalable market offers.
In the context of a DT, it is easy for firms to become aware of the importance of malleable organizational design. Both access to large volumes of data and the technologies for their analysis, for detecting changes within the business environment and the capability to adapt activities that are based on flexible digital technologies prompt continuous changes on the basis of environmental feedback, and emerging opportunities. At the same time, however, it is more difficult for the firm to distinguish the source of the change and if its development is within or beyond its limits, because it may be provoked by external agents such as clients and developers (Parker et al. 2017). In addition, a large part of the technological configuration might be beyond the control of a given firm and might be developed in an unpredictable way, under the will of external actors, such as technological giants and new digital firms. Operations and decisions based on data adjust themselves to the commentaries within a business setting, for example from the clients, when using the potential of Artificial Intelligence (AI) and automatic learning to detect changes and to react automatically to them. Rapid transformation requires firms to modify their stance and capabilities for agreement with the new opportunities that dynamically emerge and die down. Therefore, in the course of the DT, continual adaptation assisted by malleable organizational designs contribute to a wholistic confluence of the turbulence within business settings, information technological systems, and organizational capabilities. A situation known as “digital ecodynamics”, which “has no separations between its three central elements, as it is rather the fusion of all the interactions between all three elements” (El Sawy et al. 2010, p. 837).
The appearance of digital business ecosystems is another topic of DT within the firm. These ecosystems define business environments “formed by a network of interdependencies specifically generated through digital technologies” (Kopalle et al. 2020, pp. 114–15). As some shape is given to the decisions of the firm on where and how to compete, business ecosystems are an essential topic for the strategy (Jacobides et al. 2018). Over past decades, numerous firms have distanced themselves from integrated and hierarchized supply chains and are moving closer to more fragmented networks of strategic associations with external entities (Bitran et al. 2007). However, over the course of a DT, the digital business ecosystems are now not only interesting for information technology and software industries, but they are also increasingly relevant in all sectors as digital technologies spread throughout industry and society.
A key differentiating factor that distinguishes digital business ecosystems is their turbulent nature (El Sawy and Perreira 2013). In the context of entrepreneurial ecosystems, this turbulence is especially visible in the great quantity and heterogeneity of interdependent partners who give shape to the competition (Jacobides et al. 2018), the generalized diffusion and the adoption of generative, interconnected technologies (Kopalle et al. 2020), and the preferences of clients that undergo constant change (Downes and Nunes 2013). The research emphasizes the relevance of these factors and, therefore, a general movement towards digital business ecosystems (Bouncken and Kraus 2022). For example, it is evident that DT is activated and molded by a large quantity of different digital technologies and applications such as blockchain technologies, IA, and IoTs. In addition, consumers increasingly incorporate technology into their daily routines and personal interactions, which has profound implications for their demands and expectations with respect to the offers and the communications with firms.
Firms must now compete with a larger number of competitors, numerous industries and, in some cases, with completely different commercial models. A change may be appreciated towards interconnected markets in which the participants are involved in numerous networks of exchange that grow and dissolve very easily. This sort of change can mainly be attributed to the ubiquity of Internet and other related technologies upon which these networks in large measure depend, which in general leads to digitally permeated markets. In addition, the convergence of previously separated industries may be observed as heterogenous actors from different industries increasingly operate and compete within the same markets, due to the possibilities of digital technologies, which amass the experiences of previously disconnected users (i.e., Broad-band Internet, telephony, and television) (Yoo et al. 2012).
Due to the growing turbulence, digital business ecosystems are “changing the rules of the game in many industries through the disruption of business models” (Pagani 2013 p. 617). However, among the digital business ecosystems, the value proposals that are followed can radically change within a short period of time (Yoo et al. 2012). The participants within the digital business ecosystems, their positions, and their roles are subject to constant changes. In addition, firms co-create and co-capture value with a great variety of heterogenous actors who go beyond suppliers and traditional clients and range from individuals to communities of emergent firms, technological giants, and social actors (Rubio-Andrés et al. 2022; El Sawy and Perreira 2013). A completely new level of interconnections and interdependency arises and undergoes constant growth, due to the low entry barriers for practically everybody.
In summary, the change towards a flexible organizational design is integrated and is driven by the digital business ecosystems and cannot be considered in isolation (Hanelt et al. 2021). This relation is affected by the specific aspects and changes related with the conditions of the business environment. The scope of the environment can vary throughout one continuum, from a “narrow” scope, which is generally focused on specific elements related to the digital business ecosystems and their relation with organizational design, up to a “broad” scope examining thematic patterns of the DT in a more holistic way.
## Finance and accounting
The areas of Finance and Accounting were linked, because the number of articles in each one of them was excessively small to conduct the analysis. In this way, there were 17 articles to complete the co-occurrence analysis. The minimum chosen number of occurrences was 2. Two clusters appear in Fig. 13.
“Fintech” and “Blockchain” were the most highlighted nodes within Cluster 1 and grouped together such words as “financial technologies”, “innovation”, “information”, “financial services”, and banking”. This cluster was related with technologies such as blockchain applied to financial services (Fintech) and to banking. It may be said that mainstream commercial banks accept the impact of technology, the way in which financial services are changing, and the way new business models are gaining ground in the financial industry. A large number of “Fintech” firms, “Fintech” industries, and “Fintech” markets have emerged.
The most highlighted node in Cluster 2, “accounting”, grouped together such keywords as “impact”, “cryptocurrency” “digitalization”, “risk management”. This cluster is related with the digitalization of accounting, the impact of cryptocurrency, and risk management. Cryptocurrency is within a different cluster to the one in which blockchain belongs, but it is close enough, because cryptocurrency cannot be analyzed without mentioning the technological foundation of cryptocurrency: blockchain technology. Fig. 13The result of the co-occurrence analysis in the field of finance-accounting and digitalization Financial technology appeared for the first time in 2000, which suggests that the technology might have been the basis for the development of the Fintech industry. While some researchers are exploring how banks can use strategies related with entrepreneurial orientation to achieve an excellent performance in the digital era (Niemand et al. 2021), others are investigating how fully virtual operations affect the experience of service and financial habits of users (Windasari et al. 2022). In addition, the demand for money and the financial markets are not only salient topics of investigation of Fintech, but they are also classic topics when investigating the financial industry.
In a period of rapid development of Fintech, many hot terms were generated between 2016 and 2018 such as the regulation of digital currencies and banking sectors, financial transactions and their success, the Fintech industry in global banks, and the new Fintech firms. In-depth AI learning, blockchain, and other information technologies were also investigated (Marinakis and White 2022), as well as the digital wallet, mobile payment service models, P2P lending, and equity crowdfunding transaction models.
The new topics that emerged between 2018 and 2021 reflected the most recent Fintech-related developments. First, the traditional banking industry occupies an essential position within traditional finance, and the banking system and the digital bank are the last directions for development to obtain competitive advantage. Second, the intelligent contract and the payment system is a new and important technology for digital finance. Third, an increasing number of studies have centered on the reputational scores of individual investors and consumer satisfaction. Competition between banks and the efficiency of the processes are therefore important factors that test the development of firms that finance technology. Fourth, crowdfunding and blockchain are two different forms of altering and innovating traditional financial intermediaries, achieving the transformation of a business model (Cai 2018).
Information technology provides guarantees for problems of consumer security. The topic of smart data storage to protect both the privacy and the trust of the consumer is principally related with the field of information safety, which is supported by information-privacy technology, and encryption technology. Academics have launched a series of studies on data-security questions: smart data storage protected by privacy for the financial industry through cloud-computing technology (Qiu et al. 2018), the development of standards for smart financial contracts (Brammertz and Mendelowitz 2018), the application of the blockchain framework to insurance processes within the insurance industry (Raikwar et al. 2018), and secure authentification protocols for mobile payments (Fan et al. 2018). However, Fintech payment services still face challenges in terms of authorization, integrity, privacy, and availability, which affects consumer trust. Information technology is the premise for guaranteeing the security of data and service quality, thereby improving consumer trust even more so (Wiradinata 2018; Sarkar et al. 2020). Some studies have explored how the use of mobile digital payments influences purchasing in terms of such factors as service quality and security (Tang et al. 2021). Khoa [2021] demonstrated that both the benefits and the risks that the consumer perceives are important considerations for the users of mobile payment platforms. See Sun et al. [ 2021] for more detailed information on Fintech-related tendencies and topics.
With regard to accounting, digitalization leads professionals to move beyond their occupational limits. In this way, examples arise of situations in which the boundaries between professions become hazy. One revealing example was provided by Arnaboldi et al. [ 2017], who showed how media marketing specialists entered the area of accounting taking the lead in the management of social networks. Their study indicated that when the professionals crossed the organizational limits, the hybridization of professional roles became self-evident. In Arnaboldi et al. [ 2017], hybridization referred to a situation in which the actors of the organization migrated to other organizational areas, in other words, when marketing specialists entered the terrain of traditional accounting, or when accounting professionals took charge of digitalization initiatives. Social networks have relaxing effects on professional boundaries (Arnaboldi et al. 2017), the driving force being the development of digital tools and techniques. Finally, cryptocurrencies have won broad acceptance in the market and made rapid development despite their recent launches. The academic community has also made considerable efforts to investigate cryptocurrency commerce (see Fang et al. 2022).
## Findings
In this section, the findings of this SLR conducted within the field of digitalization in business economics are reported.
## Research agenda
Research into the digitalization of the firm has to be continuous, because technology is a dynamic organism (Prasad and Green 2015) and, consequently, its impact on the firm is also dynamic. In this section, the emergent tendencies of digitalization are analyzed in the different areas of the firm and identified in various firms, while responding to research question RQ3.
## Theoretical contributions
The systemic review presented in this paper on digitalization in the different functional areas of the firm has highlighted that some theories have hardly been taken into account in any of the disciplines considered in this investigation that support aspects of digitalization, DT, and the digital technologies that have been analyzed. The extended technology acceptance model has been employed for the digitalization of the firm in the area of marketing, in order to estimate the degree of consumer acceptance of digitalization (Labus, 2022), although adaptive theoretical frameworks centered on the Fishbein-Ajzen behavioral-intentions model have also been used (Fishbein and Ajzen 1975; Ajzen and Fishbein 1980). Mental process, the motivation of the consumer, attitudinal and behavioral models, and the relations between the consumer and the brand are found among a number of investigations that have significantly increased (Park and Yoo 2020). However, understanding the effects of mobile technologies on the life of the digital consumer has been turned into a need for effective marketing (Vahdat et al. 2021). In the case of immersive technologies (augmented reality (AR) and virtual reality (VR)), models are needed that include consumer characteristics such as motivations, experience, and familiarity, since these attributes are what change consumer perceptions of both the risks and the benefits of real value. The theoretical progress in management has led to the generation of new theoretical models or the adaptation of existing ones when the limits of DT exceed the established theoretical models (Hanelt et al. 2021). Technological impact, compartmentalized adaptation, systemic change, and holistic co-evaluation (Hanelt et al. 2021) are perspectives that imply an organizational change associated with the generalized diffusion of digital technologies.
Although the evolution of research on digitalization on marketing as a discipline has been centered on the concept of value and its meaning for the consumer in the digital world, as well as on its components -utilitarian and hedonic values- and on joint value creation -collaborative consumption-, some interesting topics have been highlighted in this paper on the frontier between the firm and the consumer, such as value creation and co-creation, technological advances such as AI, AR and VR in apps, impacting on consumer preferences and behavior. The dominant theories, contexts, characteristics, and methodologies identified in the analysis of consumer brand personality perceptions were employed to study the perceptions of consumers towards the personality of digital brands within different digital contexts, such as websites and online stores, social media (mobiles apps and online community), new technologies (AR, mixed reality and VR), and other contexts (online games, virtual worlds and metaverse), without forgetting the interaction between the consumer and AI, such as IoT-based devices, smart objects, chatbots, intelligent assistants, service/social robots, wearables, and AI-driven algorithms/platforms. The digital channels and the use of technology in the omnichannel marketing, or the integration of channels (Hossain et al. 2020), and touchpoints (Wagner et al. 2020) are some of the aspects that have been analyzed in this review. In the retail field, we have highlighted the importance of data within the single-channel context and the proliferation of online technologies and devices that access such data on Internet. Nevertheless, entrepreneurial practices initiated in response to the pandemics have opened new horizons of research (Yaghtin et al. 2021): [1] electronic commerce platforms and satisfying the information needs of clients; [2] products and services within digital showrooms; [3] digital contents to discover new market opportunities; and [4] the data and their high quality analysis, as well as the implementation of other data-based techniques such as automatic learning methods for high-quality data presentations, are posited as acceptable practice to attract clients to the digital platform of firms.
With regard to management, while the interest of research in DT is growing, there are still considerable uncertainties as to what DT is and what it covers. The result is that previous knowledge of organizational change can be problematic as the basis for a better understanding of the phenomenon and to offer informed counsel for practice. DT is defined here as organizational change that is triggered and molded by generalized diffusion of digital technology. The content of this change encompasses a movement towards malleable organizational designs that are integrated and driven by digital business ecosystems. This content of change can be seen from four different perspectives, which include the perspectives of technological impact, compartmentalized adaptation, systemic change, and wholistic co-evolution. The perspectives vary in their contextual scope and are centered on the processes of change within the organization, but share the common characteristic of associating organizational change with the nature of digital technologies, in particular their omnipresence and the dynamics that they induce. When linking our findings with established knowledge on organizational change, the diagnosis is that DT can be better understood as continuous change that can be triggered and molded by episodic upheavals, whereas organizational change induces additional continuous changes.
Finally, the emergent trends within the disciplines of finance and accounting continue to be on the topic of Fintech. First, information technology is still essential to uphold the future development of Fintech, because the additional improvement of algorithms will promote the development of information technology. Second, AI is a critical point of research and, therefore, future research will explicitly emphasize the impact of intelligent products on AI technology in the finance industry. It is acknowledged in the methodology that a complete range of potential clients use robot-consultants and that personal and sociodemographic variables could modulate primary relations (Belanche et al. 2019). Third, digital currency, government, finances, science and technology could be the focus of future investigations (Joao 2018; Rodriguez Bolivar and Scholl 2019). In fourth place, information technology has its unique development process in different financial fields, which will have a disruptive impact on the financial markets (Hua et al. 2019). Finally, the dynamic mechanism and the social impact of Fintech in the future will continue to be the principal direction of research.
## Managerial implications
In this study, some managerial implications have been identified in the field of marketing such as the improvement of the customer experience and the ease of customer relations through immersive technologies such as VR and AR, and interactive technologies such as (AI). In the case of customer experience, although firms can rely on cognitive technologies to improve client experience in the systems of electronic commerce (Krsteva 2016), more research is needed to understand how consumer experiences can be improved when they use technological applications both in shops (Jäger and Weber 2020, van Esch et al. 2021) and in cash machines (Cui et al. 2021), and why immersive technologies within retail commerce are still insufficient (Parekh et al. 2020). Firms should also take into account the experiences of virtual consumers in multiple environments, as well as retail sales and social networks. Continuing with the analysis of the consumer, defining patterns of transcultural consumption (Alsaleh et al. 2019) is an interesting topic for firms because digitalization is global and digital firms can sell and deliver goods beyond geographical frontiers.
Firms must continue to make progress in facilitating relationships with their customers through AI as social media are turned into the most influential channel of digital marketing. Firms could therefore use a model trained for [1] social-media data analysis and visualization in real time for their interpretation; and [2] for studying and visualizing the paths that may lead to a more committed audience (Capatina et al. 2020); and, consequently, to help digital agencies to achieve greater leverage for marketing on social media.
Social networks continue their significant growth, and firms must continue to pay attention to eWOM (Bu et al. 2021; Park et al. 2021), consumer engagement (de Oliveira Santini et al. 2020; García-de-Frutos and Estrella Ramón 2021), and branding (Dabbous and Barakat 2020; McClure and Seock 2020; Schivinski et al. 2021), without forgetting source credibility. Firms have to analyze the impact of social media influencers and the influence of celebrities on their branding activities (Sokolova and Kefi 2020).
In the field of management, the managerial implications are focused on processes of change driven by the growing diffusion of AI, robotics and the Internet, and their effect on organizational design (Korte et al. 2021). The implication of Industry 4.0 technologies for business innovation is turning out to be an interesting topic for firms. Specifically, robotics and additive manufacturing can optimize the innovation of ecological processes (Liu and De Diovanni 2019); the analysis of big data can drive supply chain innovations (Hopkins 2021); and the technologies of Industry 4.0 can catalyze the innovation of the business model for servitization (Frank et al. 2019) and the circular economy (Awan et al. 2021).
This organizational change arising in particular from DT requires the adoption of three important leadership skills within the firm: awareness of DT, acceleration of DT, and harmonization of DT. Each skill involves specific leadership attributes and capabilities and firms that are certain that they have the three leadership skills within their organization are better prepared to master the challenges of DT.
First, managers need not only to observe and to react to changes, but they also need to be aware of the varied quantity of data, and the emergent digital technologies, as well as their properties. Managers must understand that these elements are rapidly evolving and must take into account that they are interlinked with aspects related to the respective characteristics of the market, the consumer, and the country. Managers must use data analysis in a proactive manner as a management tool, in order to take this complexity into account in their evaluations of the conditions of the business environment.
Second, managers require management skills to accelerate DT in the execution of the strategy relating to innovation mechanisms within a multi-dimensional framework. These skills refer to the intellectual capability to conceive processes and novel digital products as a function of internal and external resources, as well as the willingness to invest managerial attention and financial resources during times of episodic upheavals. Above all, it is important to understand that rapid execution and experimentation to learn in the market are superior to ex ante planning and analysis, due to the dynamic nature of the ecosystems of digital businesses, the transience of competitive advantages and positioning.
Third, managers require skills of harmonization with DT with respect to the processes of organizational change. These processes are once again related with the mechanisms within the multi-dimensional framework, but with an approach on integration. The skills of harmonization with DT include managerial skills, so that new products and digital processes can be integrated within the existing organization. It covers the union and communication between ‘physical’ and ‘digital’ units, comprising areas of synergies and areas of friction, reconciling differences, and lending attention to cultural aspects through balance and combination. The managers need to focus their attention here, in other words, to decide what to maintain and what to change in the course of DT, which depends on general opportunities and risks, as well as on each specific industry, and the nature of the changes that may be taking place.
Finally, the managerial implications in the field of finance, are focused on the Fintech sector. The rapid development of technology has had a disruptive impact on traditional financing, not only creating new opportunities for business (crowdfunding, crowdlending, digital money), but also generating a large number of uncontrollable financial risks (Bromberg et al. 2017; Duca 2000): abuse of personal data, which has been turned into one of the main concerns of both consumers and regulators (Jagtiani and John 2018); information asymmetries, and even possible systemic risks have appeared in the process of technology-driven financial innovation (Yang and Li 2018). Bermann et al. [ 2021] showed that external technological funding (for example, indexed funds and exchange-traded funds) improved the competitiveness of small firms and stimulated the business spirit, by reducing the complexity and the costs of financial services. In the risk financing market, financial innovation firms must reach a size and a set quantity to influence the Fintech entrepreneurial spirit in a positive manner (Kolokas et al. 2020).
The new business models of financial services are focused on client needs more than anything else, offering the possibility of instantaneous digital transactions and obtaining sustainable competitive advantage. But the advent of innovative technologies has also created operational challenges for emergent firms, designing operational strategies within the financial service from the viewpoint of the client (Choi 2021). For example, a new hybrid multiple-standards decision-making approach has been proposed to evaluate the innovation of services, with new commercial partners as the maximum priority (Zhao et al. 2019).
## Directions for future research
This systematic review of the literature on digitalization in three mature areas of the firm: marketing, management, and finance and accounting provides future lines of research.
In the field of marketing, the first topic that is shown is related with immersive technologies such as (AR), (VR), and interactive technologies such as AI. The capability of immersive technologies to improve upon the efficiency and the effectiveness of the client experience requires more detailed examination. To do so, the results of immersive technologies need to be investigated in the context of value creation for the client. The impact of technology to facilitate client relations is another frontier of investigation that can be explored. Specifically, IA is a technology quite new in comparison with others such as AR and VR, which means that analyzing the impact of IA on digital consumer behavior patterns is a very broad area of study. The fusion of AI and marketing will certainly grow (Pitt et al. 2018; Wirth 2018; Vishnoi and Bagga 2019) in such a way that investigating the consumer journey facilitated by AI technology can help academia to obtain information on the benefits of IA technology. Some other topics for research that researchers have identified in this field are as follows: to use smart algorithms for in-depth study of automated marketing concepts (Dumitriu and Popescu 2020) and to observe how multichannel AI systems can help to create a lean or a more intelligent technology (Cosmin Tănase 2018).
Future research must seek a more multicultural approach to define the specific differences of each country in terms of the use of online channels, the adoption of technology, and the commitment of digital channels to completely understand digital consumption.
Environmental problems, greater awareness of them among people and the recent COVID-19 health crisis has awakened interest in such topics as sustainability, wellbeing, and value creation. They especially highlighted the studies within the frame of sustainable consumption that concerned their work, principally with collaborative consumption in a digital context, and online rental activities (Lee and Chow 2020; Lee and Huang 2020; Luo et al. 2020).
The possible future of social media according to marketing-related questions and its impact on the individual, the firm and public policies is another area for future research that will be addressed in coming years. Finally, another subject that is closely associated with social media studies is source credibility. Specifically, the investigation will be centered on analyzing the impact of social media influencers and the impact of celebrities on firm branding activities (Sokolova and Kefi 2020).
In the field of management and managerial skills, continued research on DT is necessary through the perspective of systemic changes, holistic co-evaluation, and compartmentalized adaptation to advance towards the development of awareness, acceleration, and harmonization of DT, respectively. Also, more research on all those aspects of intra-organizational integration and innovation as drivers of malleable organizational designs is still needed. For example, when describing strategies and steps on how organizations can guide their efforts to achieve DT, it is also proposed that future investigations on DT be approached through a complementary ethical perspective focused on three aspects: the balance between organizational effort, ethics, and value co-creation. Finally, a whole research gap is opened on the implications of Industry 4.0 technologies in business innovation (Oztemet and Gursev 2020).
In finance, the topics that have been identified for research in this study contribute to various currents of thought in the literature, including the emergent literature on Fintech, and the literature on entrepreneurship, innovation, and international business. In the first place, the field of Fintech entrepreneurship is an emergent area that is fundamental to promote entrepreneurship and the production of innovative technologies (Álvarez et al. 2016). Specifically, external factors can influence the distribution of the quality of entrepreneurship when the entry of low-quality firms is reduced (Khanin et al. 2022; Ritala et al. 2021). Studies have compared the differences between reward crowdfunding and capital crowdfunding in the development of Fintech, which can help to comprehend the workings of the entrepreneurial spirit better (Troise et al. 2021). In addition, Fintech as a strategy in the digital age, is presented as an interesting research topic, since it is having an impact on cross-border commerce, causing changes in the nature of organizations and in-service businesses (for example, commercial contracts and currency trading). Finally, the business model of digital money is another of the focuses of future research (Joao 2018; Rodriguez Bolivar and Scholl 2019), since mobile payments have almost pervaded the daily life of many people, with wide coverage, and innovative payment methods that show constant vitality (Sun et al. 2022).
In summary, the challenges that the digitalization of firms, institutions and society in general presents in this day and age are cybersecurity and user privacy (Wylde et al. 2022). Specifically, maintaining a balance between the anonymity of users and the traceability of the transaction (Fujitani et al. 2021); guaranteeing security and privacy of financial data in the face of a tendency towards open banking (Liao et al. 2022), financial scamming, and hacking (Zhao 2021). In the case of SMEs, what remains for them to do is to employ the technology as a means of transformation to confront a competitive, complex, and uncertain business environment (Rozmi et al. 2021) All these challenges are presented as incipient lines of investigation, together with others such as the processing of information obtained with different technologies from firms and their rapid response to market fluctuations (Li et al. 2021). The way in which automatic learning algorithms function and the extent to which they can affect organizational decisions and actions, being at the root of tactical and strategic errors, also remain to be studied in depth.
## Conclusions and limitations
Over the past decade and above all, as from 2020, an increase in the number of publications on digitalization, digital technologies, and DT have been seen. Although this paper is centered on the more mature areas of the firm, the literature is limited. In this paper, a detailed analysis of current achievements in investigation of digitalization has been performed, taking into account the most relevant publications. A systematic review has been conducted of the literature based on the sample of 119 high-quality peer-reviewed review articles in the fields of management, marketing, and finance and accounting.
In the discipline of marketing, five principal research tendencies have been identified in current investigations: [1] the performance of digital marketing from the perspective of the consumer centered on its adoption and engagement; [2] the management of social networks as a marketing strategy; [3] knowledge of consumer behavior through big-date technology; [4] the impact of mass-media campaigns through digital technologies; and [5] the customer journey via online outlets.
In the case of management, three principal tendencies have been highlighted in this study: [1] information management and performance, so that it is converted into knowledge for organizations; [2] the design of flexible organizations to adapt themselves to their business environment; [3] the impact of Internet technologies and digitalization on logistics and supply channel management.
Finally, in the discipline of finances and accounting, a tendency may be highlighted for each discipline. In finances, the principal research tendency is to continue inquiring into technologies such as blockchain applied to financial services, whereas in accounting the impact of cryptocurrency may be highlighted in accounting systems and risk management.
As a final conclusion, this paper may be considered to have: [1] provided the most complete and updated review of digitalization from a global perspective, summarizing the current state of knowledge within an integrated framework; [2] reduced the complexity of digitalization by offering structure and clarity; and [3] advanced links between digitalization and established points of view in the literature on management, marketing, and finance and accounting.
Our study has some limitations that can be identified in the steps of our research process. First, given that our study used a particular database, some articles may have been overlooked in the data-compilation process. Second, the filters applied in the data-analysis process might have omitted some relevant studies, due to our choices of cut-off decisions (for example, with respect to the time frames). In addition, the process of encoding the articles was manual and might therefore have been affected by subjectivity, although we sought to avoid that bias by performing multiple encoding routines and various rounds of checking the encoding. Third, with respect to the summarization, it is accepted that there might be valuable alternatives to an ‘entry-process-exit’ model (Edmondson and Mcmanus 2007) as a meta-structure. In the light of these limitations, we encourage other researchers to enlarge and to improve upon our findings, employing different sources and analytical approaches.
## Appendix
Appendix 1: Corpus of review articles analyzed in the study.
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|
---
title: Measuring grit, self-efficacy, curiosity, and intolerance of uncertainty in
first-generation college and first-generation osteopathic medical students
authors:
- DeWitt Jones
- Monet McCalla
- Elizabeth A. Beverly
journal: BMC Medical Education
year: 2023
pmcid: PMC10043857
doi: 10.1186/s12909-023-04181-9
license: CC BY 4.0
---
# Measuring grit, self-efficacy, curiosity, and intolerance of uncertainty in first-generation college and first-generation osteopathic medical students
## Body
Medical school is a challenging time for students. Increasingly, medical students report symptoms of burnout, depression, anxiety, suicidal ideation, and psychological distress during pre-clinical and clinical years [1–4]. Prior research has identified specific factors that are protective against the negative psychosocial effects of medical school. A few of these factors include grit [5], self-efficacy [6, 7], and curiosity [8]. For example, grit serves as a protective factor against burnout [9] and self-efficacy lowers levels of psychological distress in medicals students [10]. Also, students with high levels of curiosity are able to leverage more personal and social resources in the face of stressful situations, which contributes to improved emotional well-being [11].
Conversely, intolerance of uncertainty is associated with increased burnout, depressive symptoms, and psychological distress in medical students [12–14]. Yet, the practice of medicine is intrinsically uncertain. This is pertinent considering medical students who struggle to cope with uncertainty experience increased psychological distress compared to students with a higher tolerance for uncertainty [12]. Interestingly, higher levels of grit, self-efficacy, and curiosity are associated with greater tolerance to uncertainty [15–17]. Further, higher levels of curiosity can resolve uncertainty by seeking new information to address gaps in knowledge [18, 19]. Thus, identifying students at increased risk for the negative mental and emotional effects of medical school is needed to provide additional support and resources to build grit, self-efficacy, curiosity, and tolerance for uncertainty.
First-generation college and first-generation medical students may be two groups at increased risk for the negative psychosocial effects of medical school. Findings from an Association of American Medical Colleges’ Pilot Survey found first-generation college students reported higher stress, fatigue, lower quality of life, and less social support than non-first-generation students [20]; the effects on first-generation medical school students is not known. For this reason, assessing levels of grit, self-efficacy, curiosity, and intolerance of uncertainty among first-generation college and first-generation medical students is important to identify potential differences and key areas for intervention. Therefore, we aimed to examine the relationship among grit, self-efficacy, curiosity, and intolerance of uncertainty in medical students. We examined uncertainty by first-generation college (i.e., defined by first person in nuclear family to earn a college degree) and first-generation medical students (i.e., define by students with and without physician parents and students with and without physician relatives). In addition, we examined associations among first-generation college and first-generation medical student status predicting grit, self-efficacy, curiosity and exploration, and intolerance of uncertainty, controlling for age, race, and year in medical school.
We hypothesized that first-generation medical student status would predict grit, self-efficacy, curiosity, and intolerance of uncertainty, but not first-generation college student status. We based this hypothesis on the large percentage of first-generation college students in the United States (i.e., $56\%$) along with recent efforts to target programming at institutions of higher education to reduce disparities between first-generation college students and non-first-generation college students [21]. Conversely, with minimal information available about this newly emerging group, termed first-generation medical students, we expected differences in these constructs.
## Abstract
### Background
Medical school is a challenging time, with many medical students reporting symptoms of burnout, depression, anxiety, suicidal ideation, and psychological distress during pre-clinical and clinical years. First-generation college and first-generation medical students may be two groups of students at increased risk for the negative psychosocial effects of medical school. Importantly, grit, self-efficacy, and curiosity are protective factors against the negative psychosocial effects of medical school, whereas intolerance of uncertainty is a risk factor. Thus, research examining the associations among grit, self-efficacy, curiosity, and intolerance of uncertainty in first-generation college and first-generation medical students is needed.
### Methods
We conducted a cross-sectional, descriptive study to assess medical students’ grit, self-efficacy, curiosity, and intolerance of uncertainty. We conducted independent samples t-tests and regression analyses using SPSS statistical software version 28.0.
### Results
A total of 420 students participated in the study for a response rate of $51.5\%$. One-fifth of participants ($21.2\%$, $$n = 89$$) identified as first-generation students, $38.6\%$ ($$n = 162$$) participants reporting having a physician relative, and $16.2\%$ ($$n = 68$$) reported having a physician parent. Grit, self-efficacy, and curiosity and exploration scores did not differ by first-generation college status, physician relative(s), or physician parent(s). However, total intolerance of uncertainty scores differed by physician relative(s) (t= -2.830, $$p \leq 0.005$$), but not by first-generation status, or physician parent(s). Further, subscale scores for prospective intolerance of uncertainty differed by physician relative(s) (t= -3.379, $$p \leq 0.001$$) and physician parent(s) (t= -2.077, $$p \leq 0.038$$), but not by first-generation college student status. In the hierarchical regression models, first-generation college student status and first-generation medical student status were not predictive of grit, self-efficacy, curiosity and exploration, or intolerance of uncertainty, although statistical trends were observed with students with physician relative(s) predicting lower intolerance of uncertainty scores (B= -2.171, t= -2138, $$p \leq 0.033$$) and lower prospective intolerance of uncertainty (B= -1.666, t= -2.689, $$p \leq 0.007$$).
### Conclusions
These findings suggest that first-generation college students did not differ by grit, self-efficacy, curiosity, or intolerance of uncertainty. Similarly, first-generation medical students did not differ by grit, self-efficacy, or curiosity; however, first-generation medical students showed statistical trends in higher total intolerance of uncertainty and higher prospective intolerance of uncertainty. Additional research needs to confirm these findings in first-generation medical students.
## Methods
We utilized a descriptive, cross-sectional survey design to assess medical students’ grit, self-efficacy, curiosity, and intolerance of uncertainty. Specifically, we recruited medical students aged 18 years and older who were able to read and write in English.
## Ethics approval
Ethics approval for the study was obtained from the Ohio University Office of Research Compliance Institutional Review Board (approval number: 16-X-294). In complying with federal, state, and local laws and regulations for human subjects, we ensured our research met the requirements set forth in the regulations on public welfare in Part 46 of Title 45 of the Code of Federal Regulations (45 CFR 46); the principles set forth in “The Belmont Report,” and the Helsinki Declaration of 1975. Informed consent was obtained from all participants and all consented to participate in the study.
## Sample size
We conducted an a priori power analysis using G*Power 3 software [22], which determined a total sample size of 328 students (i.e., 66 first-generation college students and 262 non-first generation college students; 73 first-generation medical students and 109 non-first generation medical students) was estimated for $80\%$ power at a $5\%$ significance level ($p \leq 0.05$) to detect an effect size of $d = 0.5.$
## Participants
The electronic, anonymous survey was distributed to all medical students currently enrolled at the main campus ($$n = 569$$) and two distance campuses (A $$n = 146$$; B $$n = 101$$) of a large osteopathic medical school located in the United States. Osteopathic medical schools require the same rigorous training as allopathic medical schools. The main differences between osteopathic and allopathic medical schools include the following: [1] osteopathic medicine focuses on the whole body, and [2] osteopathic physicians have completed an additional 200 h of hands-on training with the musculoskeletal system known as osteopathic manipulative treatment. The email invitation was distributed by the study investigator (EAB) via school-maintained class lists. The survey opened on October 11, 2016 and a reminder email was sent on November 1, 2016. Participation in the study was completely voluntary.
## Measures
Participants completed a short demographic form (e.g., age, gender, race, ethnicity, year in medical school). No identifying information (i.e., name, email address, race/ethnicity) was collected in order to maintain anonymity. In addition, participants completed the following validated measures:
## Grit scale
a 12-item scale measuring consistency of interests and perseverance of effort on a 5-point scale, ranging from 1= “Not at all like me” to 5= “Very much like me.” The Grit Scale has good internal consistency (α = 0.85) for the overall scale and for each factor (Consistency of Interests, = 0.84; Perseverance of Effort, α = 0.78) [23].
## General self-efficacy scale
a 10-item scale measuring self-efficacy, or positive self-beliefs to cope with a difficult life demands, on a 4-point scale, ranging from 1=“Not at all true” to 4=“Exactly true.” The General Self-Efficacy Scale has good internal consistency ranging from α = 0.76 to α = 0.90 [24].
## Curiosity and exploration inventory
a 7-item measure exploring recognition, pursuit, and integration of new and challenging experiences on a 7-point scale, ranging from 1= “Strongly disagree” to 7= “Strongly agree.” This inventory yields two factors: Exploration, or the pursuit of new experiences, and Absorption, or being engaged in activities. The scale demonstrates good convergent and discriminant validity as well as internal consistency (α = 0.80) [11].
## Intolerance of uncertainty scale - short form
a 12-item scale measuring responses to uncertainty, ambiguous situations, and the future on a 5-point scale, ranging from 1 = “Not at all characteristic of me” to 5= “Entirely characteristic of me.” This scale yields two subscales: [1] Prospective Intolerance of Uncertainty Subscale, or fear in anticipation of future uncertainty, and [2] Inhibitory Intolerance of Uncertainty Subscale, inaction in the face of uncertainty. The scale demonstrates good convergent and discriminant validity as well as internal consistency (α = 0.85) [25]. Higher scores indicate higher intolerance of uncertainty.
## Data collection
Participants completed the survey online without identifying information via the online questionnaire service Qualtrics (Provo, UT: Qualtrics). Consent occurred online. As in previous studies conducted by the research team [26–30], participants clicked a radio button indicating “Yes, I consent to participate in this study. I may withdraw my participation at any time.“ Conversely, if participants declined to participate, they clicked a radio button indicating “I decline to participate.“ Both the online welcome screen for the survey and the informed consent document specified the voluntary nature of participation. To avoid coercion, no researchers were present when individuals elected to participate or decline, which may have generated less pressure than in a face-to-face consent process. All participants were instructed to contact the research investigators by email or telephone if they had any questions. Completion of the entire survey took approximately 30 min. Participants received a $15.00 gift card as human subject compensation. To receive the gift card, participants clicked on a new Qualtrics link where they could provide an email address to receive a gift card. This step was necessary to that their responses were not linked to their email address.
## Data analysis
Basic sociodemographic characteristics of participants were assessed using descriptive statistics. Frequencies of individual question responses were also calculated. Next, we calculated survey scores for the Total Grit Scale, Grit Subscale: Consistency of Interests, Grit Subscale Perseverance of Effort, General Self-Efficacy Scale, Curiosity and Exploration Inventory, Exploration Subscale, and Absorption Subscale, Intolerance of Uncertainty, Prospective Intolerance of Uncertainty Subscale, and Inhibitory Intolerance of Uncertainty Subscale. For missing data, we specified pairwise deletion to maximize all data available on an analysis by analysis basis. Next, we conducted bivariate (Pearson) correlations to determine associations among the survey scores and age. Correlations were evaluated by the strength of the correlation coefficient (negligible = 0.0-0.3, low-0.3-0.5, moderate = 0.5–0.7, high = 0.7–0.9, very high = 0.9-1.0) [31]. Independent samples t-tests were used to examine mean differences in grit, self-efficacy, curiosity and exploration, and intolerance of uncertainty by sociodemographic categories and by first-generation college status (i.e., first person in nuclear family to earn a college degree), and first-generation medical student status (i.e., students with and without parent physician(s) and students with and without physician relative(s). Finally, we conducted hierarchical linear regression models to examine associations among first-generation college and first-generation medical student status predicting grit, self-efficacy, curiosity and exploration, and intolerance of uncertainty, controlling for age, gender, race, and year in medical school. For gender, race, ethnicity, and year in medical school we created dummy variables for each category, with the category = 1 and all other data = 0 (e.g., Year 1 = 1, All other years = 0). Statistically significant variables from the bivariate correlations and independent t-tests were included in the regression models. Statistical significance was defined as $p \leq 0.05$ unless a Bonferroni correction was used. All analyses were conducted with SPSS statistical software version 28.0 (Chicago, IL: SPSS Inc.).
## Results
Of the 816 osteopathic medical students enrolled at the three campuses, 420 students participated in the study for a response rate of $51.5\%$. The mean age of the participants was 25.4 ± 3.2 years, $55.5\%$ ($$n = 233$$) identified as women, $44.3\%$ ($$n = 186$$) identified as men, and $0.2\%$ ($$n = 1$$) reported an identity not listed. The self-reported racial distribution of the sample included $0.5\%$ ($$n = 2$$) American Indian/Alaskan Native, $8.3\%$ Asian ($$n = 35$$), $7.1\%$ ($$n = 30$$) Black/African American, $0.5\%$ ($$n = 2$$) Native Hawaiian/Pacific Islander, $4.8\%$ ($$n = 20$$) Other, and $78.3\%$ ($$n = 329$$) White; and $4.3\%$ ($$n = 18$$) identified as Hispanic/Latino. The distribution by year in medical school was $37.4\%$ ($$n = 157$$) for first-year, $31.2\%$ ($$n = 131$$), $18.8\%$ ($$n = 79$$), and $12.6\%$ ($$n = 53$$). Note, the third- and fourth-year medical student classes did not have students on the three campuses, thus their class sizes were much smaller. Finally, one-fifth of participants ($21.2\%$, $$n = 89$$) identified as first-generation students, $38.6\%$ ($$n = 162$$) participants reporting having a physician relative, and $16.2\%$ ($$n = 68$$) reported having a physician parent. Additional demographic data are presented in Table 1.
Table 1Participant Demographics ($$n = 420$$)VariableParticipantsn (%)Age (years)25.4 ± 3.2Gender Female233 (55.5) Male186 (44.3) An identity not listed1 (0.2) Prefer not to answer0 [0]Race American Indian/Alaskan Native2 (0.5) Asian35 (8.3) Black/African American30 (7.1) Native Hawaiian/Pacific Islander2 (0.5) Other20 (4.8) White/Caucasian329 (78.3)Ethnicity Hispanic/Latino18 (4.3)Year in medical school Year 1157 (37.4) Year 2131 (31.2) Year 379 (18.8) Year 453 (12.6)*First* generation student Yes89 (21.2) No330 (78.6)Physician parent(s) Yes68 (16.2) No352 (83.8)Physician relative(s) Yes162 (38.6) No258 (61.4)
## Correlational findings
Grit had negligible to low correlations with self-efficacy ($r = 0.323$; see Table 2), curiosity and exploration ($r = 0.131$), absorption ($r = 0.126$), intolerance of uncertainty (r= -0.221), prospective intolerance of uncertainty (r= -0.117), and inhibitory intolerance of uncertainty (r= -0.311). Consistency was correlated with self-efficacy ($r = 0.212$), intolerance of uncertainty (r= -0.223), prospective intolerance of uncertainty (r= -0.140), inhibitory intolerance of uncertainty (r= -0.228), and age (r= -0.120). Similarly, perseverance was correlated with self-efficacy ($r = 0.324$), curiosity and exploration ($r = 0.167$), exploration ($r = 0.161$), absorption ($r = 0.116$), intolerance of uncertainty (r= -0.124). inhibitory intolerance of uncertainty (r= -0.208), and age ($r = 0.102$). Similarly, self-efficacy had negligible to low correlations with curiosity and exploration ($r = 0.359$), exploration ($r = 0.319$), absorption ($r = 0.273$), intolerance of uncertainty (r= -0.263), prospective intolerance of uncertainty (r= -0.161), and inhibitory intolerance of uncertainty (r= -0.343). Curiosity and exploration was correlated with inhibitory intolerance of uncertainty (r= -0.104) and age (r= -0.125). Interestingly, exploration was correlated with inhibitory intolerance of uncertainty (r= -0.131), and absorption was correlated with prospective intolerance of uncertainty ($r = 0.151$) and age (r= -0.141). Finally, intolerance of uncertainty and prospective intolerance of uncertainty were correlated with age (r= -0.115, r= -0.114), respectively.
Table 2Intercorrelations among Psychological Characteristics and Age ($$n = 392$$)ConsistencyPerseveranceSelf-EfficacyCEIExplorationAbsorptionIUSProspective IUSInhibitory IUSAgeGrit0.856***0.750***0.323***0.131**0.0940.126*-0.221***-0.117*-0.311***-0.028Consistency0.300***0.212***0.0580.0090.091-0.223***-0.140**-0.286***-0.120*Perseverance0.324***0.167***0.161**0.116**-0.124*-0.038-0.208***0.102*Self-Efficacy0.359***0.319***0.273***-0.263***-0.161**-0.343***-0.040CEI0.842***0.821***-0.0010.082-0.104*-0.125*Exploration0.384***-0.071-0.012-0.131**-0.095Absorption0.0730.151**-0.038-0.114*IUS0.936***0.899***-0.115*Prospective0.689***-0.114*Inhibitory-0.096*$p \leq 0.05$, **$p \leq 0.01$,***$p \leq 0.001$; CEI = Curiosity and Exploration Inventory; IUS = Intolerance of Uncertainty Scale
## Grit
The total mean for grit was 3.7 ± 0.5, and the subscales scores for consistency and perseverance were 3.4 ± 0.7 and 4.1 ± 0.5, respectively (see Table 3). Grit scores did not differ by first-generation college status (t= -0.283, $$p \leq 0.778$$), physician relative(s) ($t = 0.057$, $$p \leq 0.955$$), or physician parent(s) (t= -0.844, $$p \leq 0.399$$).
Table 3Mean Differences in Grit, Self-Efficacy, Curiosity and Exploration, and Intolerance of Uncertainty Scores by Sociodemographic Variables and First-Generation Statuses ($$n = 392$$)GritM ± SDConsistencyM ± SDPerseveranceM ± SDSelf-EfficacyM ± SDCuriosity & ExplorationM ± SDExplorationM ± SDAbsorptionM ± SDIntolerance of UncertaintyM ± SDProspectiveM ± SDInhibitoryM ± SD Total All participants3.7 ± 0.53.4 ± 0.74.1 ± 0.532.3 ± 4.734.2 ± 5.520.6 ± 3.413.5 ± 3.230.0 ± 8.519.6 ± 5.210.4 ± 4.1 Gender ‡ Men3.7 ± 0.5*3.3 ± 0.7*4.0 ± 0.633.1 ± 4.8**34.2 ± 5.320.6 ± 3.313.6 ± 3.129.3 ± 8.219.0 ± 5.010.3 ± 3.9Women3.8 ± 0.53.4 ± 0.74.1 ± 0.531.8 ± 4.234.3 ± 5.520.7 ± 3.413.5 ± 3.230.5 ± 8.820.0 ± 5.310.5 ± 4.3 Race ‡ American Indian/Alaskan Native3.6 ± 0.82.7 ± 2.44.5 ± 0.723.5 ± 19.1***26.5 ± 14.8*16.5 ± 6.410.0 ± 8.536.5 ± 10.622.0 ± 1.414.5 ± 9.2Asian3.4 ± 0.6***2.9 ± 0.8***3.8 ± 0.7**29.5 ± 5.8***36.0 ± 4.9*21.7 ± 3.214.3 ± 2.837.1 ± 8.5***22.9 ± 5.1***14.2 ± 4.3***Black/African American4.0 ± 0.5**3.8 ± 0.7**4.2 ± 0.533.3 ± 4.331.8 ± 6.4**18.8 ± 3.9**13.0 ± 3.328.4 ± 9.019.3 ± 6.39.1 ± 3.5Native Hawaiian/Pacific Islander3.7 ± 0.53.3 ± 0.44.1 ± 0.634.0 ± 2.838.5 ± 3.523.0 ± 5.715.5 ± 2.132.0 ± 4.222.0 ± 1.410.0 ± 2.8White3.8 ± 0.5*3.4 ± 0.6*4.1 ± 0.532.6 ± 4.1*34.3 ± 5.320.8 ± 3.313.5 ± 3.229.2 ± 8.1***19.1 ± 5.0**10.0 ± 3.9***Other3.6 ± 0.43.1 ± 0.64.0 ± 0.531.7 ± 7.831.8 ± 5.318.9 ± 3.4*12.9 ± 3.431.6 ± 10.320.3 ± 5.511.3 ± 5.3 Ethnicity ‡ Hispanic/Latino3.7 ± 0.52.9 ± 1.0*4.4 ± 0.5*29.5 ± 8.3*31.2 ± 7.719.1 ± 4.612.1 ± 4.031.3 ± 11.320.3 ± 6.610.9 ± 5.5 Year in school ‡ Year 13.8 ± 0.5*3.4 ± 0.74.2 ± 0.5**32.3 ± 5.234.4 ± 5.720.7 ± 3.713.7 ± 3.229.6 ± 8.819.3 ± 5.410.2 ± 4.2Year 23.7 ± 0.53.4 ± 0.74.0 ± 0.632.6 ± 4.334.5 ± 5.420.7 ± 3.413.8 ± 3.130.0 ± 8.219.7 ± 4.910.3 ± 4.0Year 33.7 ± 0.53.4 ± 0.64.1 ± 0.531.9 ± 4.034.2 ± 5.120.5 ± 3.113.6 ± 3.131.2 ± 9.120.2 ± 5.510.9 ± 4.3Year 43.6 ± 0.5*3.2 ± 0.8*4.0 ± 0.531.9 ± 4.932.7 ± 5.6*20.3 ± 3.012.4 ± 3.5*29.2 ± 7.818.9 ± 4.710.3 ± 4.1 First-generation college students ‡ Yes3.7 ± 0.53.4 ± 0.74.1 ± 0.632.2 ± 5.134.3 ± 6.120.5 ± 3.613.7 ± 3.430.6 ± 8.819.9 ± 5.210.7 ± 4.3No3.7 ± 0.53.4 ± 0.74.1 ± 0.532.3 ± 4.634.2 ± 5.320.7 ± 3.313.5 ± 3.229.8 ± 8.519.5 ± 5.110.3 ± 4.1 Physician parent(s) ‡ Yes3.7 ± 0.53.3 ± 0.84.1 ± 0.532.7 ± 4.934.0 ± 5.920.5 ± 3.613.5 ± 3.628.1 ± 8.918.3 ± 5.4*9.8 ± 4.4No3.7 ± 0.53.4 ± 0.74.1 ± 0.532.2 ± 4.634.2 ± 5.420.7 ± 3.313.6 ± 3.130.3 ± 8.419.8 ± 5.110.5 ± 4.1 Physician relative(s) ‡ Yes3.7 ± 0.53.4 ± 0.74.1 ± 0.632.5 ± 4.734.4 ± 5.820.9 ± 3.713.5 ± 3.328.5 ± 8.9**18.5 ± 5.4***10.0 ± 4.3No3.7 ± 0.53.4 ± 0.64.1 ± 0.532.2 ± 4.734.0 ± 5.320.5 ± 3.213.5 ± 3.231.0 ± 8.220.3 ± 4.910.7 ± 4.0*$p \leq 0.05$,**$p \leq 0.01$,***$p \leq 0.001$; M = Mean; SD = Standard Deviation; ‡Variables recoded as dummy variables with each variable category = 1 and all other data coded = 0 The overall regression model was significant (F[11,376] = 4.44, $p \leq 0.001$; see Table 4). In Step 1 (Bonferroni correction was $p \leq 0.00833$), Black/African American race ($B = 0.399$, $t = 2.721$, $$p \leq 0.007$$) was independently associated with total grit scores. In step 2 (Bonferroni correction was $p \leq 0.00625$), gender (B= -0.128, t= -2.623, $$p \leq 0.004$$) was the only statistically significant predictor of grit. Finally, in step 3 (Bonferroni correction was $p \leq 0.00454$), gender remained the only significant predictor of grit.
Table 4Summary of Hierarchical Regression Analyses Examining the Associations among Sociodemographic Characteristics and First-Generation Statuses with Grit, Self-Efficacy, Curiosity and Exploration, and Intolerance of Uncertainty ($$n = 387$$)Dependent Variable Independent Variables ‡ Step 1 a Step 2 b Step 3 c Model 1: Grit B SE t p B SE t p B SE t p Age-0.0080.008-0.9270.3540.0020.0090.2570.7970.0020.0090.1790.858Gender-0.1280.049-2.6230.009-0.1380.048-2.8600.004-0.1410.048-2.9190.004Asian-0.2520.135-1.8690.062-0.2510.134-1.8740.062-0.2640.134-1.9640.050Black/African American0.3990.1472.7210.0070.3840.1462.6330.0090.3850.1472.6220.009White0.1510.1101.3750.1700.1560.1101.4210.1560.1500.1101.3640.173Hispanic/Latino0.0670.1300.5160.6060.0300.1300.2280.8190.0420.1300.3250.746Year 10.1220.0532.3040.0220.1230.0532.3090.021Year 4-0.1280.078-1.6520.099-0.1210.078-1.5500.122First-generation college-0.0440.061-0.7140.476Physicians in family0.0330.0580.5660.572Physician parents-0.1030.077-1.3510.178 Model 1a: Consistency B SE t p B SE t p B SE t p Age-0.0240.011-2.1210.035-0.0160.012-1.3130.190-0.0180.012-1.4400.151Gender-0.1580.066-2.3800.018-0.1650.066-2.4880.013-0.1700.066-2.5710.011Asian-0.3320.183-1.8090.071-0.3440.184-1.8690.062-0.3720.184-2.0210.044Black/African American0.5630.2002.8180.0050.5370.2012.6760.0080.5260.2012.6160.009White0.1760.1501.1710.2420.1670.1511.1050.2700.1580.1511.0480.295Hispanic/Latino-0.1570.178-0.8870.376-0.1940.178-1.0890.277-0.1730.178-0.9690.333Year 10.0590.0730.8160.4150.0560.0730.7760.438Year 4-0.1630.107-1.5280.127-0.1530.107-1.4280.154First-generation college-0.0330.084-0.3890.698Physicians in family0.0880.0801.1080.269Physician parents-0.2490.105-2.3790.018 Model 1b: Perseverance B SE t p B SE t p B SE t p Age0.0090.0090.9380.3490.0210.0102.1060.0360.0210.0102.1100.036Gender-0.0980.054-1.8110.071-0.1110.053-2.0880.037-0.1120.053-2.1040.036Asian-0.1710.149-1.1510.250-0.1580.147-1.0700.285-0.1560.148-1.0550.292Black/African American0.2350.1621.4480.1480.2320.1611.4430.1500.2440.1621.5090.132White0.1270.1221.0440.2970.1450.1211.2020.2300.1420.1211.1730.241Hispanic/Latino0.29200.1442.0260.0430.2530.1431.7740.0770.2570.1441.7910.074Year 10.1840.0583.1650.0020.1890.0593.2250.001Year 4-0.0940.086-1.0940.275-0.0900.086-1.0400.299First-generation college-0.0550.067-0.8130.417Physicians in family-0.0220.064-0.3480.728Physician parents0.0420.0840.5020.616 Model 2: Self-Efficacy B SE t p B SE t p B SE t p Age0.0290.0790.3670.7140.0330.0850.3870.6990.0360.0850.4240.672Gender1.1770.4592.5630.0111.1730.4622.5410.0111.1900.4642.5660.011Asian-2.5811.250-2.0640.040-2.5811.260-2.0480.041-2.5231.268-1.9900.047Black/African American1.0021.3780.7270.4670.9981.3900.7180.4730.9691.4010.6920.489White0.2271.0140.2240.8230.2281.0250.2230.8240.2631.0290.2550.799Hispanic/Latino-1.3971.226-1.1400.255-1.4121.236-1.1420.254-1.4801.245-1.1890.235Year 10.0400.5070.0790.9370.0250.5100.0490.961Year 4-0.0580.748-0.0780.938-0.0960.753-0.1270.899First-generation college0.3600.5890.6110.541Physicians in family-0.0300.557-0.0540.957Physician parents0.3450.7330.4700.639 Model 3: CEI B SE t p B SE t p B SE t p Age0-0.0840.095-0.8890.375-0.0520.101-0.5150.607-0.0530.102-0.5250.600Gender-0.1280.551-0.2320.816-0.1460.553-0.2630.792-0.1390.555-0.2500.803Asian2.7811.5231.8260.0692.6611.5321.7370.0832.6621.5391.7290.085Black/African American-1.2531.659-0.7550.451-1.4381.671-0.8600.390-1.5581.681-0.9260.355White1.1261.2460.9040.3671.0141.2560.8080.4201.0701.2600.8490.397Hispanic/Latino-1.8241.473-1.2380.217-2.0001.483-1.3480.178-2.0161.491-1.3520.177Year 10.0030.6050.0040.997-0.0570.608-0.0940.925Year 4-0.9810.896-1.0950.274-1.0000.901-1.1100.268First-generation college0.6720.7030.9550.340Physicians in family0.5670.6660.8520.395Physician parents-0.6870.877-0.7840.434 Model 3a: Exploration B SE t p B SE t p B SE t p Age-0.0190.059-0.3160.752-0.0140.063-0.2180.827-0.0150.063-0.2380.812Gender-0.2060.342-0.6010.548-0.2130.344-0.6190.536-0.2240.345-0.6510.516Asian2.0680.9462.1870.0292.0910.9532.1950.0292.0840.9572.1780.030Black/African American-0.8431.030-0.8190.413-0.8251.039-0.7940.428-0.8371.045-0.8010.424White1.1630.7741.5030.1341.1870.7811.5200.1291.2170.7831.5540.121Hispanic/Latino-0.9660.915-1.0550.292-0.9730.923-1.0550.292-0.9210.927-0.9940.321Year 10.1300.3760.3450.7310.1020.3780.2710.786Year 40.0410.5580.0730.9420.0670.5600.1190.905First-generation college0.1850.4370.4240.672Physicians in family0.5940.4141.4330.153Physician parents-0.6370.545-1.1700.243 Model 3b: Absorption B SE t p B SE t p B SE t p Age-0.0660.056-1.1700.243-0.0380.060-0.6420.521-0.0380.060-0.6390.523Gender0.0780.3260.2380.8120.0670.3260.2060.8370.0860.3280.2620.794Asian0.7130.9010.7910.4300.5700.9030.6310.5290.5780.9080.6360.525Black/African American-0.4090.982-0.4170.677-0.6130.985-0.6220.535-0.7210.992-0.7260.468White-0.0370.737-0.0500.960-0.1730.741-0.2330.816-0.1480.744-0.1990.842Hispanic/Latino-0.8580.872-0.9840.326-1.0260.875-1.1730.242-1.0940.880-1.2440.214Year 1-0.1270.357-0.3560.722-0.1600.359-0.4450.656Year 4-1.0220.529-1.9330.054-1.0670.532-2.0060.046First-generation college0.4870.4151.1730.242Physicians in family-0.0260.393-0.0670.947Physician parents-0.0500.517-0.0970.923 Model 4: IUS B SE t p B SE t p B SE t p Age-0.3940.146-2.6980.007-0.4530.156-2.8960.004-0.4700.155-3.0250.003Gender-0.8990.850-1.0580.291-0.8080.851-0.9500.343-0.7610.846-0.9000.369Asian4.7272.3452.0160.0454.4482.3541.8890.0604.0362.3441.7220.086Black/African American-2.7562.578-1.0690.286-3.0342.592-1.1710.243-3.5402.583-1.3710.171White-2.8051.918-1.4620.145-3.1061.931-1.6090.109-3.3491.920-1.7450.082Hispanic/Latino0.2842.2690.1250.9000.3752.2800.1640.8690.1652.2720.0730.942Year 1-1.5660.933-1.6770.094-1.5700.929-1.6900.092Year 4-0.5221.378-0.3790.705-0.6741.373-0.4910.624First-generation college0.1841.0730.1710.864Physicians in family-2.1711.015-2.1380.033Physician parents-0.6691.335-0.5010.617 Model 4a: Prospective IUS B SE t p B SE t p B SE t p Age-0.2320.089-2.5950.010-0.2580.096-2.6980.007-0.2710.095-2.8550.005Gender-0.8370.520-1.6080.109-0.7900.521-1.5150.131-0.7550.516-1.4630.144Asian1.9101.4361.3300.1841.7341.4431.2010.2301.4431.4301.0090.314Black/African American-0.9681.579-0.6130.540-1.1551.589-0.7270.468-1.5061.575-0.9560.340White-1.5351.175-1.3070.192-1.7221.183-1.4560.146-1.9031.171-1.6250.105Hispanic/Latino0.5901.3900.4240.6720.6071.3970.4340.6640.4501.3860.3250.746Year 1-0.8640.572-1.5110.132-0.8600.567-1.5170.130Year 4-0.4720.845-0.5590.577-0.5830.837-0.6970.486First-generation college0.0700.6550.1070.915Physicians in family-1.6660.619-2.6890.007Physician parents-0.3330.815-0.4090.682 Model 4b: Inhibitory IUS B SE t p B SE t p B SE t p Age-0.1620.070-2.3120.021-0.1940.075-2.5900.010-0.1990.075-2.6560.008Gender-0.0620.407-0.1520.879-0.0180.408-0.0440.965-0.0060.409-0.0150.988Asian2.8171.1242.5060.0132.7151.1292.4050.0172.5931.1332.2890.023Black/African American-1.7881.236-1.4460.149-1.8801.243-1.5120.131-2.0341.248-1.6290.104White-1.2690.920-1.3800.168-1.3830.926-1.4940.136-1.4470.928-1.5590.120Hispanic/Latino-0.3061.088-0.2810.7790-0.2321.094-0.2120.832-0.2851.0980-0.2600.795Year 1-0.7010.448-1.5670.118-0.7100.449-1.5810.115Year 4-0.0500.661-0.0750.940-0.0900.663-0.1360.892First-generation college0.1140.5190.2190.826Physicians in family-0.5050.491-1.0300.304Physician parents-0.3350.645-0.5190.604CEI = Curiosity and Exploration Inventory; IUS = Intolerance of Uncertainty Scale; ‡Categorical variables recoded as dummy variables with each variable category = 1 and all other data coded = 0; Gender: men = 1, women = 0a The critical p-value for step 1 of the regression analyses was corrected to 0.00833 using the Bonferroni correction to account for six independent variablesb The critical p-value for step 2 of the regression analyses was corrected to 0.00625 using the Bonferroni correction to account for eight independent variablesc The critical p-value for step 3 of the regression analyses was corrected to 0.00454 using the Bonferroni correction to account for eleven independent variables For the grit subscales, no factors were predictive of consistency in step 3 of the model. However, self-identifying as Hispanic/Latino was a significant predictor of higher perseverance scores in step 3 of the model ($B = 0.189$, $t = 3.225$, $$p \leq 0.001$$).
## General self-efficacy
The mean for general self-efficacy was 32.3 ± 4.7. General self-efficacy scores did not differ by first-generation college status (t= -0.117, $$p \leq 0.907$$), physician relative(s) ($t = 0.581$, $$p \leq 0.561$$), or physician parent(s) ($t = 0.744$, $$p \leq 0.457$$).
The overall regression model was significant (F[11,375] = 2.167, $$p \leq 0.016$$; see Table 4); however, with the Bonferroni corrections none of the first-generation status variables were independent predictors of self-efficacy in the regression models. Further, no other sociodemographic variables predicted self-efficacy.
## Curiosity and exploration
The mean for the curiosity and exploration inventory was 34.2 ± 0.5. The subscale score for exploration was 20.6 ± 3.4, and the subscale score for absorption was 13.5 ± 3.2. Total curiosity and exploration scores did not differ by first-generation college status ($t = 0.185$, $$p \leq 0.853$$), physician relative(s) ($t = 0.656$, $$p \leq 0.512$$), or physician parent(s) (t= -0.289, $$p \leq 0.772$$). Similarly, exploration and absorption scores did not differ by first-generation college status (t= -0.301, $$p \leq 0.763$$; $t = 0.636$, $$p \leq 0.525$$), physician relative(s) ($t = 1.069$, $$p \leq 0.286$$; t= -0.007, $$p \leq 0.995$$), or physician parent(s) (t= -0.297, $$p \leq 0.767$$; t= -0.181, $$p \leq 0.856$$), respectively.
The curiosity and exploration regression model was not statistically significant (F[11,375] = 1.454, $$p \leq 0.147$$; see Table 4). In steps 1, 2 and 3 of the models, no factors independently predicted total curiosity and exploration scores. Similarly, the final models for the exploration subscale (F[11,375] = 1.664, $$p \leq 0.080$$) and absorption subscale (F[11,375] = 0.967, $$p \leq 0.477$$) were not statistically significant and no factors predicted exploration or absorption scores.
## Intolerance of uncertainty
The mean for intolerance of uncertainty was 30.0 ± 8.5. Subscale scores for prospective intolerance of uncertainty were 19.6 ± 5.2 and scores for inhibitory intolerance of uncertainty were 10.4 ± 4.1. Total intolerance of uncertainty scores differed by physician relative(s) (t= -2.830, $$p \leq 0.005$$), but not by first-generation college status ($t = 0.749$, $$p \leq 0.454$$) or physician parent(s) status (t= -1.934, $$p \leq 0.054$$). Further, prospective intolerance of uncertainty scores differed by physician relative(s) (t= -3.379, $p \leq 0.001$) and physician parent(s) (t= -2.077, $$p \leq 0.038$$), but not by first-generation college status ($t = 0.746$, $$p \leq 0.456$$)). Inhibitory intolerance of uncertainty scores did not differ by any of the first-generation statuses. Thus, means scores for total intolerance of uncertainty scores and prospective intolerance of uncertainty scores differed by first-generation medical student status, but not inhibitory intolerance of uncertainty scores.
The overall regression model was statistically significant (F[11,374] = 4.509, $p \leq 0.001$; see Table 4). In step 1 (Bonferroni correction was $p \leq 0.00833$), only age (B= -0.394, t= -2.698, $$p \leq 0.007$$) was an independent predictor of intolerance of uncertainty. In step 2 (Bonferroni correction was $p \leq 0.00625$), age (B= -0.453, t= -2.896, $$p \leq 0.004$$) remained a significant predictor of intolerance of uncertainty. Finally, in step 3 (Bonferroni correction was $p \leq 0.00454$), a statistical trend was observed with students with physician relative(s) predicting lower intolerance of uncertainty scores (B= -2.171, t= -2138, $$p \leq 0.033$$); age remained the only significant predictor of intolerance of uncertainty in the final model.
For the final model predicting prospective intolerance of uncertainty (F[11,374] = 3.806, $p \leq 0.001$; see Table 4), both age (B= -0.271, t= -2.855, $$p \leq 0.005$$) and physician relative(s) status (B= -0.1.666, t= -2.689, $$p \leq 0.007$$) demonstrated statistical trends. No factors predicted inhibitory intolerance of uncertainty in the final regression model.
## Discussion
This descriptive, cross-sectional study measured grit, self-efficacy, curiosity, and intolerance of uncertainty in medical students at a three-campus medical school in the United States. All four constructs are known factors associated with psychosocial well-being [5–8] as well as academic success, perseverance, and productivity [11, 23–25, 32–35]. Grit, self-efficacy, and curiosity and exploration scores did not differ by first-generation college status, physician relative(s), or physician parent(s). However, we observed mean differences between students with physician relative(s) and total intolerance of uncertainty. Further, we observed differences in prospective intolerance of uncertainty by students with physician relative(s) and physician parent(s). These findings indicate that first-generation medical students reported higher total intolerance of uncertainty and higher prospective intolerance of uncertainty, or fear in anticipation of future uncertainty. Next, we examined associations among first-generation statuses predicting grit, self-efficacy, curiosity and exploration, and intolerance of uncertainty, controlling for sociodemographic variables. In the regression models, first-generation college student status and first-generation medical student status did not predict grit, self-efficacy, curiosity and exploration, or intolerance of uncertainty, although statistical trends were observed with students with physician relative(s) predicting lower intolerance of uncertainty scores and lower prospective intolerance of uncertainty. Overall, these findings suggest that higher education’s efforts to level the playing field for first-generation college students are working, and first-generation college students may not be at a disadvantage with respect to these four psychological constructs that contribute to psychosocial well-being and academic success. More research on first-generation medical students is needed before definitive associations with intolerance of uncertainty can be determined.
As hypothesized, no differences were observed by first-generation college and non-first-generation college participants in grit, self-efficacy, curiosity, or intolerance of uncertainty. This finding may be attributed to higher education’s initiatives designed to address first-generation college students’ financial barriers, psychological stressors, lack of social networks, imposter syndrome, and college readiness [36–38]. While this finding cannot be generalized to all medical students, it shows that the participating first-generation college students in this study were not at a disadvantage compared to non-first-generation college students in factors associated with academic success and psychological well-being. This finding may indicate a “turning of the tide” in first-generation college student research. Importantly, the first-generation college students who matriculate to medical school likely represent a distinctive group of first-generation college students who possess more grit, self-efficacy, and curiosity and less intolerance of uncertainty to navigate the rigorous pre-medical programming and medical admission tests. While these findings suggest that certain historical disadvantages between first-generation college students and non-first-generation students are diminishing, additional research with a larger, more diverse population of students is needed before any conclusions can be determined. Further, this unique group should be examined to determine what and how they overcame psychological, academic, financial, and social barriers to enter medical school.
We also examined a newly emerging group of first-generation students that we termed first-generation medical students. To date minimal research has examined differences between medical students with and without physicians in the family. Contrary to our hypothesis, we found no differences in grit, self-efficacy, or curiosity between first-generation medical students and non-first-generation medical students. This may be explained by the associations between those constructs and strong academic performance [11, 23–25, 32–35], which is a prerequisite for admission to medical school [39, 40]. The main difference we observed was in total intolerance of uncertainty and prospective intolerance of uncertainty. Students with physicians in the family reported higher mean total intolerance of uncertainty. Both students with physician parents and with physicians in the family reported higher prospective intolerance of uncertainty. However, in our regression model, neither students with physician parents nor students with physicians in the family predicted intolerance of uncertainty and its subscales, although we observed statistical trends with physicians in the family and intolerance of uncertainty and prospective intolerance of uncertainty.
Our findings showed that both first-generation college student status and first-generation medical student status were not associated with grit, self-efficacy, or curiosity and exploration. In contrast, first-generation medical student status was associated with higher intolerance of uncertainty and higher prospective intolerance of uncertainty, and a statistical trend persisted in the final regression model. Potential explanations for this difference include first-generation medical students may feel uncertain or anxious about their future if they lack a safety net for unexpected difficulties that arise in medical school. This may add a layer of stress and pressure not felt by non-first-generation medical students. Another contributor of increased uncertainty about the future may be a lack of networking in the medical field [41]. Medical school is anxiety-provoking on its own; however, navigating medical school admissions, board examinations, audition rotations, and residency applications without a close physician contact to guide them through the process puts first-generation medical students at a distinct disadvantage. Finally, student load debt may be one more factor that adds uncertainty to a first-generation medical student’s experience more so than a non-first-generation medical student. The uncertainty of moving through life with an exorbitant amount of debt can produce a lot of anxiety [42]. Students whose family members are doctors have the means, if they choose, to help students pay for medical school. This option is less likely for first-generation medical students. Not everyone who starts medical school makes it to graduation. For a first-generation medical student, having this accumulating debt as they move through medical school makes the prospect of dismissal or leaving voluntarily all the more distressing. This uncertainty for the future can manifest in negative ways such as generalized anxiety disorder and obsessive-compulsive disorder [43–46]. Thus, identifying medical students with higher intolerance of uncertainty and prospective intolerance of uncertainty is critical to provide a supportive learning environment that promotes psychological safety [47]. More research is needed to examine psychological, academic, financial, and social barriers and their associations with uncertainty.
Finally, we observed differences in grit, self-efficacy, curiosity, and intolerance of uncertainty by age, gender, race, and year in medical school. The purpose of this study was not to examine differences in these factors by demographic variables; however, we felt it necessary to examine these relationships to create our final regression model. While it is important to control for demographic variables, observed differences do not equate true differences by groups. For example, both gender and race are social constructs that are used to categorize people based on physical characteristics and behavioral patterns; thus, observed differences do not represent a biological or genetic differences. Rather, observed differences in grit, self-efficacy, curiosity, and intolerance of uncertainty by gender and race may suggest systemic discrimination. Assessing the complexity of sexism and racism in medical education and their relationship to grit, self-efficacy, curiosity, and intolerance of uncertainty is beyond the scope of this study. Future research should examine these relationships to ensure that medical education serves the needs of all students.
## Limitations
The current study had several limitations, including the cross-sectional study design, homogeneity of the study sample from one osteopathic medical school with three campuses, selection bias, social desirability bias, and a lack of psychosocial and academic performance measures. The cross-sectional study design prevented determinations of causality among first-generation status and grit, general self-efficacy, curiosity and exploration, and intolerance of uncertainty. Data from one osteopathic medical school from a predominantly White sample limits the ability to generalize the findings to other medical schools. Further, the response rate ($51.5\%$) was moderate, and therefore the findings are susceptible to selection bias. Additionally, participants may have responded to questions in a manner that they believed would be viewed more favorably by others, thus creating social desirability bias. In an attempt to minimize social desirability bias, no researchers were present when the participants completed the survey and participation was anonymous. Moreover, longitudinal research should assess these constructs at multiple time points throughout the year with a larger, more racially and ethnically diverse sample of medical students, from multiple schools in different geographic regions. Future research must also account for the effect of the SARS-CoV-2 pandemic on these factors, especially intolerance of uncertainty. Lastly, we did not assess psychosocial outcomes or current academic performance. Including these measures in future research are needed to strengthen the quality of the study as well as provide a more complete picture of how these constructs influence medical student outcomes.
## Conclusions
These findings suggest that first-generation statuses did not predict grit, self-efficacy, curiosity and exploration, or intolerance of uncertainty. Statistical trends were observed with first-generation medical students predicting higher intolerance of uncertainty and prospective intolerance of uncertainty. Additional research with a larger, more diverse sample assessing these constructs, psychosocial outcomes, and academic performance is needed to draw meaningful conclusions.
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|
---
title: Phenotypical evaluation of lymphocytes and monocytes in patients with type
2 diabetes mellitus in Saudi Arabia
authors:
- Mamdouh Allahyani
- Amani M. Alshalawi
- Maram R. Alshalawii
- Shahad A. Alqorashi
- Abdulelah Aljuaid
- Mazen M. Almehmadi
- Mohammed A. Bokhary
- Alhanouf S. Albrgey
- Ahmad A. Alghamdi
- Abdullah F. Aldairi
- Ayman S. Alhazmi
journal: Saudi Medical Journal
year: 2023
pmcid: PMC10043885
doi: 10.15537/smj.2023.44.3.20220873
license: CC BY 4.0
---
# Phenotypical evaluation of lymphocytes and monocytes in patients with type 2 diabetes mellitus in Saudi Arabia
## Body
Diabetes mellitus (DM) is a common metabolic disorder characterized by the presence of hyperglycemia and it is linked to various complications, including neuropathic, macro-vascular, and micro-vascular disorders. 1 *Diabetes mellitus* is a global concern, given that the number of people with this disease has doubled in the last 3 decades. 2 Worldwide, nearly 415 million people are currently diagnosed with DM, and the number is expected to be approximately 642 million by 2040. 3 *Diabetes mellitus* is a key clinical and public health challenge in the Middle East region, with the prevalence of DM increasing in the Gulf Cooperation Council countries of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. 3 The situation is especially worrying in Saudi Arabia, where it has been claimed that $23.7\%$ of Saudis have DM, with adult males experiencing a higher frequency than females. 1 *Diabetes mellitus* is classified into 2 types; type 1 and 2, in which type 1 DM (T1DM) is a chronic autoimmune condition caused by pancreatic-cell death. 4 In contrast, type 2 DM (T2DM) is a disease of increasingly impaired glucose regulation caused by a combination of malfunctioning pancreatic beta cells and insulin resistance. 5 Type 2 DM is the most prevalent type of DM and has been progressively rising in frequency around the world, particularly in developing nations, accounting for 90-$95\%$ of all DM cases. 1,6 *It is* linked to high rates of morbidity and mortality, which may have an impact on the overall health of patients as well as their quality of lifestyles. 1 Type 2 DM affects people of all ages, including adolescents and young adults, however it is more common in adults. 1 The key factors that contribute to T2DM development are genetics and lifestyle factors, including physical inactivity, a sedentary lifestyle, cigarette smoking, and excessive alcohol use. 7 Research has shown that unhealthful eating habits and lifestyles cause insulin resistance in the body’s cells, which results in the development of T2DM. 1,6 Additionally, over the past 20 years, obesity has been recognized as a significant risk factor for the emergence of T2DM and is linked to worse outcomes. 8 In Saudi Arabia, economic growth has supported the adoption of luxurious lifestyles and, as a consequence, there is a tendency toward less physical activity and more unhealthy dietary habits. 6 These factors have led to the increased prevalence of T2DM in Saudi Arabia, and actions should be taken to reduce the incidence of T2DM. 6
Numerous studies have demonstrated that the immune system contributes to the development of T2DM. 9 Additionally, the higher prevalence of infections in DM patients raises the possibility that DM has an impact on the immune system. 9 Studies have shown that polymorphonuclear cells and monocytes from patients with DM have reduced functions (chemotaxis, phagocytosis, and killing) compared to cells from control groups. 9 Based on CD14 and CD16 expression, monocytes can be classified into 3 subtypes: classical (CD14+/CD16-), intermediate (CD14+/CD16+), and non-classical (CD14-/CD16+). 10 These subtypes differ in their traits and secrete various cytokines. 11 It has been shown that people with T2DM have an altered monocyte phenotypes. 12 Dysfunction of other immune cells, such as natural killer (NK) cells and neutrophils, has also been observed in cases of T2DM. 9 In addition, total T-lymphocytes, CD4+, and CD8+ subsets were found to be associated with the development of T2DM, and that CD4+ and CD8+ T-cells are critical for the progression of DM in mice and humans. 13,14 B-lymphocytes can be classified into many subtypes (based on CD27 and CD38 expression), including mature naive B-cells (CD27-/CD38+), plasmablasts (CD27+/CD38+), and resting memory B-cells (CD27+/CD38-). 15 It should be noted that the few studies that have investigated the levels of B-lymphocyte subsets in patients with T2DM have shown that B-cells are activated in T2DM patients. 16,17 Despite the fact that various studies have explored the phenotype of lymphocytes and monocytes, limited data are available regarding the phenotype of these cells in patients with T2DM in Taif, Saudi Arabia. Thus, the present study was designed to evaluate the levels of B-lymphocytes, T-lymphocytes, and NK cells and their subtypes in patients with T2DM and healthy individuals using flow cytometry. We also aimed to measure and compare the levels of B-lymphocyte subsets (based on CD27 and CD38 expression), T-lymphocyte subsets (CD4 helper and CD8 cytotoxic), and monocyte subsets (based on CD14 and CD16 markers) in patients with T2DM and healthy controls.
## Abstract
### Objectives:
To evaluate the levels of total lymphocytes, B-lymphocytes (CD19+), T-lymphocytes (CD3+), natural killer (NK) cells (CD3-/CD56+), and monocyte subsets in type 2 diabetes mellitus (T2DM) patients in Saudi Arabia. In addition, this study aimed to evaluate whether B- and T-lymphocyte subsets are frequently altered in patients with T2DM.
### Methods:
A case-control study included 95 participants recruited in the study: 62 patients with T2DM and 33 healthy individuals. All the patients were admitted to the Diabetic Centre in Taif, Saudi Arabia. Blood samples were collected between April and August 2022. The hemoglobin A1c (HbA1c) level was evaluated in all patients. Flow cytometry was used to measure the expression of B-lymphocyte, T-lymphocyte, NK cells, and monocyte markers. The unpaired t-test was carried out to evaluate the differences in these markers between T2DM patients and healthy individuals.
### Results:
Patients with T2DM were associated with a lower percentage of total lymphocytes, higher percentage of B-lymphocytes, naive, and memory B subsets. In addition, patients with T2DM showed lower percentage of total T-lymphocytes (CD3+) and CD4 T-cells, but higher CD8 T-cell expression. Also, the NK-cell level was reduced in patients with T2DM, and the levels of monocyte subsets were altered.
### Conclusion:
These data suggested that levels of lymphocytes and monocytes are impaired in T2DM patients, and this might be associated with the higher risk of infections observed in these patients.
## Methods
In this case-control study, a total of 95 participants were recruited: 62 T2DM patients and 33 healthy controls. Patients with a confirmed diagnosis of T2DM were included in the study. Patients with incomplete data regarding age and gender were excluded from the study. The age range of the patients was 45-68 years old. Clinical data were obtained from hospital records. All the patients were admitted to the Diabetic Centre in Taif, Saudi Arabia. Blood samples were collected between April and August 2022. The hemoglobin A1c (HbA1c) level was measured and evaluated according to the guidelines of World Health Organization (WHO) using an automated analyzer (Bio-Rad, New York, NY, USA). Each participant in the study (patients and healthy controls) signed an informed consent form. The study was approved by the Research Ethics Committee at Taif University, Taif, Saudi Arabia (approval number: 43-133). The study was carried out according to the principles of the Helsinki declaration.
Blood samples from the T2DM patients and healthy individuals were collected in ethylenediaminetetraacetic acid (EDTA) tubes and processed within 24-48 hours after collection. By density centrifugation with a Ficoll-Paque gradient, peripheral blood mononuclear cells (PBMCs) were separated from the whole blood samples. For this, each whole blood sample was carefully layered onto one volume of Ficoll-Paque (GE Healthcare, Little, Chalfont, Buckinghamshire, UK) and centrifuged at 2,000 rpm for 20 minutes at 4°C with the brake off. The PBMCs were stained with different fluorophore-labelled antibodies, including CD3 (FITC), CD4 (APC), CD8 (PE), CD19 (FITC), CD27 (PerCp Cy5.5), CD38 (APC), CD56 (PE), CD14 (APC Cy7), and CD16 (FITC). First, cells were washed with phosphate buffer saline (PBS) and incubated with the appropriate antibodies at a 1:100 ratio for 30 minutes in the dark. The cells were then washed with PBS and resuspended in 200 μL of PBS. The samples were analyzed via a BD FACSCanto II system (BD Bioscience, San Jose, CA, USA) and FACSDiva software, version 6 (BD Biosciences). The lymphocytes and monocytes were gated according to the side scatter (SSC) and forward scatter (FSC). The data were analyzed via FlowJo software version 7.10 (Tree Star, Ashland, Oregon, USA).
## Statistical analysis
Data were analyzed as described previously. 18 GraphPad Prism software, version 6.04 (La Jolla, CA, USA) was used to analyze the data. Patients’ data are shown as mean and standard deviation (SD). Differences in the lymphocyte and monocyte surface markers between the T2DM patients and healthy individuals were analyzed via an unpaired t-test or Mann-Whitney test according to the data distribution. Data were considered statistically significant if a p-value of <0.05 was detected.
## Results
The mean age of the patients enrolled in the study was 59±9.1 years. Gender diversity was achieved in this study with $53.2\%$ of the patients being males and $46.8\%$ being females. Table 1 provides a summary of the patients’ characteristics. Each patient’s HbA1c level was measured, and the resultant data shows that the mean glycemic index (HbA1c) was high (8.6±$2.3\%$) among the patients. This indicates that the patients had uncontrolled levels of HbA1c.
**Table 1**
| Parameters | T2DM | Healthy controls |
| --- | --- | --- |
| Age (years), mean±SD | 59±9.1 | 46±5.3 |
| Gender | | |
| MaleFemale | 33 (53.2)29 (46.8) | 19 (57.5)14 (42.5) |
| HbA1c (%), mean±SD | 8.6±2.3 | 5.3±1.4 |
The dot plots shown in Figure 1A demonstrate that the lymphocyte counts of the patients with T2DM were lower than those of the healthy individuals. In addition, the mean percentage of lymphocytes was found to be lower in the patients with T2DM compared with that in the control group, and the difference was statistically significant (p≤0.001, Figure 1B). This indicates that lymphocyte expression was impaired in the T2DM patients.
**Figure 1:** *- Percentage of lymphocytes in T2DM patients and healthy individuals. Lymphocytes were isolated from the peripheral blood of the T2DM patients and healthy individuals based on SSC and FSC using flow cytometry. A) Dot plots showing the percentage of lymphocytes in the samples obtained from healthy individuals and T2DM patients. B) The percentage of lymphocytes in both groups is presented. Data were analyzed using an unpaired t-test. Values are presented as mean±SD. ***
p≤0.001, T2DM: type 2 diabetes mellitus, SSC: side scatter, FSC: forward scatter*
To identify any differences in B-lymphocytes between the T2DM patients and healthy controls, we first examined the total B-lymphocytes (CD19+) using flow cytometry. As shown in Figure 2A, the patients with T2DM had higher expression of CD19+ B-lymphocytes than the healthy controls. The data indicate that the expression of CD19+ B-cells was twice as high in the patients with T2DM ($17.5\%$) as in the healthy controls ($9.1\%$) and that the difference was statistically significant (p≤0.001, Figure 2B). We were also interested in determining whether there were any differences in the levels of B-cell subsets. Subsets of B-lymphocytes were identified based on CD27 and CD38 expression and, as shown in the representative dot plots, there were different frequencies of B-cell subsets detected in the sample from the T2DM patients and the healthy controls (Figure 2C). The percentage of naive B-cells (CD27-/CD38+) was significantly higher in the patients with T2DM compared to that in the healthy controls (p≤0.0001, Figure 2D). Furthermore, the expression of resting memory B-cells (CD27+/CD38-) was significantly higher in the patients with T2DM compared to that in the control group (p≤0.0001, Figure 2E).
**Figure 2:** *- Levels of B-lymphocytes and their subsets in T2DM patients and healthy individuals. Lymphocytes were isolated from the peripheral blood of the T2DM patients and healthy individuals. The cells were then stained with anti-CD19 (FITC), CD27 (PerCyp Cy5.5), and CD38 (APC) and analyzed using flow cytometry. A) Representative dot plots showing the percentage of CD19+ B-cells in the samples obtained from healthy individuals and T2DM patients. B) The percentage of CD19+ cells in both groups is presented. C) Representative dot plots showing the percentage of B-cell subsets in the samples obtained from healthy individuals and T2DM according to CD27 and CD38 expression. D) The percentage of CD27-/CD38+ cells in both groups is presented. E) The percentage of CD27+/CD38- cells in both groups is presented. Data were analyzed using an unpaired t-test. Values are presented as mean±SD. ***
p≤0.001, ****
p≤0.0001, T2DM: type 2 diabetes mellitus*
Initially, the total T-lymphocyte (CD3+) levels were examined using flow cytometry. As shown in Figure 3A, the patients with T2DM had lower expression of CD3+ T-lymphocytes compared to the healthy controls. It was also found that the patients with T2DM had a significantly lower mean percentage of CD3+ T-lymphocytes compared to the healthy controls ($$p \leq 0.012$$, Figure 3B). The percentage of T-lymphocytes was $40\%$ in the T2DM patients and $60\%$ in the healthy controls. After determining that the total T-lymphocyte count was different in the T2DM group compared to that in the healthy group, we examined the expression of T-cell subsets (CD4 and CD8) using flow cytometry. The numbers and proportions of CD4 and CD8 T-cells detected in the samples from the T2DM patients and healthy controls are indicated by the representative dot plots shown in Figure 3C. It was found that CD4 expression was significantly lower in the T2DM patients compared to that in the healthy controls ($$p \leq 0.011$$, Figure 3D). In contrast, CD8 expression was significantly higher in the T2DM patients compared to that in the control group ($$p \leq 0.012$$, Figure 3E).
**Figure 3:** *- Levels of T-lymphocytes and subsets in T2DM patients and healthy individuals. Lymphocytes were isolated from the peripheral blood of T2DM patients and healthy individuals. The cells were then stained with anti-CD3 (APC Cy7), CD4 (APC), and CD8 (PE) and analyzed using flow cytometry. A) Dot plots showing the percentage of CD3+ T-cells in the samples obtained from healthy individuals and T2DM patients. B) The percentage of CD3+ cells in both groups is presented. C) Dot plots showing the percentage of CD4 and CD8 T-cells in the samples obtained from healthy individuals and T2DM patients. D) The percentage of CD4+ cells; and E) CD8+ cells in both groups is presented. Data were analyzed using an unpaired t-test. Values are presented as mean±SD. *
p≤0.05, T2DM: type 2 diabetes mellitus*
We also investigated the levels of NK cells (CD3-/CD56+). As shown in Figure 4A, the T2DM patients had lower levels of NK cells than the healthy controls. This difference was found to be statistically significant ($$p \leq 0.009$$, Figure 4B).
**Figure 4:** *- Levels of NK cells in T2DM patients and healthy individuals. Lymphocytes were isolated from the peripheral blood of the T2DM patients and healthy individuals. The cells were then stained with anti-CD3 (APC Cy7), and CD56 (PE) and analyzed using flow cytometry. A) Dot plots showing the percentage of CD3-/CD56+ NK cells in the samples obtained from healthy individuals and T2DM patients. B) The percentage of NK cells in both groups is presented. Data were analyzed using an unpaired t-test. Values are presented as mean±SD. **
p≤0.01, T2DM: type 2 diabetes mellitus*
We evaluated the levels of monocyte subsets according to the expression of CD14 and CD16. As shown in Figure 5A, the monocyte phenotype varied among the T2DM patients and the healthy controls. The expression of classical monocytes (CD14+/CD16-) was significantly reduced in the T2DM patients compared with that in the control group ($$p \leq 0.0005$$, Figure 5B). However, the expression of intermediate monocytes (CD14+/CD16+) was significantly higher in the T2DM patients than in the control group ($$p \leq 0.011$$, Figure 5C). The patients with T2DM were found to have significantly higher expression of non-classical monocytes (CD14-/CD16+) compared to the control group ($$p \leq 0.0001$$, Figure 5D).
**Figure 5:** *- Expression of monocyte subsets in T2DM patients and healthy individuals. Monocytes were isolated from the peripheral blood of the T2DM patients and healthy individuals. The cells were then stained with anti-CD14 (APC Cy7), and CD16 (FITC) and analyzed using flow cytometry. A) Dot plots showing the percentage of monocyte subsets in the samples obtained from healthy individuals and T2DM patients. B) The percentage of CD14+ cells; C) CD14+/CD16+; and D) CD16+ in both groups are presented. Data were analyzed using an unpaired t-test. Values are presented as mean±SD. *
p≤0.05, ***
p≤0.001. T2DM: type 2 diabetes mellitus*
## Discussion
The present study was designed to evaluate the levels of total lymphocytes, B-lymphocytes (CD19+), T-lymphocytes (CD3+), NK cells, and monocyte subsets in patients with T2DM in Saudi Arabia and compare them to those found in healthy individuals. We also aimed to evaluate whether the levels of B- and T-lymphocyte subsets are frequently altered in patients with T2DM. Alterations in the levels of these important immune cells can be used to predict the severity of disease progression in T2DM patients. 9 First, we have shown that the percentage of total lymphocytes was severely reduced in patients with T2DM compared to healthy individuals. Type 2 DM patients with lower lymphocyte counts have an increased risk of infection, and the ability of immune cells to respond to challenge has been found to be decreased in subjects with T2DM. 9,19 However, another study found no difference in the percentage of lymphocytes in patients with T2DM. 13 *In this* particular study, the patients had normal HbA1c levels, suggesting that patients with controlled levels of HbA1c would have no impact on lymphocyte levels.
Although the percentage of total lymphocytes was lower in our cohort of T2DM patients compared to the healthy controls, the levels of B-lymphocytes were higher in the T2DM patients compared to the healthy controls. In alignment with our results, a previous study showed that the B-lymphocyte level was higher in patients with T2DM, especially those with obesity. 16,17 Regarding the phenotype of B-lymphocyte subsets in patients with T2DM, there are limited reports in the literature on this topic. In the present study, the patients with T2DM displayed different proportions of B-lymphocyte subsets to healthy individuals, with the percentage of naive (CD27-/CD38+) and memory (CD27+/CD38-) B-cells found to be elevated in patients with T2DM. Our findings are consistent with those of previous studies that indicated that the levels of naive and memory B-cells were higher in T2DM patients compared to those in healthy people. 17 It should be noted that different strategies have been implemented to define B-lymphocyte subsets and have failed to provide consistent results. 20 A study in which B-cell subsets were defined solely by CD27 expression found that naive and memory B-cell expression was lower in patients with T2DM. 16 Another study defined B-cell subsets according to CD38 and CD24 expression, and our findings are consistent with the results of that study. 17 It has also been suggested that 10 B-cell subsets can be defined by using a panel of different flow cytometric markers, including CD27, CD24, CD38, CD5, CD10, CD21, and CD22. 21 Our findings indicate that the percentage of total T-lymphocytes (CD3+) was lower in the patients with T2DM compared with the healthy controls. This aligns with another study that reported that T-lymphocytes declined in patients with T2DM. 22 To examine this finding in more depth, we evaluated the expression of helper (CD4+) and cytotoxic (CD8+) T-cells in patients with T2DM. Our data suggest that CD4+ expression was lower and that CD8 expression was higher in the T2DM patients than in the healthy individuals. Inconsistent results have been reported regarding the expression of CD4 and CD8 in patients with T2DM. In one study, no differences in the levels of CD4 and CD8 in patients with T2DM and healthy individuals were observed. 13 The findings of another study suggested that the levels of both CD4 and CD8 were higher in T2DM patients than in healthy individuals. 23 Furthermore, CD4 and CD8 T-cell phenotyping analysis carried out in yet another study showed that there was a significant decrease in both CD4 and CD8 in T2DM patients compared to healthy individuals. 24 Nevertheless, our findings suggest that the T-lymphocyte response is impaired in patients with T2DM.
In this study, NK cell counts in T2DM patients were found to be lower than in healthy controls. Various studies have evaluated the levels of NK cells in patients with T2DM. 25,26 Our findings are consistent with those of a study that reported a reduction in NK-cell expression. 25 In that study, the level of NK cells was found to be associated with the level of HbA1c. Natural killer cells release important cytokines, such as interferon-gamma and tissue necrosis factor-alpha; therefore, a lower level of NK cells might contribute to an increased susceptibility to infection. 25 However, another study found that NK-cell expression was higher in T2DM patients than in healthy controls. 26 Finally, we evaluated the levels of monocytes in the patients with T2DM. The patients with T2DM had lower levels of classical monocytes and higher levels of both intermediate and non-classical monocytes. A previous study also found that the levels of classical monocytes were reduced. 11 Consistent with our findings, a recent systematic review found that the expression of intermediate and non-classical monocytes was higher in T2DM patients. 27 It has been suggested that the levels of non-classical monocytes are elevated during infection and inflammation and are associated with a poor glycemic index in patients with T2DM. 28,29
## Study limitations
First, the sample size was relatively small. Second, the B-lymphocyte subsets were defined according to only 2 surface markers (CD27 and CD38). It is recommended that future studies include a larger B-lymphocyte panel.
In conclusion, we assessed the levels of lymphocytes and monocytes in patients with T2DM in Taif, Saudi Arabia, and found that their phenotypes were altered. Patients with T2DM were found to have lower percentages of total lymphocytes and higher expression of B-lymphocytes and naive and memory B-cell subsets compared to healthy individuals. In addition, CD4+ T-cell expression was lower and CD8+ T-cell expression was higher in the T2DM patients. The levels of NK cells were reduced in the patients with T2DM. Furthermore, the patients with T2DM also had lower levels of classical monocytes and higher levels of both intermediate and non-classical monocytes. These data suggest that the levels of B-lymphocytes, T-lymphocytes, NK cells, and monocyte subsets are impaired in patients with T2DM in Taif, Saudi Arabia, and this might be associated with the higher risk of infections observed in these patients.
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|
---
title: COVID-19 infection during pregnancy
authors:
- Fethiye Akgül
- Can Tüzer
- Yusuf Arslan
- Bünyamin Sevim
journal: Saudi Medical Journal
year: 2023
pmcid: PMC10043889
doi: 10.15537/smj.2023.44.3.20220729
license: CC BY 4.0
---
# COVID-19 infection during pregnancy
## Body
Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) virus was determined as the agent of COVID-19, which rapidly progressed to become a global pandemic. Since the first outbreak in December 2019, it has not been fully clarified who has been most severely affected by this virus. The clinical presentation ranges from asymptomatic disease to multi-organ failure. The elderly and individuals with immunodeficiency are known to be prone to have a more severe disease. 1,2 *Pregnancy is* also an immunosupressive process that makes women susceptible to viral infections. As a result of immunosupression and the substantial changes in the cardiorespiratory system, pregrant women are more vulnerable to a severe disease course during respiratory viral infections. 3 During the H1N1 pandemic in 2009, pregnant subjects comprised $1\%$ of H1N1-infected patients, and $5\%$ of H1N1-infected patients with mortality were reported to be pregnant women. 4 Severe acute respiratory syndrome-related coronavirus-2 and Middle East respiratory syndrome coronavirus infections during pregnancy have been assumed to be responsible for severe clinical outcomes such as admission to intensive care units (ICUs), endotracheal intubation, renal failure, and even death. 5,6 At the beginning of the COVID-19 pandemic, there were concerns regarding pregnant women because of the harmful effects of viral infections such as pneumonia. Nevertheless, pregnant women with COVID-19 infection have not been reported to have experienced more harmful effects of the virus and vertical transmission has not been shown precisely to date. 7,8 More recently, an increase in admissions to ICUs and mortality rates has come to the fore because of new variants of the virus. However, there are currently a limited number of studies on this issue.
The aim of this study was to evaluate the maternal and fetal clinical outcomes in SARS-CoV-2 infected pregnant women, including the differences between patients who became infected in each of the 3 trimesters, from March 2020 until September 2021 in a single province in southeast Turkey.
## Abstract
### Objectives:
To evaluate the maternal and fetal clinical outcomes in SARS-CoV-2 infected pregnant women during the whole period of the pandemic in a single province in the southeast of Turkey.
### Methods:
This retrospective study included patients who were screened from the medical registration system and found to be infected with SARS-CoV-2 virus during pregnancy. The demographic, clinical, laboratory, and radiological features of all the patients were obtained and compared between groups classified as severe-critical and mild-moderate disease severity.
### Results:
The mean age of all the cases was 29.0±5.3 years in the mild-moderate cases, and 30.1±5.5 years in the severe-critical cases. The rates of 3rd trimester, cesarean and premature birth, high body mass index (BMI), symptoms of cough and dyspnea, the presence of comorbidities, and hypothyroidism were significantly higher in the severe-critical cases than in the mild-moderate group. In the univariate analyses, BMI, dyspnea, cough, maternal complication rate, the neutrophil/lymphocyte ratio, the values of white blood cells, procalcitonin, high-sensitive C-reactive protein, D-dimer, ferritin, aspartate aminotransferase, and alanine aminotransferase were detected as significant risk factors. In the multivariate analysis, only procalcitonin was a significant factor.
### Conclusion:
In the 3rd trimester of pregnancy, obesity and hypothyroidism were found to be risk factors for severe-critical cases of COVID-19 infection, and the clinical course was more severe with a higher rate of mortality in the recent period of the pandemic.
## Methods
The patients were screened retrospectively from the medical records in the public health management system and hospital management system, which are both registration systems in Turkey. The study included pregnant women determined with COVID-19 positivity who presented between March 2020 and September 2021. The patients included were those with confirmed COVID-19 positivity during pregnancy, who provided written informed consent for participation in the study via messenger applications on mobile phones. Patients were excluded from the study if they were diagnosed with COVID-19 before or after pregnancy, if they could not be contacted by telephone, or if they did not give informed consent.
The demographic, clinical, laboratory, and radiological features of all the patients were obtained from hospital records. The course of the pregnancy, treatment options, and the health status of the newborns were also obtained from hospital records and assessed in detail. The patients were categorised according to body mass index (BMI) as underweight (<18.5 kg/m 2), normal (18.5-24.9 kg/m 2), pre-obesity (25-29.9 kg/m 2), obesity class I (30-34.9 kg/m 2), obesity class II (35-39.9 kg/m 2), and obesity class III (>40 kg/m 2). 9 All the patients included in the study were assessed according to the clinical findings on first admission to hospital and 2 groups were formed as patients with mild-moderate severity of COVID-19, and patients with severe-critical COVID-19. These 2 groups were compared in respect of the relevant variables examined in the study. Coronavirus disease-19 severity was assessed according to the following criteria: I) mild (no pneumonia); II) moderate (pneumonia present with respiratory symptoms but no accompanying hypoxia; III) severe (respiratory rate of ≥30/min, oxygen saturation of <$94\%$, PaO2/FiO2<300, pulmonary infiltration of >$50\%$ [involvement of >$50\%$ of the total lung parenchyma area as pulmonary infiltration or ground-glass opacities on thorax computed tomography {CT}] and dyspnea; and IV) critical (respiratory failure, septic shock, and multi-organ failure requiring mechanical ventilation). 10 The demographic, clinical and laboratory findings, treatment options, and maternal and neonatal outcomes were compared between the severe-critical and mild-moderate cases. The demographic and clinical outcomes of the pregnant women with confirmed COVID-19 infection in a single province of Turkey during the whole period of the pandemic were evaluated in detail.
Approval for the study was granted by the Ethics Committee of Batman Training and Research Hospital, Batman, Turkey, with decision number 280, on 8th September 2021. The necessary permission for the study was obtained from the Turkish Health Ministry and the Provincial Health Department in Batman, Turkey.
## Statistical analysis
Descriptive statistics were used to evaluate the demographic and clinical characteristics. Quantitative variables not showing normal distribution were analyzed using the Mann-Whitney-U test and in the comparisons of categorical variables, the Chi-square test was applied. Multivariate analysis with binary logistic regression analysis was carried out for the associated factors obtained from univariate analysis with binary logistic regression analysis. The optimal cut-off threshold was determined according to receiver operating characteristic curve (ROC) analysis. All data analyses were carried out with the Statistical Package for the Social Sciences, version 15.0 (SPSS Inc, Chicago, IL, USA). A p-value of<0.05 was considered significant.
## Results
Evaluation was carried out of a total of 291 pregnant women infected with SARS-CoV-2 virus. During the defined period, the total number of COVID-19-positive patients was 58,811, of which 1178 ($2\%$) were pregnant women. The population in Batman, Turkey, is 620,000, and the positivity rate of COVID-19 in the province was estimated to be $9.5\%$ between March 2020 and September 2021. In the same time period, the number of pregnant women was 19,800.
When the distribution of the patients diagnosed with COVID-19 infection was examined by months, the majority were seen in August 2021 (Figure 1). The mean age was determined as 29.0±5.3 years for all the cases, 29.0±5.3 years for the mild-moderate cases, and 30.1±5.5 years for the severe-critical cases ($p \leq 0.05$).
**Figure 1:** *- Distribution of our cases by month.*
A total of 4 pregnant cases developed mortality. The mean age of these cases was 34 years, and all were a singleton pregnancy. The initial symptoms were cough, fatigue, and dyspnea in all 4 subjects. One subject had gestational diabetes mellitus and one hypothyroidism. None of them had received a COVID-19 vaccination. All 4 cases were in the third trimester of pregnancy and the type of delivery was cesarean section. All the newborns were healthy.
Body mass index measurements ($$p \leq 0.014$$), the presence of cough ($p \leq 0.001$), dyspnea ($p \leq 0.001$), pre-existing comorbidities ($$p \leq 0.041$$), and hypothyroidism ($$p \leq 0.041$$) were significantly higher in the severe-critical cases than in the mild-moderate cases (Table 1). The rates of third trimester ($$p \leq 0.013$$), hospitalization ($p \leq 0.001$), mortality ($p \leq 0.001$), chest X-ray ($p \leq 0.001$), and CT screening ($p \leq 0.001$) were significantly higher in the severe-critical cases than in mild-moderate cases (Table 2). The mild-moderate cases did not receive any COVID-19 treatment. In the severe-critical group, treatment was administered of ritonavir-lopinavir (2 patients), favipravir (9 patients), plaquenil (9 patients), systemic steroid (5 patients), immune plasma (one patient), intravenous immunoglobulin (2 patients), and anti-IL6 (tocizulumab) (one patient; Table 2). The rates of cesarean sections ($$p \leq 0.004$$), premature births ($p \leq 0.001$), and COVID-19 testing of newborns with reverse transcriptase-polymerase chain reaction ($$p \leq 0.001$$), length of hospital stay of the newborns ($p \leq 0.001$), and maternal complications (cough, dyspnea, fatigue, headache, muscle pain, loss of taste and smell, and other; $$p \leq 0.0024$$) were also higher in severe-critical patients than in the mild-moderate group.
When the laboratory values were assessed, the values of white blood cells (WBC, $p \leq 0.001$), peripheral blood neutrophils ($$p \leq 0.001$$), high-sensitive C-reactive protein (Hs-CRP, $$p \leq 0.001$$), aspartate aminotransferase (AST, $$p \leq 0.001$$), D-dimer ($$p \leq 0.001$$), ferritin ($p \leq 0.001$), and procalcitonin ($p \leq 0.001$) were found to be higher in the severe-critical cases than in mild-moderate cases (the highest values were assessed).
In the univariate analyses, according to binary logistic regression analysis, BMI ($$p \leq 0.004$$), dyspnea ($p \leq 0.001$), cough ($p \leq 0.001$), maternal complication frequency ($$p \leq 0.001$$), NLR ($p \leq 0.001$), WBC ($$p \leq 0.003$$), procalcitonin ($$p \leq 0.001$$), Hs-CRP ($p \leq 0.001$), D-dimer ($$p \leq 0.029$$), ferritin ($$p \leq 0$$,001), AST ($$p \leq 0.006$$), and alanine aminotransferase (ALT; $$p \leq 0.004$$) were detected as significant risk factors (Table 3). In the multivariate analysis, only procalcitonin was determined to be a significant risk factor ($$p \leq 0.032$$, Table 3).
**Table 3**
| Variables | P-values | OR | 95% CI |
| --- | --- | --- | --- |
| Number of pregnancies | 0.457 | 1.149 | 0.796-1.658 |
| BMI (kg/m2) | 0.004 | 7.143 | 1.842-27.692 |
| First trimester | 0.380 | | |
| Second trimester | 0.997 | 0.000 | 0.000 |
| Third trimester | 0.164 | 4.444 | 0.544-36.325 |
| Dyspnea | <0.001 | 45.714 | 5.573-375.011 |
| Cough | <0.001 | 69.793 | 8.427-578.018 |
| Maternal complication | 0.001 | 24.937 | 3.886-160.026 |
| NLR | <0.001 | 1.169 | 1.081-1.265 |
| WBC (µL) | 0.003 | 1.000 | 1.000-1.001 |
| Procalcitonin(ng/ml) | 0.001 | 49.808 | 4.748-522.534 |
| CRP (mg/dl) | <0.001 | 1.040 | 1.021-1.060 |
| D-dimer (ng/ml) | 0.029 | 1.000 | 1.000-1.001 |
| Ferritin (ng/ml) | 0.001 | 1.018 | 1.007-1.028 |
| AST (U/L) | 0.006 | 1.015 | 1.004-1.026 |
| ALT (U/L) | 0.004 | 1.016 | 1.005-1.026 |
| Multivariate logistic regression analysis forward method | Multivariate logistic regression analysis forward method | Multivariate logistic regression analysis forward method | Multivariate logistic regression analysis forward method |
| Procalcitonin | 0.032 | 15.955 | 1.261-201.927 |
| BMI: body mass index, NLR: neutrophil lymphocyte ratio, WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase, OR: odds ratio | BMI: body mass index, NLR: neutrophil lymphocyte ratio, WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase, OR: odds ratio | BMI: body mass index, NLR: neutrophil lymphocyte ratio, WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase, OR: odds ratio | BMI: body mass index, NLR: neutrophil lymphocyte ratio, WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase, OR: odds ratio |
No statistically significant difference was determined between patients with and without asthma in respect of the rates of severe-critical disease ($$p \leq 1.000$$), hospitalization ($$p \leq 0.485$$), admission to ICU ($$p \leq 1.000$$), and mortality ($$p \leq 1.000$$).
The cases in the third trimester were determined to have higher rates of severe-critical disease ($$p \leq 0.027$$), hospitalization ($$p \leq 0.008$$), and mortality ($$p \leq 0.048$$) than the patients in other trimesters.
The number of previous pregnancies was determined to be positively correlated with the severity of the disease, Hs-CRP, AST, and ferritin, and negatively correlated with peripheral blood eosinophil values. The trimester in which the COVID-19 infection was diagnosed showed a positive correlation with disease severity, WBC, neutrophil, Hs-CRP, D-dimer, and creatinine, and a negative correlation with hemoglobin. The severity of the disease was positively correlated with Hs-CRP, AST, D-dimer, ferritin, and procalcitonin, and negatively correlated with hemoglobin and peripheral blood eosinophil values (Table 4).
**Table 4**
| Variables | Number of pregnancies | Number of pregnancies.1 | In which trimester? | In which trimester?.1 | Disease severity | Disease severity.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | r | P-values | r | P-values | r | P-values |
| Disease severity | 0.140 | 0.017 | 0.171 | 0.003 | | |
| Hemoglobin (g/dl) | -0.048 | 0.592 | -0.213 | 0.016 | -0.221 | 0.012 |
| WBC (µL) | -0.143 | 0.108 | 0.242 | 0.006 | 0.160 | 0.073 |
| Neutrophil (µL) | -0.073 | 0.413 | 0.283 | 0.001 | 0.154 | 0.083 |
| Lymphocyte (µL) | -0.158 | 0.077 | -0.084 | 0.350 | -0.142 | 0.112 |
| CRP (mg/dl) | 0.252 | 0.006 | 0.194 | 0.034 | 0.429 | <0.001 |
| Eosinophil (µL) | -0.215 | 0.015 | 0.016 | 0.863 | -0.192 | 0.032 |
| AST (U/L) | 0.196 | 0.032 | 0.116 | 0.207 | 0.311 | 0.001 |
| ALT (U/L) | 0.148 | 0.104 | 0.002 | 0.985 | 0.138 | 0.131 |
| D-dimer (ng/ml) | 0.161 | 0.204 | 0.494 | <0.001 | 0.489 | <0.001 |
| Ferritin (ng/ml) | 0.232 | 0.039 | 0.123 | 0.281 | 0.429 | <0.001 |
| Creatinine (mg/dl) | 0.010 | 0.916 | 0.187 | 0.042 | 0.127 | 0.167 |
| Procalcitonin (ng/ml) | -0.006 | 0.962 | 0.173 | 0.157 | 0.544 | <0.001 |
| WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase | WBC: white blood cell, CRP: C-reactive protein, AST: aspartate aminotransferase, ALT: alanine aminotransferase |
For severe-critical diseases, the optimal cut-off value of procalcitonin was calculated as 0.425 (AUC=0.965, $95\%$ CI: [0.907-1.022]) with $87.5\%$ sensitivity and $95\%$ specificity. The cut-off value for CRP was calculated as 105.5 (AUC=0.868, $95\%$ CI: [0.675-1.061]) with $87.5\%$ sensitivity, and $95.3\%$ specificity. For D-dimer, the cut-off value was calculated as 2000 (AUC=0.863, $95\%$ CI: [0.688-1.039]) with $75\%$ sensitivity and $79.1\%$ specificity. The cut-off value for ferritin was determined to be 161.5 (AUC=0.860, $95\%$ CI: [0.639-1.082]) with $87.5\%$ sensitivity and $93\%$ specificity, and for NLR, the cut-off value was calculated as 5.79 (AUC=0.811, $95\%$ CI: [0.611-1.011]) with $75\%$ sensitivity and $69.8\%$ specificity (Figure 2).
**Figure 2:** *- The results of receiver operating characteristic curve analysis for C-reactive protein, D-dimer, ferritin, and neutrophil/lymphocyte ratio. ROC: receiver operating characteristic curve, CRP: C-reactive protein, NLR: neutrophil/lymphocyte ratio*
## Discussion
This study with evaluations from a single province in the Southeast of Turkey with a population of 620,00011 contributes significant information to the medical literature. The cases in the third trimester of pregnancy were observed to experience more severe-critical disease than the women in the earlier periods of pregnancy. It was also determined that the severe-critical cases and the cases which resulted in mortality were mostly seen in the last period of the study, and the 4 patients with mortality had not been vaccinated against the disease.
Although more than $90\%$ of pregnant women with COVID-19 infection recover without hospitalization, a rapid clinical deterioration can be seen in some cases. Some studies have reported that symptomatic pregnant patients were at a higher risk of severe disease and mortality compared to symptomatic non-pregnant women of reproductive age. 12,13 In another study, it was reported that pregnant women with COVID-19 positivity were at higher risk of mortality than those without COVID-19, but not at higher risk than non-pregnant women of reproductive age with COVID-19 infection. 14 *In a* study covering the first 5 months of the pandemic, in which 75 hospitalized pregnant patients were evaluated, the admission rate to the ICU was found to be $2.7\%$ and the clinical course of the disease in these patients was similar to that of the general population. 15 *In a* meta-analysis, the mortality rate was $1\%$ in SARS-CoV-2 infected pregnant women. 16 In the present study, covering the first 18 months of the pandemic, the rate of hospitalization was $16.5\%$, the rate of admission to the ICU was $3.1\%$, and the rate of mortality was $1.3\%$. A study from the United States of America reported that $27\%$ of SARS-CoV-2 infected pregnant women had mild disease ($26\%$), severe, and critical disease ($5\%$). 17 In the current study, these rates for pregnant women with confirrmed COVID-19 infection were $84.2\%$ for mild, $12.7\%$ for moderate, $1\%$ for severe, and $2.1\%$ for critical disease, and the high rate of mild disease course was thought to be related to the screening of all patients, including both outpatients and those who were hospitalized.
Age of ≥35 years, obesity, and pre-existing comorbidities (hypertension, diabetes mellitus, and others) have been determined as risk factors for severe COVID-19 disease and mortality. 12,13 In the current study, high BMI values ($$p \leq 0.014$$), the presence of dyspnea ($p \leq 0.001$), cough ($p \leq 0.001$), and comorbidities ($$p \leq 0.041$$) were detected at higher rates in severe-critical cases. Furthermore, a higher rate of hypothyroidism was found in severe-critical cases ($$p \leq 0.041$$) similar to the findings in another study. 18 The clinical effects of asthma on COVID-19 remain unclarified. It has been suggested that an increased vulnerability to the SARS-CoV-2 virus and a more severe disease course are due to the higher risk of exacerbations by viral infections. 19 In another study, it was suggested that accumulated eosinophils and type II inflammation cytokines (IL-4, IL-5, and IL-13) were protective. 19,20 In the present study, no significant differences were detected in the disease severity ($$p \leq 1.000$$), hospitalization ($$p \leq 0.485$$), admission to ICUs ($$p \leq 1.000$$), and mortality ($$p \leq 1.000$$) between the patients with and without asthma.
The findings of this study demonstrated that the values of WBC ($p \leq 0.001$), neutrophil ($$p \leq 0.001$$), Hs-CRP ($$p \leq 0.001$$), AST ($$p \leq 0.001$$), D-dimer ($$p \leq 0.001$$), ferritin ($p \leq 0.001$), and procalcitonin ($p \leq 0.001$) were higher in severe-critical cases than in mild-moderate cases. These findings are not specific to pregnant women but are the same as in the general population with COVID-19 infection. 21 It was also shown in the univariate analysis that obesity, the symptoms of dyspnea and cough, the parameters of NLR, WBC, procalcitonin, Hs-CRP, D-Dimer, ferritin, AST, and ALT were significant risk factors for severe-critical disease.
For pregnant patients, the type and timing of delivery should be decided according to the severity of COVID-19, the presence of comorbidities and obstetric indications. In a study evaluating 230 pregnant patients, the rates of caesarean delivery were higher than vaginal delivery and these were mostly premature births. 22 In another study, a higher rate of caesarean delivery was reported but there was no supporting evidence as the COVID-19 infection mostly did not threaten the maternal health status. 23 *In a* meta-analysis covering 61 studies, 790 SARS-CoV-2 infected pregnant patients and 548 newborns were analysed. The rate of caesarean delivery was $72\%$ and premature births was $23\%$. 24 In the current study, caesarean delivery was determined at the rate of $40.2\%$ and premature births at $17.5\%$. Importantly, cesarean delivery was more common in the severe-critical cases, most of which were diagnosed in the third trimester.
According to the COVID-19 management guidelines of the Turkish Health Ministry, no treatment was recommended for asymptomatic pregnant women. 25 Even if there was no contra-indication for screening lungs with CT, it was also recommended to avoid CT as much as possible. 26 Therefore, the screening rates of CT and X-ray were found to be higher in the severe-critical cases in this study. According to the same guidelines, follow-up without treatment was recommended for non-severe SARS-CoV-2 infected pregnant women. 25 Thus, only the severe-critical cases in this study were treated with appropriate treatment options.
The rate of spontaneous abortion was estimated to be $10\%$ in women aged 25-29 years, and approximately $57\%$ in those aged ≥45 years according to a national prospective cohort study. 27 *In a* recent study from Turkey, it was stated that COVID-19 affected pregnancy and increased the rates of maternal mortality, stillbirth, and abortus. 28 In the current study, 13 ($4.5\%$) pregnancies resulted in spontaneous abortion. Consistent with the findings of some other studies, COVID-19 infection was not seen to increase the risk of spontaneous abortion. 26 A previous review, 18 stated that asymptomatic and mild cases are more commonly seen in the third trimester, whereas another review claimed that the third trimester was the most vulnerable period of the pregnancy for COVID-19 infection. 29 In the present study, 8 ($88.9\%$) of the 9 severe-critical cases were in the third trimester and the remaining one ($11.1\%$) patient was in the first trimester. In addition to a higher rate of severe-critical disease ($$p \leq 0.027$$), higher rates of hospitalization ($$p \leq 0.008$$), and mortality ($$p \leq 0.048$$) were determined in patients in the third trimester than in those at the earlier stages of pregnancy.
Considering that pregnant women are generally under the age of 40 years, these patients had the right to take a COVID-19 vaccine in June 2021 in Turkey. After this date, 102 ($35\%$) pregnant women were diagnosed with COVID-19 infection and only 4 patients had a COVID-19 vaccination. These vaccinated patients were diagnosed in August 2021 and recovered following a mild clinical course. The 3 patients in this study who developed mortality were diagnosed with COVID-19 in August (the recent period) and none had received a COVID-19 vaccination. Therefore, it might be suggested that new variants of the virus showed a deleterious clinical effect.
## Study limitations
First, non-pregnant women could have been recruited into the study as a control group. Second, due to the retrospective design of the study, it was not possible to identify clinical changes in the data in a prospective manner.
In conclusion, the most interesting finding of the study was the positive correlation between the number of pregnancies and the severity of COVID-19. It was also seen that the severity of the disease worsened mostly in the recent period of the pandemic, possibly due to new variants of the virus. Physicians should be aware of the increased risks for pregnant women with obesity, hypothyroidism, or in the third trimester. Considering the clinical outcomes of the vaccinated pregnant women in this study, COVID-19 vaccination seems to be the only option to avoid severe disease.
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|
---
title: CYP2D6 genetic polymorphisms in Saudi systemic lupus erythematosus patients
authors:
- Lena M. Hassen
- Maha H. Daghestani
- Mohammed A. Omair
- Arwa K. Althomali
- Fatimah B. Almukaynizi
- Ibrahim A. Almaghlouth
journal: Saudi Medical Journal
year: 2023
pmcid: PMC10043891
doi: 10.15537/smj.2023.44.3.20220581
license: CC BY 4.0
---
# CYP2D6 genetic polymorphisms in Saudi systemic lupus erythematosus patients
## Body
The cytochrome P450 2D6 (CYP2D6) gene region is extensively polymorphic. There are almost 150 variant alleles, predominantly represented by single nucleotide polymorphism (SNP), identified today and cataloged in the database of the Human Cytochrome P450 Allele Nomenclature (now Pharmacogene Variation Consortium). *These* genetic variations cause phenotypic variations in endogenous and exogenous metabolism leading to large inter-individual variability in metabolic outcomes and drug response. 1 The CYP2D6 polymorphism also exhibits substantial inter-ethnic differences. The worldwide data on CYP2D6 genetic/allelic frequencies revealed that the most frequently observed variant alleles, besides CYP2D6 * 1 (a wild-type allele), were CYP2D6 * 2, * 4, * 10, * 17, and * 41. Some of the highest reported CYP2D6 variants are reported among Asian populations, where the most prevalent allele is CYP2D6 * 10 (decreased-functional allele) in East Asians. In fact, the present trends for CYP2D6 * 10 frequencies pivot between 45-$50\%$, as confirmed by several meta-analysis studies. 2,3 Despite decades of research, the role of CYP in systemic lupus erythematosus (SLE) is still unknown. Some of the earliest postulated hypotheses include the possibility that an unknown substrate metabolized by CYP may trigger autoimmunogens leading to the generation of immune disorders. It is also possible that alternative CYP metabolism of xenobiotics may contribute to multiple chemical sensitivity syndrome, such as drug-induced lupus. The possibility even extends to which perturbed oxidative reactions due to variant CYP involved in metabolizing endogenous substrate, including arachidonic acids associated with oxidative stress, may play a role in the generation of atherosclerosis and cancer, such that seen in some cases of SLE. 4 Based on these assumptions, SLE represents an important context under which CYP2D6 variants may confer heightened risk. However, evidence from the literature shows conflicting results. For example, some studies have reported that the CYP2D6 * 4 (odds ratio [OR]=2.0, $95\%$ confidence interval [CI]=[1.17-3.44]; $$p \leq 0.01$$) and poor metabolizer phenotype (OR=1.78, $95\%$ CI=[1.25-2.53], $$p \leq 0.001$$) are associated with SLE development. 5 Other studies, on the other hand, suggested no correlation between CYP2D6 * 4 gene mutations and SLE. 6-8 We believe that these differences are contingent on ethnic differences.
The evidence of ethnicity is an important factor in pharmacogenetics studies. Additionally, the knowledge of CYP2D6 polymorphism in SLE may help establish a recommendation for considering genetic markers in optimizing care and therapy for lupus patients, particularly in the case of specific organ involvement. Thus, we carried out a study investigating the prevalence of the selected CYP2D6 SNPs (Table 1) in Saudi SLE patients and evaluating the possible correlation between the variant CYP2D6 genotypes with the clinical features of SLE.
**Table 1**
| SNP ID/name | Position reference | SNP region | Genetic variation | Functional consequence | Phenotype prediction * | Global MAF† |
| --- | --- | --- | --- | --- | --- | --- |
| rs1080985 (CYP2D6 * 2) | NC_000022.11:g.42132375G>C | Promotor | 2KB upstream variant | No function | - | Samples = 14,996 G = 0.87 (Ref) C = 0.13 (Alt) |
| rs28624811 (CYP2D6 * 36) | NC_000022.11:g.42131531G>A | Promotor | 2KB upstream variant | No function | - | Samples = 55,678 G = 0.66 (Ref) A = 0.34 (Alt) |
| rs1065852 (CYP2D6 * 4) | NC_000022.11:g.42130692G>A | Exon 1 | Missense variant; splice defect | Decreased function | PM | Samples = 48,628 G = 0.79 (Ref) A = 0.21 (Alt) |
| rs28371725 (CYP2D6 * 41) | NC_000022.11:g.42127803C>T | Intron 6 | Intron variant; splice defect | Decreased function | IM-PM | Samples = 43,504 C = 0.91 (Ref) T = 0.09 (Alt) |
| rs1135840 (CYP2D6 * 10) | NC_000022.11:g.42126611C>G | Exon 9 | Missense variant | Decreased function | IM-PM | Samples = 24,724 C = 0.43 (Ref) G = 0.57 (Alt) |
## Abstract
### Objectives:
To determine the prevalence of selected single nucleotide polymorphisms (rs1080985, rs28624811, rs1065852, rs28371725, and rs1135840) in cytochrome P450 2D6 (CYP2D6) gene among Saudi systemic lupus erythematosus (SLE) patients and to investigate the association between the genetic variants and clinical features of SLE.
### Methods:
This cross-sectional study was carried out on adult Saudi patients at King Khalid University Hospital, Riyadh, Saudi Arabia. Patients with confirmed SLE based on the 2012 Systemic Lupus International Collaborating Clinics classification criteria were included in the study. Peripheral blood was collected for genomic deoxyribonucleic acid extraction and TaqMan® technologies were used for target genotyping. For statistical analysis, differences in genotype frequencies were determined using the Chi-square test, and the association between the variant genotypes and SLE features was evaluated using logistical regression models.
### Results:
There were 107 participants included in this study. Overall, the most predominant ($23.4\%$) recessive genotype was AA in rs28624811, and the least prevalent ($1.9\%$) recessive genotype was TT in rs28371725. Moreover, the variant rs1080985 genotypes (GC or CC) were significantly associated with the presence of serositis manifestation (OR=3.15, $$p \leq 0.03$$), even after adjusting for age and gender. However, the dominant rs28624811 genotype (GG) was associated with renal involvement (OR=2.56, $$p \leq 0.03$$).
### Conclusion:
Systemic lupus erythematosus patients carrying CYP2D6 variants might be considered at risk for certain manifestations of SLE. Further studies are needed to investigate the implication of these genetic variations in clinical outcomes and drug response.
## Methods
This study was a cross-sectional observational investigation. The study participants were recruited from the National Lupus Prospective Cohort at King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia, between March 2020 and March 2021.
Adult (18 years and older) Saudi patients who were diagnosed with SLE at least one year prior to study enrollment and who had met the 2012 Systemic Lupus International Collaborating Clinics classification criteria were included in our study. 9 The sample size for the first objective (prevalence of CYP2D6 SNPs among Saudi SLE patients) was calculated using an online calculator based on the following assumptions: confidence level of $80\%$, error margin of $5\%$, population portion ($18\%$) of CYP2D6 * 41 among population size of approximately 2000 adult Saudis with SLE. 10,11 Moreover, the sample size for the second objective (correlation between CYP2D6 genotypes and SLE features) was calculated using G*Power software (version 3.1.9.7) based on the following parameters: 2-tailed test, alpha error probability of 0.05, power (1 minus beta error probability) of 0.80, and correlation effect for the alternative hypothesis of 0.30 (medium-sized effect size was used based on conventional Cohen’s standard for Chi-square contingency test).
Research ethical approval for this study was acquired from the Institutional Review Board of King Saud University in Riyadh, Saudi Arabia (approval no.: E-19-3955). Written informed consent was obtained from all participants according to the IRB of KSU guidelines. All study procedures were in accordance with the principles of Helsinki Declaration.
Participants’ characteristics were collected from the electronic health records database according to a standardized clinic procedure at KKUH, Riyadh, Saudi Arabia, and the SLE cohort protocol described previously. 12 The collected data included age, gender, and clinical results for SLE.
A 4-mL sample of peripheral blood from each patient was collected into ethylenediaminetetraacetic acid-treated tubes after an overnight fast. The blood samples were immediately centrifuged at 2,000xg for 20 minutes at 10-15oC to obtain a buffy coat sample, then aliquoted appropriately and stored at -80oC until further testing.
The total genomic deoxyribonucleic acid (DNA) was extracted from 200 μL of buffy coat samples using the QIAamp Genomic DNA Blood kit (QIAGEN, Minneapolis, MN, USA) according to the manufacturer’s protocol. The eluted genomic DNA samples were then diluted to 20 ng/μL prior to the genotyping process. The quantity and quality of the extracted genomic DNA was evaluated using a NanoDrop™ 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA).
The TaqMan™ SNP Genotyping kits including TaqMan® SNP Genotyping Master Mix and Assays were purchased from Applied Biosystems (Thermo Fisher Scientific Inc., Waltham, MA, USA). The assays included: C__32407252_30 for rs1080985, C__27102448_30 for rs28624811, C__11484460_40 for rs1065852, C__34816116_20 for rs28371725, and C__27102414_10 for rs1135840.
The working stock per reaction well was prepared at a final volume of 20.00 μL, including 12.00 μL of TaqMan® master mix, 0.60 μL of TaqMan® assay, 6.40 μL of nuclear-free water, and 2.00 μL of gDNA sample. For assay C__27102448_30, the working stock per reaction well was prepared at a final volume of 10.00 μL, including 3.00 μL of TaqMan® master mix, 5.75 μL of TaqMan® assay, 0.25 μL of nuclear-free water, and 1.00 μL of DNA sample.
The polymerase chain reaction (PCR) condition was set for 40 cycles as follows: polymerase activation for 10 minutes at 95oC; dsDNA denaturation for 15 seconds at 95oC; and annealing/extension for 90 seconds at 60oC. For assay C__27102448_30, the PCR condition was set for 50 cycles as follows: polymerase activation for 10 minutes at 95oC; dsDNA denaturation for 15 seconds at 95oC; and annealing/extension for one minute at 60oC. The PCR and fluorescence measurements were carried out using a ViiA 7 Real-Time PCR system (Thermo Fisher Scientific Inc., Waltham, MA, USA) according to the manufacturer’s instructions.
For genotyping error rate checking, 30 samples ($5\%$ for each SNP) were chosen randomly to be re-genotyped blindly following the above method. The allelic differences between genotypes obtained were evaluated for any inconsistencies.
## Statistical analysis
The collected dataset included randomly missing values. Thus, multiple imputations using the Mersenne Twister method were applied. Descriptive analysis was used to describe participants’ general and clinical characteristics. Continuous variables were expressed as mean ± standard deviation (SD), while non-continuous (nominal) variables were expressed as proportion and percentage. The Fisher’s Exact test with mid-P values adjustment was carried out to evaluate the CYP2D6 genotypes deviation from the Hardy-Weinberg equilibrium. Moreover, the crosstabulation method using a Chi-square test of independence (2x2 contingency test) was utilized to examine the relationship between the CYP2D6 genotypes and SLE features. The Bonferroni correction method of Z-test was used to adjust the computed P-values. Furthermore, multiple univariate logistical regression models were employed to analyze the association of the variant CYP2D6 genotypes as predictors (independent variables) for SLE features (dependent variables). For the adjusted models, age and gender were considered covariates. A bootstrap test based on 1000 samples (bias-corrected and accelerated method) was carried out to verify statistically significant findings. All statistical analyses were evaluated with $95\%$ confidence intervals (CI). A p-value of <0.05 was considered significant. The statistical analyses were carried out using the Statistical Package for the Social Sciences, version 27.0 (IBM Corp., Armonk, NY, USA).
## Results
A total of 107 participants with confirmed SLE were included in this study. Table 2 shows the distribution of the selected CYP2D6 SNPs in our study sample. All of the selected CYP2D6 SNPs were consistent with Hardy-Weinberg equilibrium, except for rs1065852 ($p \leq 0.001$). Nevertheless, the genotypes and alleles showed significant genotypic variability, where the frequencies of the least common variants were more than $1\%$.
**Table 2**
| SNPs | Genotypes (N=107) | Genotypes (N=107).1 | Genotypes (N=107).2 | Alleles (N=214) | Alleles (N=214).1 | P-values (for HWE) |
| --- | --- | --- | --- | --- | --- | --- |
| rs1080985 | G/G | G/C | C/C | G | C | 0.90 |
| rs1080985 | 56 (52.3) | 43 (40.2) | 8 (7.5) | 155 (72.4) | 59 (27.6) | 0.90 |
| rs28624811 | G/G | G/A | A/A | G | A | 0.77 |
| rs28624811 | 30 (28.0) | 52 (48.6) | 25 (23.4) | 112 (52.3) | 102 (47.7) | 0.77 |
| rs1065852 | G/G | G/A | A/A | G | A | <0.001 |
| rs1065852 | 80 (74.8) | 14 (13.1) | 13 (12.2) | 174 (81.3) | 40 (18.7) | <0.001 |
| rs28371725 | C/C | C/T | T/T | C | T | 0.86 |
| rs28371725 | 76 (71.0) | 29 (27.1) | 2 (1.9) | 181 (84.6) | 33 (15.4) | 0.86 |
| rs1135840 | G/G | G/C | C/C | G | C | 0.36 |
| rs1135840 | 40 (37.4) | 55 (51.4) | 12 (11.2) | 135 (63.1) | 79 (36.9) | 0.36 |
| Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms | Values are presented as numbers and precentages (%). P-values above 0.05 represents consistency of CYP2D6 genotypes with HWE principle. HWE: Hardy-Weinberg equilibrium, SNPs: single nucleotide polymorphisms |
In our study sample, for rs1080985 (CYP2D6 * 2A), the G allele was identified as dominant and the C allele as a variant. For rs28624811 (CYP2D6 * 36), the G allele was identified as dominant and the A allele as a variant. For rs1065852 (CYP2D6 * 4), the G allele was identified as dominant and the A allele as a variant. For rs28371725 (CYP2D6 * 41), the C allele was identified as dominant and the T allele as a variant. For rs1135840 (CYP2D6 * 10), the G allele was identified as dominant and the C allele as a variant.
Overall, the most predominant variant genotype (AA) was in rs28624811 ($23.4\%$) within the promoter region, and the least prevalent variant genotype (TT) was in rs28371725 ($1.9\%$) within the intronic region of CYP2D6 gene (Table 2).
Table 3 shows the observed distribution of the patient characteristics per CYP2D6 SNP genotypes. Among our study sample, most participants were females ($85\%$) in their reproductive age. Overall, the mean age for our study sample was 37.59±11.3 years. Considering their clinical characteristics, the most common manifestations were immunological disorders ($86\%$), followed by arthritis ($80.4\%$), and cutaneous involvements ($77.6\%$). Furthermore, Table 3 shows the results from the Chi-square test used to analyze the relationship between the CYP2D6 genotypes and SLE features. Results indicated a statistically significant correlation between rs1080985 SNP and serositis manifestation, as well as between rs28624811 SNP and renal involvement ($$p \leq 0.03$$).
**Table 3**
| Variables | CYP2D6 genotypes | CYP2D6 genotypes.1 | CYP2D6 genotypes.2 | CYP2D6 genotypes.3 | CYP2D6 genotypes.4 | CYP2D6 genotypes.5 | CYP2D6 genotypes.6 | CYP2D6 genotypes.7 | CYP2D6 genotypes.8 | CYP2D6 genotypes.9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | rs1080985 | rs1080985 | rs28624811 | rs28624811 | rs1065852 | rs1065852 | rs28371725 | rs28371725 | rs1135840 | rs1135840 |
| | 0 (n=56) | 1 (n=51) | 0 (n=30) | 1 (n=77) | 0 (n=80) | 1 (n=27) | 0 (n=76) | 1 (n=31) | 0 (n=40) | 1 (n=67) |
| Age, mean±SD | 38.5±10.4 | 36.6±12.2 | 37.1±10.1 | 37.8±11.8 | 38.7±11.4 | 34.3±10.8 | 37.6±11.1 | 37.5±12.1 | 35.4±11.1 | 38.9±11.3 |
| P-values | 0.37 | 0.37 | 0.77 | 0.77 | 0.08 | 0.08 | 0.97 | 0.97 | 0.12 | 0.12 |
| Gender (n=91) | 48 | 43 | 26 | 65 | 67 | 24 | 67 | 24 | 34 | 57 |
| P-values | 0.84 | 0.84 | 0.77 | 0.77 | 0.52 | 0.52 | 0.16 | 0.16 | 0.99 | 0.99 |
| Cutaneous (n=83) | 45 | 38 | 25 | 58 | 61 | 22 | 59 | 24 | 31 | 52 |
| P-values | 0.47 | 0.47 | 0.37 | 0.37 | 0.57 | 0.57 | 0.98 | 0.98 | 0.99 | 0.99 |
| Arthritis (n=86) | 44 | 42 | 23 | 63 | 62 | 24 | 61 | 25 | 33 | 53 |
| P-values | 0.62 | 0.62 | 0.55 | 0.55 | 0.20 | 0.20 | 0.96 | 0.96 | 0.67 | 0.67 |
| Serositis (n=20) | 6 | 14 | 4 | 16 | 18 | 2 | 15 | 5 | 8 | 12 |
| P-values | 0.03 | 0.03 | 0.37 | 0.37 | 0.08 | 0.08 | 0.66 | 0.66 | 0.79 | 0.79 |
| Renal (n=50) | 29 | 21 | 19 | 31 | 35 | 15 | 38 | 12 | 20 | 30 |
| P-values | 0.27 | 0.27 | 0.03 | 0.03 | 0.29 | 0.29 | 0.29 | 0.29 | 0.60 | 0.60 |
| Neurological (n=19) | 7 | 12 | 2 | 17 | 16 | 3 | 13 | 6 | 10 | 9 |
| P-values | 0.14 | 0.14 | 0.06 | 0.06 | 0.30 | 0.30 | 0.78 | 0.78 | 0.13 | 0.13 |
| Hematological (n=47) | 23 | 24 | 14 | 33 | 32 | 15 | 34 | 13 | 22 | 25 |
| P-values | 0.53 | 0.53 | 0.72 | 0.72 | 0.16 | 0.16 | 0.79 | 0.79 | 0.07 | 0.07 |
| Immunological (n=92) | 45 | 47 | 24 | 68 | 68 | 24 | 66 | 26 | 36 | 56 |
| P-values | 0.08 | 0.08 | 0.27 | 0.27 | 0.61 | 0.61 | 0.69 | 0.69 | 0.35 | 0.35 |
| Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) | Values are presented as numbers or proportion in a subgroup. P-values (Bonferroni-adjusted) below 0.05 represents a statistically significant relationship (contingency) between CYP2D6 genotypes and participant characteristics. CYP2D6: cytochrome P450 2D6, SD: standard deviation, SNP: single nucleotide polymorphism, CYP2D6 SNP genotype 0: reference variant (homozygous dominant), CYP2D6 SNP genotype 1: alternative variants (heterozygous and homozygous recessive) |
Table 4 shows the results of logistical regression analysis for the association of CYP2D6 polymorphisms as explanatory variables for the different SLE features. We found that the rs1080985 genotypes were significantly associated with the presence of serositis ($$p \leq 0.03$$). The odds of having serositis were 3.15 times higher in those carrying the variant genotype than in those carrying the dominant genotype. Also, we found that the rs28624811 genotypes were associated significantly with renal involvement ($$p \leq 0.03$$). The odds of having renal involvement were 0.39 times higher in those carrying the variant genotype compared to those carrying the dominant genotype, whereas the odds of having renal involvement were much higher in those carrying the dominant genotype compared to those carrying the variant genotype (OR=2.56; $95\%$ CI=[1.07-6.13]; $$p \leq 0.03$$). These associations remained statistically significant even after adjusting for age and gender ($p \leq 0.05$).
**Table 4**
| Variables | Unadjusted model | Unadjusted model.1 | Unadjusted model.2 | Adjusted model* | Adjusted model*.1 | Adjusted model*.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR | 95% CI | P-values | OR | 95% CI | P-values |
| Cutaneous | Cutaneous | Cutaneous | Cutaneous | Cutaneous | Cutaneous | Cutaneous |
| rs1080985 (1) | 0.71 | 0.29-1.78 | 0.47 | 0.65 | 0.25-1.68 | 0.38 |
| rs28624811 (1) | 0.61 | 0.21-1.82 | 0.38 | 0.63 | 0.21-1.95 | 0.43 |
| rs1065852 (1) | 1.37 | 0.46-4.11 | 0.57 | 1.08 | 0.34-3.40 | 0.90 |
| rs28371725 (1) | 0.99 | 0.36-2.69 | 0.98 | 1.08 | 0.38-3.08 | 0.89 |
| rs1135840 (1) | 1.01 | 0.39-2.57 | 0.99 | 1.20 | 0.45-3.20 | 0.72 |
| Arthritis | Arthritis | Arthritis | Arthritis | Arthritis | Arthritis | Arthritis |
| rs1080985 (1) | 1.27 | 0.49-3.33 | 0.62 | 1.29 | 0.47-3.51 | 0.62 |
| rs28624811 (1) | 1.37 | 0.49-3.82 | 0.55 | 1.48 | 0.51-4.31 | 0.47 |
| rs1065852 (1) | 2.32 | 0.63-8.61 | 0.21 | 2.09 | 0.54-8.11 | 0.28 |
| rs28371725 (1) | 1.02 | 0.36-2.94 | 0.96 | 1.27 | 0.42-3.91 | 0.67 |
| rs1135840 (1) | 0.80 | 0.29-2.20 | 0.67 | 0.83 | 0.29-2.36 | 0.72 |
| Serositis | Serositis | Serositis | Serositis | Serositis | Serositis | Serositis |
| rs1080985 (1) | 3.15 | 1.11-8.98 | 0.03 | 3.01 | 1.05-8.66 | 0.04 |
| rs28624811 (1) | 1.70 | 0.52-5.59 | 0.38 | 1.71 | 0.52-5.66 | 0.38 |
| rs1065852 (1) | 0.28 | 0.06-1.28 | 0.10 | 0.24 | 0.05-1.13 | 0.07 |
| rs28371725 (1) | 0.78 | 0.26-2.38 | 0.66 | 0.73 | 0.23-2.27 | 0.58 |
| rs1135840 (1) | 0.87 | 0.32-2.36 | 0.79 | 0.95 | 0.35-2.63 | 0.93 |
| Renal | Renal | Renal | Renal | Renal | Renal | Renal |
| rs1080985 (1) | 0.65 | 0.3-1.40 | 0.27 | 0.59 | 0.27-1.30 | 0.19 |
| rs28624811 (1) | 0.39 | 0.16-0.93 | 0.03 | 0.39 | 0.16-0.94 | 0.04 |
| rs1065852 (1) | 1.61 | 0.67-3.87 | 0.29 | 1.39 | 0.56-3.43 | 0.48 |
| rs28371725 (1) | 0.63 | 0.27-1.48 | 0.29 | 0.62 | 0.26-1.50 | 0.29 |
| rs1135840 (1) | 0.81 | 0.37-1.78 | 0.60 | 0.92 | 0.41-2.06 | 0.83 |
| Neurological | Neurological | Neurological | Neurological | Neurological | Neurological | Neurological |
| rs1080985 (1) | 2.15 | 0.77-5.99 | 0.14 | 2.11 | 0.75–5.91 | 0.16 |
| rs28624811 (1)) | 3.97 | 0.86-18.36 | 0.08 | 4.13 | 0.89–19.24 | 0.07 |
| rs1065852 (1 | 0.50 | 0.13-1.87 | 0.30 | 0.43 | 0.11–1.66 | 0.22 |
| rs28371725 (1) | 1.16 | 0.40-3.40 | 0.78 | 1.22 | 0.41–3.63 | 0.72 |
| rs1135840 (1) | 0.47 | 0.17-1.27 | 0.13 | 0.49 | 0.18–1.35 | 0.17 |
| Hematological | Hematological | Hematological | Hematological | Hematological | Hematological | Hematological |
| rs1080985 (1) | 1.28 | 0.59-2.74 | 0.53 | 1.18 | 0.54-2.60 | 0.67 |
| rs28624811 (1) | 0.86 | 0.37-2.00 | 0.72 | 0.87 | 0.37-2.07 | 0.75 |
| rs1065852 (1) | 1.87 | 0.78-4.53 | 0.16 | 1.63 | 0.66-4.03 | 0.29 |
| rs28371725 (1) | 0.89 | 0.38-2.08 | 0.79 | 0.88 | 0.37-2.12 | 0.78 |
| rs1135840 (1) | 0.49 | 0.22-1.08 | 0.08 | 0.54 | 0.24-1.22 | 0.14 |
| Immunological | Immunological | Immunological | Immunological | Immunological | Immunological | Immunological |
| rs1080985 (1) | 2.87 | 0.85-9.68 | 0.09 | 3.00 | 0.87-10.31 | 0.08 |
| rs28624811 (1) | 1.89 | 0.61-5.86 | 0.27 | 1.96 | 0.62-6.19 | 0.25 |
| rs1065852 (1) | 1.41 | 0.37-5.44 | 0.62 | 1.34 | 0.34-5.30 | 0.68 |
| rs28371725 (1) | 0.79 | 0.25-2.53 | 0.69 | 0.88 | 0.27-2.89 | 0.83 |
| rs1135840 (1) | 0.57 | 0.17-1.91 | 0.36 | 0.56 | 0.16-1.92 | 0.36 |
It is worth mentioning that there were statistically trending risk associations between the CYP2D6 SNPs in the promoter region and SLE clinical features. For instance, we found that carrying the variant rs28624811 genotype could possibly be associated with neurological manifestation (OR=3.97; $95\%$ CI=[0.86-18.36]; $$p \leq 0.08$$). Also, we found that carrying the variant rs1080985 genotype could possibly be associated with immunological manifestation (OR=2.87; $95\%$ CI=[0.85-9.68]; $$p \leq 0.09$$). However, these associations remained insignificant even after adjusting for age and gender ($p \leq 0.05$).
## Discussion
The present study provides a new perspective on lupus-associated genetic loci. Herein, we selected 5 SNPs in different loci on the CYP2D6 gene. Through genotyping, we identified the prevalence of the selected SNPs among our study sample. Overall, we found that CYP2D6 SNP rs28624811 (CYP2D6 * 36) within the promoter gene region was prevalent in Saudis with SLE. Also, we found a possible correlation between the CYP2D6 polymorphisms and SLE manifestations. Perhaps the generation of deficient CYP enzymes leading to disturbed endogenous and exogenous metabolism might be responsible for determining the trend of manifestations in SLE. 13 The overall CYP2D6 allelic frequencies in our study were incongruent with the general population reported previously. 2,3 For instance, throughout the Middle Eastern populations, CYP2D6 * 2 ($22\%$) and CYP2D6 * 41 ($20\%$) were the most common alleles, while CYP2D6 * 4 ($8\%$) and CYP2D6 * 10 ($6\%$) were the least prevalent alleles. We found that CYP2D6 * 10 ($37\%$) and CYP2D6 * 2 ($28\%$) were more common alleles, while CYP2D6 * 4 ($19\%$) and CYP2D6 * 41 ($15\%$) were less prevalent alleles. Our findings indicated that functionally significant variants are prevalent among Saudi SLE patients, suggesting that most of them are predicted to have inactive or decreased activity. A similar metabolic deviation was also evident in other ethnic groups, such as Jewish populations. 2 *This is* presumably due to the high consanguinity (high inbreeding coefficient) leading to interethnic haplotype variability. 14 However, a fair representation of these populations remains insufficient to draw such presumptions.
Among all the Middle Eastern populations, for instance, the highest prevalence ($32\%$) of CYP2D6 * 2 was among Iranian and Eastern Azerbaijan populations. 15 Similarly, high frequencies were reported in Turkey and Syria. 16,17 Meanwhile, the lowest prevalence ($9\%$) was reported among nomadic groups in the Israel/Palestine region. 18 Likewise, low frequencies (<$16\%$) were reported in Saudi Arabia, United Arab Emirates, and Ethiopia. 19-21 Our results of CYP2D6 * 2 for SLE patients were higher than other local studies. 19,20 Furthermore, we demonstrated the prevalence of a new SNP (rs28624811) located in the promoter region of the CYP2D6 gene. Interestingly, results showed that the variant allele CYP2D6 * 36 ($48\%$) was frequently found among our study sample compared to the other selected CYP2D6 SNPs. We speculate that the occurrence of this variant within the core regulatory region of the CYP2D6 promoter might affect the transcription factor binding, which alters the promoter activity in gene transcription, mRNA stability, and translation. Subsequently, this may alter the enzyme level responsible for endogenous and exogenous metabolism, potentially contributing to different clinical outcomes.
Furthermore, our results from the regression analysis showed a statistically significant association between CYP2D6 * 2 and serositis. The CYP2D6 has been identified in smooth tissue membranes. 22,23 However, its interaction with the bilayer lipid membrane is yet to be explored. We speculate that CYP2D6 might exhibit similar functional characteristics to cytochrome P450 2J2 (CYP2J2) found in cardiovascular tissues. The CYP2J2 is involved in the metabolism of arachidonic acid to epoxyeicosatrienoic acids, which are anti-migratory, anti-proliferative, and anti-inflammatory responses in endothelial cells. 24 Thus, genetic polymorphism may have functional consequences leading to an increased risk of heart disease. In fact, some studies on the Saudi population have reported that hypertensive patients showed a significantly higher frequency of CYP2J2 * 7 and CYP2D6 * 10 compared to patients with normal blood pressure. 25 Additionally, we found a statistically significant association between CYP2D6 * 36 and renal manifestations. In a case study, Leung et al 26 reported on 2 patients with acute renal damage that suffered multiple drug allergies/intolerances. 27 In both cases, the renal injury was believed to be related to increased drug exposure leading to nephrotoxicity due to poor metabolizer of CYP polymorphism. Other studies also demonstrated that polymorphic CYPs might promote renal cancer development by downregulating the death-associated protein kinase-1. 28,29 Thus, further investigation of SLE needs to account for CYP polymorphisms for individualized medicine practices. 30 Taken together, the genetic polymorphisms within the promoter region of the CYP2D6 gene could be potential predictors for certain SLE features. However, further research on a large population must confirm these results.
The trends for CYP2D6 * 4 frequencies in the Middle East pivot between 3-$12\%$, as confirmed by meta-analysis studies. 2,31 Our observed frequency of CYP2D6 * 4 among our study sample was above the previously reported data. However, recent data from regional studies did not support our findings. For instance, approximately $28\%$ (on average) of Egyptians were reported to carry the recessive allele of CYP2D6 * 4. 32-34 Moreover, Alkreathy et al 19 found that $100\%$ of their Saudis subjects had the CYP2D6 * 4 allele. The authors of these studies did not explain their divergent results, though methodology differences may be important to consider.
Our advent finding that the genotypes at rs1065852 deviated from the Hardy-*Weinberg equilibrium* also should not be neglected. Although a re-genotyping experiment was carried out to confirm our results, there were zero missing calls or allelic differences between the repeated samples. In fact, the overall genotyping quality for SNP rs1065852 was $98.2\%$. Additionally, population stratification may explain the deviation. We found that genotyping results for individuals with presumably African ancestral backgrounds drove the genotypic distribution towards equilibrium ($$p \leq 0.05$$) but not for the remaining individuals. This finding may support the existence of unique genetic profiles for individuals with Arab ancestral backgrounds. However, other unknown confounding factors might have remained unaccounted for within the scope of our study.
Another interesting finding was the identification of CYP2D6 * 41. In a recent meta-analysis review, the CYP2D6 * 41 allele was relatively more common throughout the Middle East compared to other regions worldwide. 3 The highest percentages were seen among Palestinians ($29\%$), followed by Saudi Arabians ($18\%$). 10,18 Our findings of CYP2D6 * 41 contradicted these studies, which was the least frequent variant in our study sample. This may support the existence of a unique genetic profile of Saudi SLE, especially considering the homogeneity of our sample population (namely, subjects with similar diagnoses and ethnic backgrounds). However, further research on a large population is needed to confirm these results.
The overall percentage of CYP2D6 * 10 found among our study sample was higher than the pooled data from Middle Eastern populations. 2,3 However, we found that the pattern of the C allele to the G allele among our study sample was similar to that of the global data reported in the Allele Frequency Aggregator project database at the National Center for Biotechnology Information (release version: 20201027095038). Thus, it should not be excluded that other mutations (such as CYP2D6 * 4 or CYP2D6 * 41) could be in linkage disequilibrium with CYP2D6 * 10 and play a role in the phenotypic effect. 35 Intriguingly, we detected that SLE patients carrying the dominant rs1135840 genotype (GG) had slightly higher neurological (OR=2.15; $95\%$ CI=[0.79-5.86]; $$p \leq 0.14$$) and hematological (OR=2.05; $95\%$ CI=[0.93-4.55]; $$p \leq 0.08$$) involvement compared to those carrying the variant genotype (Table 4). The role of CYP2D6 in the brain varies from the metabolism of endogenous compounds, such as dopamine and serotonin, to interactions with numerous drugs that are of potential clinical importance for neurological and cognitive disorders. 36,37 Moreover, recent studies showed the association of CYP2D6 * 10 with an increased risk of hepatological and hematological toxicity as a result of drug-related adverse reactions. 38 Interestingly, a recent study investigating the CYP2D6 * 10 in SLE patients showed that the GG genotype had decreased activity. 39 Similarly, Lee et al 40 found that carriers of the GG genotypes in rs1134840 had elevated levels of norclozapine. We speculate that the alleles of importance may vary between populations (namely, population-dependent), which needs to be considered in clinical research or patient care.
## Study limitations
First, not all factors have been taken into account systematically. Nevertheless, genetic variants are far less susceptive to confounding bias than most non-genetic measures. Thus, our results merely provide support for a possible link between CYP2D6 genotypes and SLE criteria. Second, the Saudi population has unique variations compared with other Arab ethnicities in the region. Our subject recruitment method from the National Lupus Cohort offered a fair representation of the population. Though, the racial admixture and high consanguinity of the Saudi population should not be neglected in genetic studies. Third, the recruitment of our study sample was restricted by the COVID-19 pandemic; thus, the small sample size might have limited power. To circumvent the potential bias stemming from false discovery, we carried out an a priori power inquiry to determine a reasonable sample size, then carried out a post-hoc power analysis to confirm the conferred statistical significance. Nevertheless, the results of this investigation should be interpreted with caution. We intend to carry out the experiment using the whole-gene sequencing experiment on a larger longitudinal SLE cohort to confirm the study findings. 12 In conclusion, we identified a common CYP2D6 polymorphism in Saudi SLE patients. This finding may support the existence of unique genetic profiles for SLE patients with Saudi ancestral backgrounds. Also, we found several CYP2D6 SNPs possibly related to the different clinical features of SLE. This a priori knowledge might be a useful reference in potentially determining genetic factors of SLE-related dysfunctions and may help understand the likely pathophysiological link between the SLE manifestations. *Future* genetics studies on this topic may lead to alternative disease intervention strategies for these patients.
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|
---
title: The interaction between no folic acid supplementation during early pregnancy
and preeclampsia increased the risk of preterm birth
authors:
- Yi-Jie Zhang
- Hong Jiang
- Chengqiu Lu
- Yi Sun
- Shudong Cui
- Chao Chen
journal: Saudi Medical Journal
year: 2023
pmcid: PMC10043892
doi: 10.15537/smj.2023.44.3.20220695
license: CC BY 4.0
---
# The interaction between no folic acid supplementation during early pregnancy and preeclampsia increased the risk of preterm birth
## Body
Preterm birth (PTB) is defined as birth before 37 weeks of gestational age and represents a major cause of death in newborns and children under 5 years of age. 1-3 Based on clinical intervention, preterm births can be divided into iatrogenic preterm births and spontaneous PTBs. 1 The etiology of PTB is thought to be multifactorial and is still largely unknown. Many studies have demonstrated that periconceptional folate acid (FA) supplementation could decrease the risk of PTB. 4-7 *Preeclampsia is* also strongly associated with PTB. 1,8,9 However, we do not know if the risk of preterm birth would increase even more if a pregnant woman lacked FA supplementation during the first 12 weeks of pregnancy and then went on to develop preeclampsia. There are very few studies relating to the interaction between no folic acid (FA) supplementation in early period of pregnancy and preeclampsia on PTB.
An interaction is defined as the combined effect caused by 2 or more exposure factors at the same time that is not equal to the sum of the individual effects. When the former is greater than the latter, it is defined as a positive interaction, thus indicating that the effect is enhanced when 2 or more factors co-exist at the same time; this means biological synergy. A synergy index (S) >1 indicates that there is a positive additive interaction between 2 exposure factors. Attributable interaction refers to the amount of effect caused by the interaction between 2 factors. Excess relative risk of interaction effects (RERI) is used to describe the relative effect size caused by attributive interaction. The larger the RERI is, the stronger the interaction between factors is. 10 In the current study, we hypothesized that there is a positive additive interaction between a lack of FA supplementation during the early period of pregnancy and preeclampsia which increase the risk of PTB. The findings of this study provide new insights into our understanding of FA intake in early pregnancy and how this strategy might reduce the risk of PTB for women developing preeclampsia.
## Abstract
### Objectives:
To explore if there is a positive additive interaction between no folic acid (FA) supplementation in early period of pregnancy and preeclampsia which increases the risk of preterm birth (PTB).
### Methods:
We matched 1471 women who had live-birth singleton preterm infants with 1471 women who had live-birth singleton term infants at 15 Chinese hospitals in 2018. We excluded women who took folic acid less than 0.4 mg/d or less than 12 weeks in early stage, women with gestational hypertension, chronic hypertension, or preeclampsia during previous pregnancy. We calculate odds ratios for PTB by performing conditional logistic regression comparing preterm group with term group.
We quantified the interaction between 2 exposures by synergy (S) and relative excess risk due to interaction (RERI).
### Results:
Approximately $40\%$ of preterm cases did not take FA in early pregnancy. After adjusting confounding factors by logistic regression, when the 2 exposures (no early FA supplementation and preeclampsia) co-existed, the risk of all PTB increased significantly (aOR11=12.138; $95\%$ CI 5.726-25.73), the interaction between 2 exposures was positive ($S = 1.27$) and increased 2.385-fold risk of all PTB (RERI=2.385); and there were similar results on iatrogenic PTB (aOR11=23.412; $95\%$ CI 8.882–60.71, $S = 1.18$, RERI=3.347).
### Conclusion:
Our multicenter study showed, for the first time, that there was a positive additive interaction between no FA supplementation in early pregnancy and preeclampsia which increased the risk of all PTB, especially iatrogenic PTB.
## Methods
A retrospective multicenter 1:1 matched case-control study was carried out. The data used in the current study were extracted from a ‘preterm risk factor study’ database. 11 The ‘Preterm risk factor study was a case-control study carried out between January 2018 and December 2018 in 15 hospitals across China. The study included 15 hospitals: 4 county-level hospitals, 6 prefecture-level hospitals and 5 provincial hospitals from North-Western, Eastern, South-Central, and regions of China. Children’s Hospital of Fudan University served as the research center and was responsible for data coordination and the integration of information. The study was approved by the Research Ethics Committee of Children’s Hospital of Fudan University ([2018] no.: 84).
Selected cases were defined as mothers who gave birth to a live-born singleton preterm newborn (24-36 weeks). Controls were defined as mothers who delivered a live-born singleton term newborn (37-41 weeks and birth weight between 2500-4000 g). An eligible control was matched to a case by delivery date (within 2 days), delivery site (same hospital), and the newborn’s gender. The exclusion criteria were as follows: i) pregnant women who did not have a complete set of information relating to preeclampsia and the supplementation of folic acid; ii) pregnant women who took <0.4 mg/day of folic acid or <12 weeks during early pregnancy; iii) pregnant women who had chronic hypertension, gestational hypertension or preeclampsia during previous pregnancy, and iv) pregnant women who refused to participate.
The determination of gestational age was based on calculating the last menstrual period (irregular menstruation combined with ultrasonic examination). Maternal diseases were diagnosed by referring to the 9th Edition of “Obstetrics and Gynecology” (edited by Xie et al). 12 *Diabetes mellitus* during pregnancy and preeclampsia were diagnosed according to national guidelines. 13,14 Smoking included active smoking and passive smoking defined according to World Health Organization (WHO) definitions. 15 Pre-pregnancy body mass index (BMI) was divided into 2 groups (<24 kg/m 2 and ≥24 kg/m 2) according to Chinese standards. 16 We recorded a range of information relating to maternal and fetal characteristics, including maternal age, parity, maternal education, race, residence in pregnancy, family income, supplementation of folic acid (dose and duration), preeclampsia, supplementation of FA before pregnancy (dose and duration), smoking in pregnancy, previous preterm birth, diabetes mellitus in pregnancy, chronic renal disease, hypothyroidism, placenta problems, pre-pregnancy BMI, assisted reproduction, prenatal examination, and family history of hypertension.
This study is a 1:1 group case-control study. The parameters were set according to the odds ratio (OR) value of previous studies and the incidence of the control group, α=0.05, β=0.20, using pass 15.0 software to calculate the main risk factors. The sample size of preeclampsia (OR=9, $1.5\%$ in the control group) was 26 cases. The sample size of FA supplementation (OR=1.3, $70\%$ in the control group) was 1086 cases.
## Statistical analyses
We used Epidata version 3.4 (The EpiData Association, Odense, Denmark) to establish the database, perform logical consistency checks, and verify data. Stata version 15.0 (StataCorp, College Station, TX, USA) statistical software was used for statistical processing. Univariate analyses included the Chi-squared test, 2-sample independent t-tests, and the rank-sum test. A P-P plot was used to test normality. We used conditional multivariate logistic regression analysis to estimate the combined and individual adjusted odds ratios (aORs) of 2 exposures (no FA supplementation and preeclampsia) on the risk of PTB and stratified to estimate combined and individual adjusted odds ratios (aORs) of the 2 exposures on the risk of iatrogenic PTB and spontaneous PTB. Confounding factors were controlled for. $P \leq 0.05$ was significant. The synergy index (S), relative excess risk due to interaction (RERI), and proportion attributable to interaction (AP) were calculated to estimate the interaction between 2 exposures. 10 We used the combined and individual adjusted OR (aOR) values to calculate S, RERI, and AP. 10 *Missing data* was deleted.
## Results
Figure 1 shows a flowchart showing how the participants were recruited. A total of 2774 preterm cases were recruited initially. Of these, 41 cases did not agree to participate in the survey, 52 cases were missing data relating to folic acid supplementation, 1130 cases did not take 0.4 - 0.8 mg/day of folic acid for 12 weeks during early pregnancy, 80 cases had chronic hypertension, gestational hypertension or preeclampsia during previous pregnancy; therefore, these cases were all excluded. Consequently, there was a total of 1471 preterm cases in the case group; $58.3\%$ (857 cases) delivered male infants. In total, 655 cases of preterm birth presented with iatrogenic preterm birth (iPTB) and 816 cases of preterm birth presented with spontaneous preterm birth (sPTB). Overall, the control group included 1471 term controls, who met the inclusion and exclusion criteria; finally, $58.3\%$ (857 cases) delivered male infants.
**Figure 1:** *- Flowchart showing the recruitment process for participants.*
Table 1 shows the demographic characteristics of the 2 groups. The mean age of all participants was 30.20±5.20 years. During pregnancy, 398 ($13.5\%$) participants resided in the countryside, 1062 ($36.1\%$) participants in small towns, and 1482 ($50.4\%$) participants in cities. There were 787 ($26.7\%$) participants with an education level of middle school or below, 688 ($23.4\%$) of high school, and 1467 ($49.9\%$) of university or above. The 2 groups showed significant differences ($p \leq 0.05$) in terms of maternal age, parity, race, residence during pregnancy, maternal education, and family income monthly per person.
**Table 1**
| Characteristics | Total n=2942 | Preterm n=1471 | Term n=1471 | t/P |
| --- | --- | --- | --- | --- |
| Maternal age * | 30.20±5.202 | 30.50±5.589 | 29.89±4.765 | <0.001 |
| <20 | 56 (1.9) | 40 (2.7) | 16 (1.1) | <0.001 |
| 20-34 | 2252 (76.5) | 1075 (73.1) | 1177 (80.0) | |
| ≥35 | 634 (21.6) | 356 (24.2) | 278 (18.9) | |
| Parity | Parity | Parity | Parity | |
| Unipara | 1448 (49.2) | 658 (44.7) | 790 (53.7) | <0.001 |
| Multipara | 1494 (50.8) | 813 (55.3) | 681 (46.3) | |
| Race | Race | Race | Race | |
| Han | 2777 (94.4) | 1373 (93.3) | 1404 (95.4) | 0.013 |
| Others | 165 (5.6) | 98 (6.7) | 67 (4.6) | |
| Maternal Education | Maternal Education | Maternal Education | Maternal Education | |
| Middle school or below | 787 (26.7) | 467 (31.7) | 320 (21.8) | <0.001 |
| High school | 688 (23.4) | 377 (5.6) | 311 (21.1) | |
| University or above | 1467 (49.9) | 627 (42.6) | 840 (57.1) | |
| Residence during pregnancy | Residence during pregnancy | Residence during pregnancy | Residence during pregnancy | |
| City | 1482 (50.4) | 650 (44.2) | 832 (56.6) | <0.001 |
| Small town | 1062 (36.1) | 568 (38.6) | 494 (33.6) | |
| Countryside | 398 (13.5) | 253 (17.2) | 145 (9.8) | |
| Family income monthly per person (yuan) | Family income monthly per person (yuan) | Family income monthly per person (yuan) | Family income monthly per person (yuan) | |
| <2,000 | 224 (7.6) | 141 (9.6) | 83 (5.6) | <0.001 |
| 2,000-10,000 | 2115 (71.9) | 1053 (71.6) | 1062 (72.2) | |
| >10,000 | 603 (20.5) | 277 (18.8) | 326 (22.2) | |
We carried out univariate analyses to compare the rate of prenatal diseases, FA supplementation, maternal age, and pre-pregnancy BMI between preterm and term groups (Table 2). In total, $1.5\%$ of women in the term group had preeclampsia; this compared to $13.5\%$ of women in the preterm group. In total, $31.0\%$ of women in the term group did not take folic acid supplementation in the first trimester; this compared to $40.0\%$ of women in the preterm group.
**Table 2**
| Characteristics | Total n=2942 | Preterm n=1471 | Term n=1471 | P-value |
| --- | --- | --- | --- | --- |
| Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy | |
| No | 1045 (35.5) | 589 (40.0) | 1456 (31.0) | <0.001 |
| Full | 1897 (64.5) | 882 (60.0) | 1015 (69.0) | |
| Preeclampsia | Preeclampsia | Preeclampsia | Preeclampsia | |
| No | 2721 (92.5) | 1272 (86.5) | 21449 (98.5) | <0.001 |
| Yes | 221 (7.5) | 199 (13.5) | 122 (1.5) | |
| Smoking in pregnancy | Smoking in pregnancy | Smoking in pregnancy | Smoking in pregnancy | |
| No | 2758 (93.7) | 1354 (92.0) | 1404 (95.4) | <0.001 |
| Yes | 184 (6.3) | 117 (8.0) | 67 (4.6) | |
| Previous preterm birth | Previous preterm birth | Previous preterm birth | Previous preterm birth | |
| No | 2830 (96.2) | 1377 (93.6) | 1453 (98.8) | <0.001 |
| Yes | 112 (3.8) | 94 (6.4) | 18 (1.2) | |
| Diabetes mellitus in pregnancy | Diabetes mellitus in pregnancy | Diabetes mellitus in pregnancy | Diabetes mellitus in pregnancy | |
| No | 2438 (82.9) | 1176 (79.9) | 1262 (85.8) | <0.001 |
| Yes | 504 (17.1) | 295 (20.1) | 209 (14.2) | |
| Chronic renal disease | Chronic renal disease | Chronic renal disease | Chronic renal disease | |
| No | 2940 (99.9) | 1469 (99.9) | 1471 (100) | 0.157 |
| Yes | 2 (0.1) | 2 (0.1) | 0 (0) | |
| Hypothyroidism | Hypothyroidism | Hypothyroidism | Hypothyroidism | |
| No | 2790 (94.8) | 1398 (95.0) | 1392 (94.6) | 0.617 |
| Yes | 152 (5.2) | 73 (5.0) | 79 (5.4) | |
| Placenta problems | Placenta problems | Placenta problems | Placenta problems | |
| No | 2569 (88.2) | 1194 (81.2) | 1402 (95.3) | <0.001 |
| Yes | 346 (11.8) | 277 (18.8) | 69 (4.7) | |
| Pre-pregnancy BMI (kg/m2) | Pre-pregnancy BMI (kg/m2) | Pre-pregnancy BMI (kg/m2) | Pre-pregnancy BMI (kg/m2) | |
| <24 | 2396 (81.4) | 1178 (80.1) | 1218 (82.8) | 0.058 |
| ≥24 | 546 (18.6) | 293 (19.9) | 253 (17.2) | |
| Assisted reproduction | Assisted reproduction | Assisted reproduction | Assisted reproduction | |
| No | 2744 (93.3) | 1363 (92.7) | 1381 (93.9) | 0.185 |
| Yes | 198 (6.7) | 108 (7.3) | 90 (6.1) | |
| Prenatal examination | Prenatal examination | Prenatal examination | Prenatal examination | |
| No | 216 (7.3) | 97 (6.6) | 119 (8.1) | 0.120 |
| Yes | 2726 (92.7) | 1374 (93.4) | 1352 (91.9) | |
| Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy | |
| No | 1592 (54.1) | 859 (58.4) | 733 (49.8) | <0.001 |
| Partial | 501 (17.0) | 229 (15.6) | 272 (18.5) | |
| Full | 849 (28.9) | 383 (26.0) | 466 (31.7) | |
| Family history of hypertension | Family history of hypertension | Family history of hypertension | Family history of hypertension | |
| No | 2864 (97.3) | 1427 (97.0) | 1437 (97.9) | 0.251 |
| Yes | 78 (2.7) | 44 (3.0) | 34 (2.3) | |
| Values are presented as numbers and percentages (%). PTB: preterm birth, FA: folic acid, BMI: body mass index | Values are presented as numbers and percentages (%). PTB: preterm birth, FA: folic acid, BMI: body mass index | Values are presented as numbers and percentages (%). PTB: preterm birth, FA: folic acid, BMI: body mass index | Values are presented as numbers and percentages (%). PTB: preterm birth, FA: folic acid, BMI: body mass index | Values are presented as numbers and percentages (%). PTB: preterm birth, FA: folic acid, BMI: body mass index |
The results arising from multivariate analysis are shown in Table 3; this included all statistically significant factors identified by univariate analyses, along with pre-pregnancy BMI and assisted reproduction, comparing all preterm births with term births. After adjusting for confounding factors preeclampsia (aOR=9.684, $95\%$ confidence interval [CI]: [5.967-15.716]) were significantly associated with all preterm birth. Using full FA supplementation as a reference, the results demonstrated that no FA supplementation significantly increased the risk of all PTB (aOR=1.351, $95\%$ CI: [1.073-1.701]), after adjusting for confounding factors.
**Table 3**
| Variable | aOR Value | 95% CI | P-value |
| --- | --- | --- | --- |
| Maternal age | Maternal age | Maternal age | |
| <20 | 1.039 | 0.835-1.293 | 0.732 |
| 20-34 | 1.000 | | |
| ≥35 | 2.136* | 1.101-4.142 | 0.025 |
| Parity | Parity | Parity | |
| Multipara | 1.230* | 1.039-1.507 | 0.030 |
| Race | Race | Race | |
| Han | 1.000 | | |
| Others | 1.387 | 0.902-2.133 | 0.160 |
| Maternal education | Maternal education | Maternal education | |
| Middle school or below | 1.459* | 1.122-1.898 | 0.005 |
| High school | 1.407* | 1.126-1.759 | 0.003 |
| University or above | 1.000 | | |
| Residence during pregnancy | Residence during pregnancy | Residence during pregnancy | |
| City | 1.000 | | |
| Small town | 1.352* | 1.094-1.670 | 0.005 |
| countryside | 1.686* | 1.212-2.346 | 0.002 |
| Family income monthly per person (yuan) | Family income monthly per person (yuan) | Family income monthly per person (yuan) | Family income monthly per person (yuan) |
| <2,000 | 1.037 | 0.680-1.582 | 0.725 |
| 2,000-10,000 | 0.996 | 0.966-1.220 | 0.770 |
| >10,000 | 1.000 | | |
| Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy | Supplementation of FA in early pregnancy |
| No | 1.351* | 1.073-1.701 | 0.011 |
| Full | 1.000 | | |
| Preeclampsia | Preeclampsia | Preeclampsia | |
| Yes | 9.684* | 5.967-15.716 | <0.001 |
| Smoking in pregnancy | Smoking in pregnancy | Smoking in pregnancy | |
| Yes | 1.691* | 1.168-2.447 | 0.007 |
| Previous preterm birth | Previous preterm birth | Previous preterm birth | |
| Yes | 4.566* | 2.620-7.959 | <0.001 |
| diabetes mellitus in pregnancy | diabetes mellitus in pregnancy | diabetes mellitus in pregnancy | |
| Yes | 1.431* | 1.146-1.786 | 0.002 |
| Placenta problems | Placenta problems | Placenta problems | |
| Yes | 11.634* | 6.661-20.320 | <0.001 |
| Pre-pregnancy BMI (kg/m2) | Pre-pregnancy BMI (kg/m2) | Pre-pregnancy BMI (kg/m2) | |
| ≥24 | 0.870 | 0.699-1.084 | 0.204 |
| Assisted reproduction | Assisted reproduction | Assisted reproduction | |
| Yes | 1.235 | 0.878-1.740 | 0.226 |
| Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy | Supplementation of folic acid before pregnancy |
| No | 1.068 | 0.927-1.231 | 0.360 |
| Partial | 0.987 | 0.837-1.160 | 0.880 |
| Full | 1.000 | | |
There was a total of 1471 preterm cases in the preterm group; 655 cases of preterm birth presented with iPTB and 816 cases of preterm birth presented with spontaneous preterm birth (sPTB).
Table 4 showed the results of individual and joint effects of no FA supplementation and preeclampsia on all PTB, subgroups (iPTB, sPTB), and measures of interaction.
**Table 4**
| Variable | Case/control number | aOR value | 95% CI | P-value | RERI | AP | S |
| --- | --- | --- | --- | --- | --- | --- | --- |
| All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | All preterm birth (n=1471)/control group (n=1471) | |
| None OR00 | 784/1001 | 1.000 | | | | | |
| No FA supplementation OR01 | 488/448 | 1.290* | 1.026-1.623 | <0.001 | | | |
| Preeclampsia OR10 | 98/14 | 9.463 | 5.090-17.60 | 0.027 | | | |
| No FA supplementation + preeclampsia OR11 | 101/8 | 12.138* | 5.726-25.73 | <0.001 | 2.385 | 0.19 | 1.27 |
| Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) | Iatrogenic preterm birth (n=655)/paired control group (n=655) |
| None OR00 | 304/452 | 1.000 | | | | | |
| No FA supplementation OR01 | 165/190 | 1.385 | 0.938-2.047 | 0.180 | | | |
| Preeclampsia OR10 | 91/8 | 19.680* | 8.462-45.77 | <0.001 | | | |
| No FA supplementation + preeclampsia OR11 | 95/5 | 23.412* | 8.882-60.71 | <0.001 | 3.347 | 0.14 | 1.18 |
| Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) | Spontaneous preterm birth (n=816)/paired control group (n=816) |
| None OR00 | 480/549 | 1.000 | | | | | |
| No FA supplementationOR01 | 323/258 | 1.353* | 1.009-1.814 | 0.043 | | | |
| Preeclampsia OR10 | 7/6 | 1.140 | 0.317-4.106 | 0.841 | | | |
| No FA supplementation + preeclampsia OR11 | 6/3 | 2.056 | 0.471-8.984 | 0.338 | - | - | - |
After adjusting confounding factors conditional multivariate analysis revealed that no early FA supplementation (aOR01=1.290, $95\%$ CI: [1.026-1.623]), preeclampsia (aOR10=9.463, $95\%$ CI: [5.090-17.60]) was significantly associated with all preterm birth. When the 2 exposures (no early FA supplementation and preeclampsia) existed, the risk of all PTB increased significantly (aOR11=12.138; $95\%$ CI: [5.726-25.73]). The interaction between 2 exposures was positive ($S = 1.27$>1) and increased 2.385-fold risk of all PTB (RERI=2.385).
A total of 655 iatrogenic preterm births were compared with their paired control group. After adjusting confounding factors conditional multivariate analysis revealed that preeclampsia (aOR10=19.680, $95\%$ CI: [8.462-45.77]) was significantly associated with iatrogenic preterm birth. When the 2 exposures (no early FA supplementation and preeclampsia) existed, the risk of iatrogenic PTB increased significantly (aOR11=23.412; $95\%$ CI: [8.882-60.71]). The interaction between the 2 exposures was positive ($S = 1.18$>1) and increased 3.347-fold risk of iatrogenic PTB (RERI=3.347).
A total of 816 spontaneous preterm births were compared with the paired control group. Following the adjustment of confounding factors, no early FA supplementation was significantly associated with spontaneous PTB (aOR01=1.353, $95\%$ CI: [1.009-1.814]). However, preeclampsia was not significantly associated with spontaneous PTB (aOR11=1.140, $95\%$ CI: [0.317-4.106], $$p \leq 0.841$$). The combined effect of no early FA supplementation and preeclampsia was not significantly associated with spontaneous PTB (aOR11=2.056, $95\%$ CI: [0.471-8.984], $$p \leq 0.338$$).
## Discussion
The current study found that there was a positive additive interaction between no early FA supplementation and preeclampsia which increased the risk of all PTB (including iatrogenic PTB and spontaneous PTB) and iatrogenic PTB. The interaction between the 2 exposures increased 2-fold risk of all PTB (RERI=2.385) and 3-fold risk of iatrogenic PTB (RERI=3.347). Based on a review of the published literature, our study is the first to report the positive interactive effects between no early FA supplementation and preeclampsia which increased the risk of PTB. The possible mechanisms underlying this positive interaction could be partially explained by the fact that women who did not take FA may have high levels of Hct, a condition that is associated with hypertension. 17,18 Our study may be representative of China because of the large sample size and the coverage of various development areas with different levels of economic development (East, middle, and West). Because this was a matched case-control study, we adjusted the confounding factors for preterm birth, such as the gender of the baby, season, environmental factors, maternal age, parity, residence, maternal education, smoking in pregnancy, previous preterm birth, diabetes mellitus in pregnancy, placenta problems, pre-pregnancy BMI, assisted reproduction, and supplementation of folic acid before pregnancy; consequently, our results are stable.
In agreement with other previous reports, our data confirmed that preeclampsia was significantly associated with PTB and iatrogenic PTB. 9,19 The incidence of hypertensive pregnancy disorders reported in previous literature from China was between $2\%$ to $12\%$. 20,21 The incidence of preeclampsia in this study was $7.5\%$ and was therefore relatively high.
We noted that a lack of FA supplementation in early period of pregnancy led to a significant increase in the risk of spontaneous PTB after adjusting for confounding factors. Similar to our findings, Shao et al 22 also reported that the possibility of spontaneous PTB without premature rupture of membranes decreased by $17\%$ (OR=0.83, $95\%$ CI: [0.70-0.97]) in mothers who took folic acid supplements for more than 3 months before pregnancy and throughout pregnancy. This study showed that there were still $35.5\%$ of mothers who did not take FA supplements in early period of pregnancy. Considering the increased risk of iatrogenic PTB for women who suffered preeclampsia, and the increased risk for spontaneous PTB, the policy for folic acid supplementation during early pregnancy should be strongly recommended in China.
## Study limitations
We are also aware that our current study has some limitations that need to be considered. This was a retrospective case-control study; consequently, it is difficult to infer causality. Therefore, a prospective cohort study is needed in the future. In addition, we did not test the folic acid levels in the plasma of the mothers included in this study. Although the confounding factors were controlled, residual confounding factors could not be completely excluded.
In conclusion, our study suggested that there was a positive additive interaction between a lack of folic acid supplementation in early pregnant period and preeclampsia which increased the risk of all preterm birth, especially the risk of iatrogenic PTB. A lack of FA supplementation in early pregnant period increased the risk of spontaneous PTB. This highlighted the importance of FA supplementation in early pregnancy with regard to reducing the risk of PTB in pregnant women who developed preeclampsia and spontaneous PTB.
## References
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|
---
title: Evaluation of clinical and laboratory characteristics and factors affecting
mortality in 500 hospitalized COVID-19 patients
authors:
- Petek Ş. Konya
- Neşe Demirtürk
- Derya Korkmaz
- Havva Tünay
- Elif Betül Koşar
journal: Saudi Medical Journal
year: 2022
pmcid: PMC10043905
doi: 10.15537/smj.2022.43.11.20220641
license: CC BY 4.0
---
# Evaluation of clinical and laboratory characteristics and factors affecting mortality in 500 hospitalized COVID-19 patients
## Body
A wide clinical range, from asymptomatic outpatients to critically unwell patients needing intensive care unit follow-up, is seen in COVID-19 disease caused by SARS-CoV-2. Risk factors for a severe course have been identified since the pandemic’s start. As a result, it is especially important to monitor individuals at risk of developing a serious illness and dying. To lower the mortality rate, it is critical to identify high-risk individuals as soon as possible. 1 *As a* result, the purpose of this study was to establish the factors affecting mortality and to retrospectively assess and diagnose the clinical and laboratory characteristics of COVID-19 patients admitted to our hospital.
## Abstract
### Objectives:
To evaluate the clinical and laboratory characteristics of COVID-19 patients admitted to Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey, and to determine the factors affecting mortality.
### Methods:
A total of 500 patients who were diagnosed with COVID-19 between 19th of March and 30th of September 2020 in Afyonkarahisar Health Sciences University, Faculty of Medicine, Pandemic Service, Afyonkarahisar, Turkey, were retrospectively investigated for this study. These individuals’ prognoses, demographic, clinical, laboratory, and radiological information were examined and recorded retrospectively. Comparisons were carried out between the characteristics of patients with a prognosis of death and those who recovered.
### Results:
Of the 500 definite COVID-19 cases included in the study, $53.8\%$ were male and the mean age was 57.6±15.1 (18-88 years). The most common comorbidities were hypertension and diabetes mellitus. A total of 45 ($9\%$) patients developed mortality. Factors such as advanced age, male gender, shortness of breath, fever at admission, comorbid conditions such as hypertension, diabetes mellitus, cardiovascular diseases, lymphopenia, high C-reactive protein, high D-dimer, and high ferritin in the laboratory were found to be important risk factors for mortality. Treatments such as hydroxychloroquine, favipiravir, and lopinavir/ritonavir were not found to have lower mortality rates than one another.
### Conclusion:
Considering these elements when assessing patients and adjusting the course of treatment according to the recommendations of the most recent guidelines may lower mortality.
## Methods
A total of 500 individuals tested positive for SARS-CoV-2 and were subsequently given a conclusive COVID-19 diagnosis at the Pandemic Service of the Faculty of Medicine, Afyonkarahisar, Turkey. Between March 19 and September 30, 2020, were retrospectively investigated for this study. The definitive diagnosis was made by using RT-PCR to identify the SARS-CoV-2 virus in oropharyngeal and nasopharyngeal swabs. Analyses were carried out on the complete blood count, liver and kidney tests, lactate dehydrogenase (LDH), D-dimer, ferritin, and C-reactive protein (CRP). A thoracic computed tomography (CT) was carried out in all patients except for pregnant patients. These individuals’ prognoses, demographic, clinical, laboratory, and radiological information were examined and recorded retrospectively. Comparisons were carried out between the traits of patients whose prognosis were death and those whose prognosis was the cure. Patient’s files contained demographic information on the patients, while the hospital database had information on laboratory results and radiological images.
Radiological findings included bilateral ground glass opacities, predominantly peripherally located multilobar and subsegmental consolidations, linear opacities, “cobblestone” appearance, and “inverted halo sign.” Other findings such as pleural fluid, cavitation, and lymphadenopathy were defined as atypical involvement. 2 In our study, radiological findings were classified as typical and atypical.
The guidelines of the Ministry of Health’s COVID-19 Adult Patient Therapy Guide were followed when determining the need for hospital follow-up and the antiviral treatment options and dosages. Transferred to the critical care unit were patients with a respiratory rate >30/minute, progression of dyspnea, and SpO2<$90\%$ in room air despite therapy. 3 The study excluded patients admitted to the intensive care unit, pediatric patients, and patients who were COVID-19 probable cases but whose SARS-CoV-2 PCR test came out negative.
The Ethics Committee of Afyonkarahisar Health Sciences University, Faculty of Medicine, Pandemic Service, Afyonkarahisar, Turkey and the Ministry of Health of Turkey approved this study (date: August 21, 2020, No.: $\frac{2020}{360}$).
## Statistical analysis
The Statistical Package for the Social Sciences, version 22.0 (IBM Corp., Armonk, NY, USA) package program was carried out for statistical analysis. Descriptive statistical methods (mean, standard deviation [SD], median, frequency, percentage, minimum, and maximum) were carried out to evaluate the study data. The Shapiro-Wilk test evaluated the conformity of quantitative data to normal distribution. Pearson Chi-square test and Fisher-Freeman-Halton test were caried out to compare qualitative data. A p-value of <0.05 was considered significant.
## Results
In this study, 500 patients with a positive SARS-CoV-2 PCR test and a definitive diagnosis of COVID-19 between March 19 and September 30, 2020, were examined. A total of 45 ($9\%$) patients developed mortality. It was determined that 455 patients were discharged with healing, 23 patients resulted in mortality after transfer to intensive care, and 22 were mortal during their follow-up in the ward.
Of the 500 definite COVID-19 cases included in the study, 231 ($46.2\%$) were female, 269 ($53.8\%$) were male, and the mean age was 57.6±15.1 (18-88 years). When the distribution of 45 mortal patients according to gender was analyzed, it was found that there were 38 male and 7 female patients, and mortality was significantly higher in the male gender (p≤0.001). The mean age of the patients who resulted in mortality was 72.2 (57-94 years). In patients who did not develop mortality, the mean age was 61.3 years, and age was considered an important risk factor for mortality.
Cough was the most common presenting symptom in 293 ($58.6\%$) patients. A part from this, myalgia was observed in 166 ($33.2\%$) patients, dyspnea in 148 ($29.5\%$) patients, fever in 120 ($24\%$) patients, and sore throat in 76 ($15.2\%$) patients, and headache in 81 ($16.1\%$) patients. Patients with dyspnea (p≤0.001) and fever ($$p \leq 0.05$$) on admission had a significantly higher mortality rate than other patients. The distribution of the patient’s symptoms is shown in Table 1.
**Table 1**
| Symptoms | Total | Mortality (n=45) | Non-mortality (n=455) | P-values |
| --- | --- | --- | --- | --- |
| Cough | 293 (58.6) | 35 (77.0) | 258 (56.7) | 0.16 |
| Myalgia | 166 (33.2) | 10 (22.2) | 156 (34.2) | 0.55 |
| Shortness of breath | 148 (29.5) | 32 (71.1) | 116 (25.4) | 0.001 |
| Fever | 120 (24.0) | 20 (44.4) | 100 (21.9) | 0.05 |
| Headache | 81 (16.1) | 4 (8.8) | 77 (16.9) | 0.19 |
| Sore throat | 76 (15.2) | 2 (4.4) | 74 (16.2) | 0.64 |
| Nausea and vomiting | 71 (14.1) | 7 (15.5) | 64 (14.0) | 0.67 |
| Diarrhea | 50 (10.0) | 5 (11.1) | 45 (9.8) | 0.19 |
The most common comorbidities were hypertension ($35.2\%$), followed by diabetes mellitus ($22\%$), chronic obstructive pulmonary disease (COPD; $13.4\%$), cardiovascular diseases ($5.8\%$), and renal failure ($2.6\%$). A total of 13 ($2.8\%$) of the patients were pregnant at presentation. When the mortality and non-mortality groups were compared in terms of comorbidities, significant differences were found for hypertension ($62.2\%$-$32\%$), diabetes mellitus ($42.2\%$-$20\%$), cardiovascular diseases ($22.0\%$-$4.1\%$) (p≤0.001, $$p \leq 0.05$$), but not for COPD ($$p \leq 0.67$$) and renal failure ($$p \leq 0.13$$).
When the laboratory parameters of the patients were analyzed, leucopenia was found in 68 ($13.6\%$) patients and leucocytosis in 45 ($9\%$) patients. Lymphopenia was found in 103 ($20.5\%$) patients, while lymphocyte count was normal in 397 ($79.5\%$) patients. Thrombocytopenia was found in 120 ($24\%$) patients, and D-dimer level was above 1000 µg/l in 81 ($16.2\%$) patients. Lactate dehydrogenase levels were high in 253 ($50.6\%$) patients, and CRP level was above 100 mg/L in 132 ($26.5\%$) patients. When ferritin values were analyzed, 99 ($19.7\%$) patients had values above 500 mg/L. When the neutrophil/lymphocyte ratio was analyzed, it was found to be above 3.2 in 300 ($60\%$). The mortality rate of patients with lymphopenia (p≤0.001), elevated CRP ($$p \leq 0.05$$), elevated D-dimer ($$p \leq 0.05$$), and ferritin ($$p \leq 0.006$$) on admission were significantly higher than the other patients. While leukocyte count, neutrophil/lymphocyte ratio, platelet count, and LDH level did not differ significantly between the mortal and non-mortal groups. The laboratory parameters of the patients are shown in Table 2.
**Table 2**
| Findings | Total | Mortality (n=45) | Non-mortality (n=455) | P-values |
| --- | --- | --- | --- | --- |
| Lymphopenia (<800)/ | 103 (20.5) | 27 (60.0) | 76 (16.7) | 0.001 |
| Ferritin >500mcg/L | 99 (19.7) | 21 (46.6) | 78 (17.1) | 0.05 |
| D-dimer >1000 ng/ml | 81 (16.2) | 17 (37.7) | 64 (14.0) | 0.05 |
| CRP >100mg/L | 132 (26.5) | 32 (71.1) | 100 (21.0) | 0.006 |
| Thrombocytopenia (<150.000/mm3) | 120 (24.0) | 15 (33.3) | 105 (23.0) | 0.55 |
| PNL/L >3.2 | 300 (60.0) | 35 (77.8) | 265 (58.2) | 0.67 |
| LDH > 250 IU/ml | 253 (50.6) | 33 (73.3) | 220 (48.3) | 0.19 |
| Leukocytosis (>10.000) | 45 (9.0) | 15 (33.3) | 30 (6.5) | 0.16 |
| Leucopenia | 85 (17.0) | 5 (11.1) | 80 (17.5) | 0.13 |
Thorax CT was carried out in 454 ($90.8\%$) patients, and thorax CT was found normal in 55 ($11\%$) patients. Typical involvement in COVID was found in 360 ($72\%$) patients, while atypical involvement was found in 41 ($8.2\%$) patients. Of the patients with a mortal course, 7 ($15.5\%$) had atypical involvement on thorax CT, and 38 ($84.4\%$) had typical involvement. The mortality rate of patients with typical involvement was significantly higher than those with atypical involvement (p≤0.001).
Antiviral treatments were given to the patients following the guidelines of the Ministry of Health, Turkey. When the antiviral drugs used were analyzed, it was determined that 132 ($26.4\%$) patients treated with hydroxychloroquine (HCQ), 314 ($62.8\%$) patients received favipiravir, and 41 ($8.2\%$) patients treated with HCQ + favipiravir, and 13 ($2.6\%$) patients treated with lopinavir/ritonavir. When the antiviral treatments given to the patients with the mortal course were analyzed, $9\%$ of the patients who treated with HCQ, $7.3\%$ of the patients who treated with HCQ + favipiravir, and $9.5\%$ of the patients who treated with favipiravir resulted in mortality, and statistically, no significant difference was found between the antiviral treatments received by the patients with the mortal outcome ($$p \leq 0.16$$). Mortality rates according to treatment options are shown in Table 3.
**Table 3**
| Variables | Hydroxychloroquine | Hydroxychloroquine + favipiravir | Favipiravir | Lopinavir/ritonavir | P-value |
| --- | --- | --- | --- | --- | --- |
| Mortality | 12 (9.0) | 3 (7.3) | 30 (9.5) | 0 (0) | 0.16 |
| Non-mortality | 120 (91.0) | 38 (92.7) | 284 (90.5) | 13 (100) | 0.16 |
## Discussion
In our study, factors such as advanced age, male gender, shortness of breath, and fever at admission, comorbid conditions such as hypertension, diabetes mellitus, cardiovascular diseases, lymphopenia, high CRP, high D-dimer, and high ferritin in the laboratory were found to be important risk factors for mortality.
In our investigation, it was discovered that older people and men had greater death rates. The mortality rate was around 5 times higher in men than in women, and patients who died on average were 72 years old. In a research published in the literature, it was discovered that age, gender, and men’s characteristics were risk factors for mortality. 3 According to certain research, women are less prone to viral infections than males and have stronger macrophage, neutrophil activation, and superior antibody production. 4 The most common symptoms of COVID-19 are fever, dry cough, and shortness of breath. Some patients may also experience myalgia, nasal congestion, sore throat, headache, arthralgia, and diarrhea. 5 *In a* study by Fu et al 6 involving 3600 patients, fever was the most common symptom in $83\%$ of patients, cough in $60\%$, fatigue in $38\%$, and the mortality rate was significantly higher in patients with fever, fatigue, and headache. On the other hand, fever and cough were the 2 most common symptoms with a rate of $82.1\%$ for fever and $45.8\%$ for cough, and a significant association with mortality was found in Tian et al’s 7 study in 262 patients. In our study, the cough was the most common symptom with a rate of $46.3\%$, followed by fever in $29.5\%$, sore throat in $27.5\%$, malaise in $26.8\%$, myalgia in $21.5\%$, arthralgia in $18.8\%$, headache in $16.8\%$ and dyspnea in $10.7\%$. The lower rate of symptoms such as fever and cough compared to other studies was associated with observing mild to moderate cases. When the symptoms were compared in the mortal and non-mortal groups, a significant difference was found in fever ($$p \leq 0.001$$) and dyspnea ($$p \leq 0.05$$), whereas no significant difference was found in other symptoms.
The most common comorbidity in our study was hypertension ($35.2\%$), followed by diabetes mellitus ($22\%$), COPD ($13.4\%$), and cardiovascular diseases ($5.8\%$). Comorbidity in COVID-19 patients is a factor that significantly increases mortality. 8 While $70\%$ of the patients included in our study had comorbidity, all of our patients with mortality had one or more comorbidities. When the mortality and non-mortality groups were compared in terms of comorbidities, a significant difference was found for hypertension ($62.2\%$ - $32\%$), diabetes mellitus ($42.2\%$ - $20\%$), and cardiovascular diseases ($22\%$ - $4.1\%$, p≤0.001). In contrast, no significant difference was found between COPD and renal failure. A meta-analysis of 34 studies on comorbidities affecting mortality, diabetes mellitus, COPD, cardiovascular disease, and hypertension were important risk factors for comorbidity. In contrast, no significant relationship was found between acute cardiac failure, renal failure, and mortality. 9 The results of the studies in the literature were similar to our study.
Laboratory parameters have an important place in the diagnosis and prognosis of COVID-19. Thrombocytopenia and lymphopenia are the most common findings in complete blood count. Elevated D-dimer levels support coagulopathy and are important in the severe disease course. Inflammatory markers are increased during COVID-19, and CRP has an important place in predicting the prognosis of the disease. 10 *In a* study carried out on patients with the severe course, Li et al 11 found that the level of lymphopenia, increased D-dimer, and elevated ferritin were the most important laboratory indicators of prognosis. In our study, when the laboratory parameters of the patients were analyzed, lymphopenia (p≤0.001), elevated CRP ($$p \leq 0.05$$), D-dimer ($$p \leq 0.05$$), and ferritin ($$p \leq 0.006$$) were significantly different in the mortal and non-mortal groups.
Thoracic CT plays a complementary role in diagnosing viral pneumonia, and the severity of pneumonia provides valuable information for the prognosis of patients. 12 A computerized tomography scan was carried out in the majority ($90.8\%$) of the patients we included in the study, and all patients with mortality had pulmonary involvement. While 360 ($72\%$) of the patients had typical involvement in COVID-19, 41 patients ($8.2\%$) had atypical involvement. Of the patients with a mortal course, 7 ($15.5\%$) had atypical, and 38 ($84.4\%$) had typical involvement in thorax CT. The mortality rate of patients with typical involvement was significantly higher than those with atypical involvement (p≤0.001). In our study, the microbiological diagnosis was also carried out in patients without typical radiological findings, which are common in COVID-19 patients in the literature. No other reason was found to explain the radiological findings in these patients. Therefore, it should be kept in mind that patients without typical radiological findings may also have COVID-19. Still, it should be taken into account that mortality is less in these patients compared to those with typical involvement.
Hydroxychloroquine is a drug developed in the mid-20th century to treat malaria. It has been used in treating autoimmune diseases due to its immunomodulatory effects on various cytokines, including interleukin-1 (IL-1) and IL-6. 13 Hydroxychloroquine was shown to be effective against SARS-CoV-2 in invitro studies, but its use was discontinued in later studies due to its lack of antiviral activity against SARS-CoV-2 and its cardiotoxic effects. 14 In our center, 132 ($26.4\%$) patients were treated with HCQ, and 12 ($9\%$) were mortal. Favipiravir is a purine analog that inhibits the RNA-dependent RNA polymerase of influenza and other RNA viruses and is approved in Japan for treating influenza. 15 Regarding its potential role in COVID-19, favipiravir has in vitro activity against SARS-CoV-2. However, it is unclear whether sufficient drug levels can be achieved in vivo to inhibit SARS-CoV-2. 16 *In a* prospective, multicenter study involving 240 COVID-19-positive adult patients with moderate disease from China, favipiravir was compared with umifenovir in treatment. The clinical cure rate on day 7 was significantly higher in the favipiravir group ($71.4\%$) than in the umifenovir group ($55.8\%$, $$p \leq 0.019$$). 17 In our study, 314 ($62.8\%$) patients received favipiravir, and 41 ($8.2\%$) patients received HCQ + favipiravir treatment. In our study, mortality developed in $9.5\%$ of patients receiving favipiravir and $7.3\%$ receiving HCQ + favipiravir.
The US Food and Drug Administration has approved the protease inhibitor lopinavir/ritonavir for the treatment of HIV. It has been demonstrated in vitro that lopinavir/ritonavir inhibits the replication of SARS-CoV-1 and MERS-CoV. According to a study on SARS-CoV-1 patients, those who received lopinavir/ritonavir had lower rates of acute respiratory distress syndrome and mortality. 18 Additionally, lopinavir/ritonavir and supportive care were evaluated in a research to treat COVID-19, and no appreciable difference was discovered in terms of clinical recovery duration, viral clearance, or mortality. 19 Since it falls under group B of the Ministry of Health’s treatment recommendations, lopinavir/ritonavir has been included in our country’s treatment options, particularly for pregnant patients. Only pregnant patients in our trial received lopinavir/ritonavir, and no fatality was noted.
None of these antiviral medications are suggested per the current recommendations. When the antiviral therapies that the patients who died got were examined in our study, there was no discernible difference in mortality ($$p \leq 0.16$$). Numerous meta-analyses have demonstrated that these medications are ineffective, even though their effects could not be assessed because there was no control group in our study that did not receive antiviral medications. 20-22 Current guidelines recommend nirmatrelvir/ritonavir, monoclonal antibodies in mild outpatients, remdesivir, baricitinib, and tofacitinib in hospitalized moderate to severe cases. 23 *It is* important to access current treatments to reduce mortality. 24
## Study limitation
A standard treatment could not be carried out in patients. It can be explained by adhering to the treatment regimes regulated according to the Ministry of Health guides starting from the first period of COVID-19 disease.
In conclusion, our study found a strong relationship between the risk of death and the presence of concomitant conditions such as hypertension, diabetes mellitus, cardiovascular diseases, lymphopenia, high CRP, raised D-dimer, and ferritin at the time of presentation. Treatments such as HCQ, favipiravir, and lopinavir/ritonavir were not found to have lower mortality rates than one another. Considering these elements when assessing patients and adjusting the course of treatment according to the recommendations of the most recent guidelines may lower mortality.
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---
title: The prevalence of metabolic syndrome among university students in Wasit, Iraq
authors:
- Alaa H. Zamil
- Seenaa S. Amin
journal: Saudi Medical Journal
year: 2022
pmcid: PMC10043912
doi: 10.15537/smj.2022.43.11.20220558
license: CC BY 4.0
---
# The prevalence of metabolic syndrome among university students in Wasit, Iraq
## Body
Metabolic syndrome (MetS) prevalence has been rising steadily over the past few decades all over the world. This condition, which is now considered a major problem in both public health and clinical practice, is approaching epidemic levels. 1 Metabolic syndrome has been linked to an increased risk of diabetes as well as both atherosclerotic and non-atherosclerotic cardiovascular disease (CVDs). The hallmarks of MetS involve central obesity, hyperglycemia, hypertention, and abnormal lipid profiles. 2 The leading factor in patient death and morbidity, particularly in diabetes patients, is CVDs. 3 As reviewed previously several organizations have attempted to create MetS diagnostic criteria. The World Health Organization (WHO) made the first attempt in 1998, defining MetS as a syndrome categorized by insulin resistance and the presence of insulin resistance was an essential component of the syndrome, with a minimum 2 from following criteria: raised triglycerides, low high density lipoprotein (HDL-C) levels, raised blood pressure, obesity, and microalbuminuria. In 2001 The National Cholesterol Education Program (NCEP) published a new group of criteria that included fasting blood glucose (FBG), blood pressure, dyslipidemia, and waist circumference (WC). While in 2005 the International Diabetes Federation (IDF) added central obesity as a requirement for MetS diagnosis, focusing on waist circumference as a simple screening tool. 4 The IDF and the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) carried out a research in 2009 to determine whether or not 3 of the 5 criteria for MetS were present, and it was relied upon in the present research. 5 The global prevalence of MetS in young adults ranged from $5\%$ to $7\%$, whereas the results of the National Health and Nutrition Examination Survey (NHANES) showed that the prevalence of MetS rose dramatically among people aged 20-39 years old in the United States (from $16.2\%$ to $21.3\%$), and among Asian race individuals (from $19.9\%$ to $26.2\%$). 6,7 The presence of one component of MetS increases the chance of acquiring MetS in the future and likely represents a significant lifetime burden of CVD risk. This increases the risk of having CVD later in life.
According to a systematic study that was carried out by Campo-Arias et al, 8 there is a large amount of heterogeneity of MetS prevalence in college students. This prevalence ranges from $0\%$ to $19.2\%$ according to NCEP-ATP III definition. 8 This varied may be due to influenced by their new surroundings and social influences, which can lead to hazardous behaviors. Unhealthy diets and a low levels of physical activity have been the most common harmful behaviors. Any of these factors can lead to overweight and an increase in MetS prevalence and associated symptoms. 9 According to a previously reported analysis, MetS prevalence and its components is influenced by a variety of factors, including genetic background, levels of physical activity, diet, diabetic family history, smoking, and educational attainment. 1,4 Early detection of MetS components may result in focused therapies that stop the syndrome from developing and, as a result, minimizes the risk of CVD later in life. There are few published statistics on the prevalence of MetS among young adults in Iraq. Therefore, this study aimed to determine MetS prevalence among Iraqi university students and recognize potential risk factors for MetS prevention and management in young adults.
## Abstract
### Objectives:
To determine the prevalence of the metabolic syndrome (MetS) and its related risk factors in a group of healthy subjects.
### Methods:
This cross-sectional analytic investigation used a convenient sample of 300 apparently healthy university students from Wasit, Iraq, between October 2021 and February 2022. The data was collected using a structured direct interview with a self-administered questionnaire. Anthropometric measurements (waist circumference [WC], body mass index [BMI], height, weight, and the blood pressure), total cholesterol level, triglyceride level, high-density lipoprotein (HDL-C), and fasting blood glucose (FBG) were all measured. IDF/AHA/NHLBI criteria were used to diagnose metabolic syndrome.
### Results:
Overall, $41.3\%$ of students had MetS, with female ($66.9\%$) students having the highest frequency. The most common component of MetS was elevated FBG ($98.3\%$), followed by increased WC ($87.9\%$), and finally a low HDL-C level ($85.4\%$). The following factors were found to be predisposing to MetS: being female (OR=2.32), over the age of 20 (OR=1.96), obese (OR=9.46), high consumption of fast food (OR=2.35), and physically inactive.
### Conclusion:
Metabolic syndrome prevalence and defining criteria are significantly high among Iraqi university students. Fasting blood glucose was the most common component followed by increased WC. The significant risk factors for MetS were older age, females, high BMI (≥25), low physical-activity, and eating of fast foods, and can remedy the risk if the components of the disease are reported at a younger age.
## Methods
This cross-sectional study participants were selected from Wasit University, Technical Institute, and Kut University College, Wasit, Iraq, between October 2021 and February 2022. A convenient sample of 300 students for this study of 18-25 years from mixed colleges and stages were chosen. The inclusion criteria were adults (18-25 years old), both genders, and have no chronic disease. The exclusion criteria were CVD patients, diabetic or hypertensive patients, autoimmune disease patients, pregnant and breastfeeding women, and patients with kidney and liver problems. A verbal agreement was obtain from all participants after being informed on the study’s purpose and the expected benefits. The research was carried out in accordance with the Helsinki Declaration.
A formalized direct meeting with questionnaires managed by the interviewer was used. The questionnaire included requests on demographics, sedentary lifestyle, physical activity, and dietary habits. The amount of fruit and vegetables eaten daily was determined by tallying up the total number of servings of fruit and vegetables consumed on a daily basis over the course of a typical week. Insufficient consumption of fruit and vegetables was defined as having fewer than 5 servings on a daily basis. The question, “On average, how many meals per week do you consume that were not made at home?” was used to determine how often respondents ate meals away from home, specifically fast food. Daily eating of fast food (≥1-3 times/week) considered eating. The smoking status were divided into 2 groups regular active smokers (smoking at least 10 cigarette/week) and non-smokers, and type of smoking classified to cigarette, Argela, and both.
It was suggested that engaging in physical exercise of a moderate level for more than 150 minutes a week 10 should be considered sufficient to keep one in a decent condition of health. This was the dividing line that our research used to separate participants who engaged in regular physical activity from those who did not participate in such activity.
Following a 5-minute break time, consented participants were provided a verified self-questionnaire. Once completed, anthropometric data of BMI and waist circumference were taken by a trained nurse.
Weight, length, and waist measurement were all considered. A portable stadiometer (Seca, Germany) was used to measure height, a portable electronic scale (Germany) was used to measure weight, and a non-stretchable tape for the measurement of waist circumference, when exhaling, WC was measured at the midway between the top of the hip bone and the lowest rib. During the weighing, participants were asked to dress light clothing and refrain from wearing shoes. Weight was recorded in kilograms and height was recorded in meters. Body mass index (BMI) (kg/m 2) was calculated using weight and height. Underweight (BMI: <18.5), normal weight (BMI: 18.5-24.9), overweight (BMI: >25), and obese (BMI: >30) were used to categorize participants. 10 The measurement of systolic and diastolic blood pressures (SBP and DBP) after a 15-minute rest by using a standardized mercury sphygmomanometer (MDF, USA), and it was measured twice at least a half-hour gap, and the participant’s BP was calculated using the average of 2 readings.
The biochemical tests listed below were performed on blood samples that were drawn in the morning after a 12-hour fast. An enzymatic colorimetric method with readymade kits was used to measure fasting serum glucose (FSG), triglyceride (TG), serum cholesterol, and HDL: Glucose MR (fasting) kit (LINEAR CHEMICALS, SPAIN), Triglycerides MR kit (LINEAR CHEMICALS, SPAIN), HDL- cholesterol kit (LINEAR CHEMICALS, SPAIN), and Cholesterol CHOD PAP kit (Biolabo SAS, France).
According to the definition IDF/AHA/NHLBI criteria, participants had MetS when they had 3 or more of the following 5 criteria: i) A high FBG levels ≥100 mg/dL; ii) A high blood pressure reading (defined as a SBP ≥130 mmHg or a DBP ≥85 mmHg); iii) A low level of HDL-C (for male <40 mg/dL and female <50 mg/dL); iv) A high level of TG ≥150 mg/dL; and v) An increased waist circumference (male ≥37 inches (94 cm) and female ≥31.5 inches (80 cm).
## Statistical analysis
Data analysis was carried out using the SPSS version 25.0 software (IBM Corp., Armonk, NY, USA). The test of Shapiro-Wilk was used to evaluate whether the data were assumed to be normal. Continuous variable expressed as median (interquartile rang), and difference between groups were checked using Mann-Whitney tests. The categorical data are presented as percentage and frequency, and difference between groups were checked using Chi-square test. The association between the MetS group and a potential risk factor is measured using an odds ratio (OR) with $95\%$ confidence intervals (CI). A p-value of <0.05 was deemed statistically significant.
## Results
A total of 300 students were included, 165 ($55\%$) female and 135 ($45\%$) male students. Their overall age was ranged [18-25] years. Over weight and obese students represented ($31\%$, $7\%$) respectively of the research sample, $9.7\%$ of them had regular physical activity. Most students were nonsmokers (244; $81.33\%$)(Table 1).
**Table 1**
| Characteristics | Total (n=300) | Total (n=300).1 | Female (n=165) | Female (n=165).1 | Male (n=135) | Male (n=135).1 | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Characteristics | n | % | n | % | n | % | |
| Age (years) | | | | | | | |
| 18-21 | 160 | 53.3 | 96 | 58.2 | 64 | 47.4 | 0.063 |
| 22-25 | 140 | 46.7 | 69 | 41.8 | 71 | 52.6 | 0.063 |
| Body mass index (Kg/m2) | | | | | | | |
| Underweight (<18.5) | 20 | 6.7 | 14 | 8.5 | 6 | 4.4 | 0.186 |
| Normal (18.5-24.9) | 166 | 55.3 | 93 | 56.3 | 72 | 53.3 | 0.186 |
| Overweight (25-29.9) | 93 | 31 | 45 | 27.3 | 49 | 36.3 | 0.186 |
| Obese (≥30) | 21 | 7.0 | 13 | 7.9 | 8 | 6.0 | 0.186 |
| WC (inch) | | | | | | | |
| <31.5 female | 157 | 52.3 | 63 | 38.2 | 94 | 69.6 | 0.033* |
| <37 male | 157 | 52.3 | 63 | 38.2 | 94 | 69.6 | 0.033* |
| ≥31.5 female | 143 | 47.7 | 102 | 61.8 | 41 | 30.4 | 0.033* |
| ≥37 male | 143 | 47.7 | 102 | 61.8 | 41 | 30.4 | 0.033* |
| Systolic BP mmHg | | | | | | | |
| <130 | 233 | 77.7 | 141 | 85.5 | 92 | 68.1 | <0.001** |
| ≥130 | 67 | 22.3 | 24 | 14.5 | 43 | 31.9 | <0.001** |
| Diastolic: | | | | | | | |
| <85 | 238 | 79.3 | 138 | 83.6 | 100 | 74.1 | 0.046* |
| ≥85 | 62 | 20.7 | 27 | 16.4 | 35 | 25.9 | 0.046* |
| Physical activity (min/week) | | | | | | | |
| <150 | 271 | 90.3 | 160 | 97.0 | 111 | 82.0 | <0.001** |
| ≥150 | 29 | 9.7 | 5 | 3.0 | 24 | 18.0 | <0.001** |
| Smoking | | | | | | | |
| Yes | 56 | 18.67 | 2 | 1.2 | 54 | 40.0 | <0.001** |
| No | 244 | 81.33 | 163 | 98.8 | 81 | 60.0 | <0.001** |
| Smoking type | | | | | | | |
| Cigarette | 21 | 7.0 | 0 | 0 | 21 | 15.6 | <0.001** |
| Argela | 25 | 8.34 | 2 | 1.2 | 23 | 17.0 | <0.001** |
| Both | 10 | 3.33 | 0 | 0 | 10 | 7.4 | <0.001** |
| Fast food (times/week) | | | | | | | |
| 0 | 36 | 12.0 | 14 | 8.5 | 22 | 16.3 | 0.004* |
| 1-3 | 146 | 48.7 | 94 | 57.0 | 52 | 38.5 | 0.004* |
| >3 | 118 | 39.3 | 57 | 34.5 | 61 | 45.2 | 0.004* |
| Vegetable eating (times/week) | | | | | | | |
| 0 | 9 | 3.0 | 7 | 4.2 | 2 | 1.5 | 0.094 |
| 1-5 | 200 | 66.7 | 110 | 66.7 | 90 | 66.7 | 0.094 |
| >5 | 91 | 30.3 | 48 | 29.1 | 43 | 31.8 | 0.094 |
Most of students had high levels of FBG (≥100 mg/dl) $90\%$ male ($94.1\%$) was significantly ($$p \leq 0.034$$) higher than female ($86.7\%$). Low levels of HDL-C were significantly higher in females than males ($p \leq 0.001$) as shown in (Table 2).
**Table 2**
| Paraneters | Total (n=300) | Total (n=300).1 | Female (n=165) | Female (n=165).1 | Male (n=135) | Male (n=135).1 | P -value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Paraneters | n | % | n | % | n | % | P -value |
| FBG (mg/dl) | | | | | | | 0.034* |
| <100 | 30 | 10 | 22 | 13.3 | 8 | 5.9 | 0.034* |
| ≥00 | 270 | 90 | 143 | 86.7 | 127 | 94.1 | 0.034* |
| Triglycerides (mg/dl) | | | | | | | -- |
| <150 | 300 | 100 | 165 | 100 | 135 | 100 | -- |
| ≥150 | 0 | 0 | 0 | 0 | 0 | 0 | -- |
| HDL-C(mg/dl) | | | | | | | <0.001** |
| <50(female) | | | 139 | 84.2 | | | <0.001** |
| <40 (male) | 195 | 65 | | | 56 | 41.49 | <0.001** |
| ≥50 (female) | | | 26 | 15.8 | | | <0.001** |
| ≥40 (male) | 105 | 35 | | | 79 | 58.51 | <0.001** |
| Total cholesterol (mg/dl) | | | | | | | -- |
| <200 | 300 | 100 | 165 | 100 | 135 | 100 | -- |
| ≥200 | 0 | 0 | 0 | 0 | 0 | 0 | -- |
The prevalence of MetS using IDF/ AHA/NHLBI criteria was $41.3\%$, affecting 124 students. Sixty two ($20.7\%$) students tested positive for one MetS criteria, 112 ($37.3\%$) for 2 MetS criteria, 98 ($32.7\%$) for 3 MetS criteria, and 26 ($8.7\%$) students for 4 MetS criteria. There were no participants that were positive for 5 components (Table 3). Elevated FBG ($98.3\%$) has been found to be the most prevalent MetS component, followed by increased WC ($87.9\%$) and low HDL-C ($85.4\%$), whereas elevated SBP was $42.7\%$ and DBP was $33.8\%$ (Figure 1).
Metabolic syndrome participants had significantly ($p \leq 0.05$) higher weight, BMI, WC, SBP, DBP, FBG, TC, and reduced HDL C levels, and not significantly for TG levels ($$p \leq 0.23$$) (Table 4).
**Table 4**
| Variables | Metabolic syndrome Median (interquartile range) | Metabolic syndrome Median (interquartile range).1 | Z Test | P-value |
| --- | --- | --- | --- | --- |
| Variables | YES (n=124) | NO (n=176) | Z Test | P-value |
| Weight (kg) | 71 (24.0) | 62.5 (19.0) | 6.05 | <0.001** |
| Height (cm) | 164.5 (13.0) | 166 (17.0) | 1.9 | 0.057 |
| BMI (kg/m²) | 26.55 (4.8) | 22.22(4.1) | 9.86 | <0.001** |
| WC (inch) | 37 (7.0) | 31 (4.0) | 9.66 | <0.001** |
| SBP (mmHg) | 125 (25.0) | 115 (12.0) | 5.54 | <0.001** |
| DBP (mmHg) | 82 (15.0) | 75 (10.0) | 6.1 | <0.001** |
| Fasting blood glucose | 113 (11.0) | 110 (15.0) | 2.7 | 0.006* |
| Total cholesterol (mg/dl) | 167 (8.0) | 165 (11.0) | 2.64 | 0.008* |
| TG (mg/dl) | 108 (5.0) | 107 (5.0) | 1.18 | 0.23 |
| HDL-C (mg/dl) | 39 (11.0) | 42 (10.0) | 2.5 | 0.01* |
MetS was found to be significantly more common in female than male students ($p \leq 0.001$) (OR [odds ratio]=2.321). The older participants were more at risk of developing MetS ($$p \leq 0.004$$) (OR=1.963). As BMI increased, the prevalence of MetS rose, as it was high among participants with BMI ≥25 ($66.9\%$) as compared to the $33.1\%$ of participants who had BMI<25 ($p \leq 0.001$). Moreover, low physical activity has been associated with a higher risk of the developing MetS among the participants ($p \leq 0.001$). Regarding dietary habits, MetS was most common in individuals with increased consumption of fast food ($51.6\%$) and low in individuals with increased consumption of vegetables and fruits ($26.1\%$). The incidence of MetS was not significantly associated with smoking ($$p \leq 0.57$$) according to small number of smokers in this study (Table 5).
**Table 5**
| Risk factors | Metabolic syndrome | Metabolic syndrome.1 | Chi-square test | P-value | Odds ratio | 95% CI |
| --- | --- | --- | --- | --- | --- | --- |
| Risk factors | Yes n=(124) | No n=(176) | Chi-square test | P-value | Odds ratio | 95% CI |
| Age group (years) | | | | | | |
| 18-21 | 54 (43.5) | 106(60.2) | χ2 = 8.131 | 0.004* | 1.96 | 1.23-3.13 |
| 22- 25 | 70 (56.5) | 70(39.8) | χ2 = 8.131 | 0.004* | 1.96 | 1.23-3.13 |
| Gender | | | | | | |
| Female | 83 (66.9) | 82(46.6) | χ2= 12.166 | <0.001** | 2.32 | 1.44-3.74 |
| Males | 41 (33.1) | 94(53.4) | χ2= 12.166 | <0.001** | 2.32 | 1.44-3.74 |
| Smoking group | | | | | | |
| Smokers | 25 (20.2) | 31 (17.6) | χ2= 0.311 | 0.57 | 1.18 | 0.65-2.12 |
| Non smokers | 99(79.8) | 145(82.4) | χ2= 0.311 | 0.57 | 1.18 | 0.65-2.12 |
| Type of smoking | | | | | | |
| Cigarettes+ both | 11 (8.9) | 20 (11.35) | χ2=3.49 | 0.06 | 3.18 | 0.92-10.9 |
| Argela | 14 (11.3) | 11 (6.25) | χ2=3.49 | 0.06 | 3.18 | 0.92-10.9 |
| BMI (Kg/m2) | | | | | | |
| <25 | 41 (33.1) | 145(82.4) | χ2=75.113 | <0.001** | 9.46 | 5.52-16.23 |
| ≥25 | 83 (66.9) | 31(17.6) | χ2=75.113 | <0.001** | 9.46 | 5.52-16.23 |
| Physical activity (minute/week) | | | | | | |
| <150 | 124 (100) | 147 (83.5) | χ2=22.6 | <0.001** | 0.54 | 0.48-0.60 |
| ≥150 | 0 (0) | 29 (16.5) | χ2=22.6 | <0.001** | 0.54 | 0.48-0.60 |
| Fast-food (times/week) | | | | | | |
| 1-3 | 49 (39.5) | 97 (55.1) | χ2=11.3 | 0.001* | 2.35 | 1.42-3.86 |
| >3 | 64 (51.6) | 54 (30.7) | χ2=11.3 | 0.001* | 2.35 | 1.42-3.86 |
| Vegetable eating (times/week) | | | | | | |
| 1-5 | 88(73.9) | 112(65.1) | χ2=2.55 | 0.11 | 0.65 | 0.39- 1.10 |
| >5 | 31 (26.1) | 60 (34.9) | χ2=2.55 | 0.11 | 0.65 | 0.39- 1.10 |
## Discussion
The fundamental result of this research was MetS has a prevalence of $41.3\%$ ($66.9\%$ female and $33.1\%$ male) in university-aged adults. This agreed with the findings for Egyptian university students: $16.7\%$ MetS prevalence in 18-25 year old, which use the ATP-III guidelines. 11 The risk of developing MetS prevalence in the Kingdom of Saudi Arabia among university students was $17.7\%$. 12 While the study by Olfert et al 13 reported that MetS prevalence among West Virginia University students was $15\%$.
Many studies in Iraq reported MetS prevalence in various populations. For example, Al-Azzawi et al 5 showed that $37.8\%$, $40.6\%$, and $46.9\%$ (respectively for NCEP/ATP III 2005, IDF 2005 and [2009] revised IDF definition) of Iraqis aged 25-85 at the Baghdad Teaching Hospital were diagnosed with MetS. Additionally, $40.9\%$ (IDF criteria) of people in Erbil, Iraq aged ≥18 year were diagnosed with MetS. 14 Lastly, $40.6\%$ ($42.8\%$ females and $36.5\%$ males) of participants in a Baghdad study of obese adults with different ages have MetS. 15 A systematic survey of the relevant studies revealed that there is a significant frequency of MetS among college students, which is consistent with these findings. 8 *This is* due to the fact that young people who have recently started college are at a crucial age for the creation of good routines and behaviors that fall into routines. For example, first-year college students have a greater propensity to experience a more rapid increase in body mass than the usual adult. The use of tobacco, the drinking excessive amounts of soft drinks, and the consumption of an unhealthy diet are all instances of poor lifestyle choices. It has been demonstrated that the existence of these risk factors, in conjunction with obesity, increases the likelihood of developing MetS in individuals of any age, regardless of their educational or occupational history. 8 The MetS prevalence could vary between studies according to the MetS cluster used, target population, obesity, and the participant’s lifestyle (irregular meal time, physical inactivity, and increased stress). Also, because there are several methods for diagnosing MetS depending on the standards and thresholds set by the authorities, these factors differ greatly from country to country due to various MetS diagnosis cut-off values and criteria. 16 The findings of the current study demonstrate that FBG was the most frequent component of MetS ($98.3\%$), followed by WC ($87.9\%$), and finally, HDL-C ($85.4\%$). This result in agreement with the study by Alowfi et al 16 which report high-FBG and high WC were the most prevalent MetS components in adolescent students in Saudi Arabia.
Various factors, including diet, can cause high blood sugar levels. Excess sugar and carbohydrate consumption increases blood sugar levels after meals because the food is broken down into glucose molecules that enter the bloodstream.
The studies by Mahrous et al 11 in Egypt and Naghipour et al 1 in Iran showed that central obesity was the most prevalent component ($41.8\%$ and $75.8\%$, respectively) of MetS among the students. While another study in Colombia reported low HDL C levels as the most frequent MetS component, 17 and a study by Mbugua et al 18 found the most prevalent component was elevated triglycerides for university students in Kenya.
In the current study, Age, gender, BMI, physical activity and type of foods are the most important risk factors and lifestyle factors that effect on the incidence of Mets, as shown in (Table 5).
A statistically significant association was observed between the magnitude of MetS and increasing age, ranging from $43.5\%$ in the group of 18-21 year olds to $56.5\%$ for those aged 22-25 years, a result that agreed with rates found by Roos et al. 19 The study presented here shows that female, as compared to the male participants, had higher MetS ($66.9\%$, $33.1\%$) respectively, with (OR=2.32, $95\%$ CI=1.4-3.7); a similar finding to an analysis of Pai et al. was noted 2, and agreed with Zafar et al, 20 who carried out their study among 2982 Indians and found that females ($13.8\%$) had MetS more than male ($9.6\%$). Although the specific cause of such gender differences is unknown, it has been suggested that females are less active than males. 11 The main risk factors for MetS development include obesity and abdominal obesity (OR=9.46). This result was agreement with the study by B. Damiri et al 21 who reported MetS prevalence among overweight and obese university Palestinians students was high. Another study by Balgoon et al 12 shows young female university students in Saudi Arabia with higher BMI values had a higher chance of having MetS, and this increasing BMI also correlates with other MetS components.
Regarding physical activity, participants had a high level of physical activity had a significantly low rate of MetS (OR=0.54, $95\%$ CI=0.48-0.60). Numerous studies have demonstrated that exercise lowers the incidence of MetS. 22 Increased levels of leisure-time physical activity are linearly related with a decreased risk of MetS, whether in duration or intensity. 23 *In this* study, $91.1\%$ of participants had fast food, and $56.6\%$ of them had more than 3 times/week. High consumption of fast food was substantially linked to MetS (OR=2.35, $95\%$ CI=1.42-3.86). Although the students were aware of the risks to their health posed by consuming fast food, their eating patterns did not suggest that they were engaging in behavior that could be detrimental to their health. This was particularly true when the students were interacting with their friends, who tended to consume more fast food than the students themselves did. 24 Fast food intake has negative consequences on the pandemic of overweight and obesity, fast food eating has been identified as a significant contributor to obesity and rapid weight gain among a number of dietary variables and high risk for metabolic disease. 25
## Study limitations
The study has a small sample size and was carried out in a single institution. Also different ethnicity (Arabs, Kurds, and Turkmans), and country wide study were not included.
In conclusion, this study shows that the high prevalence of MetS in university students, FBG was the most common component followed by increased WC, and the significant risk factors for MetS were older age, females, high BMI (≥25), low physical-activity, and eating of fast foods. This last result raises the possibility that high FBG may be an important indicator of early pathology linked to the onset of MetS, so adoption of preventive lifestyle modification (like healthy eating behaver and increased physical activity) is recommended to avoid development of MetS among young adult.
Further studies with more studies on this population are needed to help build a comprehensive screening and intervention approach for university students. In addition, investigating the importance and significance of high FBG levels in collage students may have significant benefits for public health, since treatment aiming at decreasing high FBG levels may minimize the future occurrence of MetS and associated clinical disease. Lasty, study MetS prevalence among high school students, and there should be educational programs that encourage healthy eating and provide support for those activities at school.
## References
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9. Reisinger C, Nkeh-Chungag BN, Fredriksen PM, Goswami N.. **The prevalence of pediatric metabolic syndrome—A critical look on the discrepancies between definitions and its clinical importance**. *Int J Obes* (2021) **45** 12-24
10. Lee DH, Nam JY, Kwon S, Keum N, Lee JT, Shin MJ. **Lifestyle risk score and mortality in Korean adults: a population-based cohort study**. *Sci Rep* (2020) **10** 10260. PMID: 32581249
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12. Balgoon MJ, Al-Zahrani MH, Alkhattabi NA, Alzahrani NA.. **The correlation between obesity and metabolic syndrome in young female university students in the Kingdom of Saudi Arabia**. *Diabetes Metab Syndr Clin Res Rev* (2019) **13** 2399-23402
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|
---
title: Antibacterial and anti-biofilm activity of plumbagin against multi-drug resistant
clinical bacterial isolates
authors:
- Mohammad A. Alfhili
- Irfan Ahmad
- Yasser Alraey
- Abdulaziz Alangari
- Taha Alqahtani
- Ayed A. Dera
journal: Saudi Medical Journal
year: 2022
pmcid: PMC10043915
doi: 10.15537/smj.2022.43.11.20220446
license: CC BY 4.0
---
# Antibacterial and anti-biofilm activity of plumbagin against multi-drug resistant clinical bacterial isolates
## Body
Bacterial infections, such as bacteremia and pneumonia, have been associated with high rates of morbidity, mortality and economic costs, posing a real threat to public health globally. The mainstay of drug therapy for these infections is appropriate antibiotic treatment. 1 Over the past 2 decades, however, the level of antimicrobial resistance of major bacterial species, such as *Escherichia coli* (E. coli) and *Pseudomonas aeruginosa* [P. aeruginosa], has increased substantially. 2 Moreover, multidrug resistance (MDR), defined as resistance to at least one agent in ≥3 antimicrobial groups, has increasingly been reported locally and globally. For instance, a recent study from Saudi Arabia has found that $67\%$ of E. coli urine isolates were MDR. 3 Multidrug resistance constitutes a stumbling block in the face of developing new therapeutics. Nevertheless, a combinatorial approach involving the use of established antimicrobial agents along with naturally-derived compounds has produced encouraging results. 4 Quinones are aromatic dicarbonyl compounds that naturally exist in plants but can also be synthetically synthesized. Quinones have gained considerable attention for their medicinal properties including antimicrobial, anti-inflammatory, anti-atherosclerotic, and antitumor functions. 5 Plumbagin, or 5-hydroxy-2-methyl-naphthalene-1,4-dione, is a yellow crystalline naphthoquinone derivative with a molar mass of 188.17 g/mol. A vitamin K3 analogue, PGN possesses an additional hydroxy group on carbon 5 in the naphthalene group (Figure 1). Plumbagin is present in the root, leaf, and stem bark of multiple plants including Plumbaginaceae, Ebenceae, Dioncophyllaceae, Ancestrocladaceae, Droseraceae, and Juglandaceae families. 6,7 In particular, the roots of *Plumbago zeylanica* L. have been successfully used in India for therapeutic purposes against dermal and musculoskeletal conditions for more than 2500 years. 8
**Figure 1:** *- Molecular structure of plumbagin.
39
Characteristic hydroxyl and methyl groups added to para-naphthoquinone are noted.*
Beside its anti-inflammatory, antioxidant, antitumor, and antidiabetic activities,previous reports have demonstrated the efficacy of PGN against pathogenic microbes. 9-12 In particular, *Staphylococcus aureus* [S. aureus] and Candida albicans were susceptible to PGN in vitro and in vivo. 13,14 Plumbagin also exhibited synergistic activity with ciprofloxacin and piperacillin against MDR strains of methicillin-resistant S. aureus (MRSA), which was through inhibition of DNA gyrase. 15,9 Nevertheless, the potential efficacy of PGN against other bacterial species remains poorly studied.
Most studies were carried out using plant extracts with mixed chemical constituents, and little is known regarding PGN against MDR bacterial isolates in Saudi Arabia. In this report, we examine the antibacterial activity of PGN against clinical isolates of Gram-negative (E. coli, *Klebsiella pneumoniae* [K. pneumoniae], Salmonella Typhi [S. Typhi], and P. aeruginosa) and Gram-positive (S. aureus, *Staphylococcus saprophyticus* [S. saprophyticus], *Streptococcus pyogenes* [S. pyogenes], and *Enterococcus faecalis* [E. faecalis]) bacteria in an attempt to further elucidate its potential application in antimicrobial therapy. We also sought to determine the anti-virulence potential of PGN through evaluating its anti-biofilm activity against these isolates.
## Abstract
### Objectives:
To evaluate the antibacterial activity of plumbagin (PGN) against multidrug resistance (MDR) clinical isolates.
### Methods:
This study was carried out at the Department of Clinical Lab Sciences, King Khalid University from October 6, 2021 to December 14, 2021. We investigated the antibacterial and anti-virulence activity of PGN against MDR Gram-negative (Escherichia coli, Klebsiella pneumoniae, Salmonella Typhi, and Pseudomonas aeruginosa) and Gram-positive (*Staphylococcus aureus* [S. aureus], *Staphylococcus saprophyticus* [S. saprophyticus], Streptococcus pyogenes, and Enterococcus faecalis) clinical bacterial isolates. Agar well diffusion, microdilution assay, colony count method, biofilm formation, and time-kill kinetics were employed to probe the MIC, MBC, and anti-virulence activity of PGN.
### Results:
Plumbagin inhibited the growth of all tested isolates, with S. saprophyticus exhibiting the highest sensitivity. MIC values ranged from 0.029 to 0.117 µg/mL whereas MBC ranged from 0.235 to 0.94 µg/mL, with $79\%$ to $99\%$ growth inhibition. Moreover, all tested isolates showed a marked decrease in biofilm formation, with S. saprophyticus and S. aureus being the most sensitive.
### Conclusion:
Plumbagin is a stand-alone, broad spectrum antibacterial with promising potential against the rising threat of antimicrobial resistance.
## Methods
This is an experimental investigation that was carried out at the Department of Clinical Lab Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia, from October to December 2021. Ethical approval was obtained from the Research Ethics Committee at King Khalid University (ECM#2021-2801).
## Preparation of PGN stock solution
Plumbagin (CAS #481-42-5) from P. zeylanica L was obtained from Solarbio Life Science (Beijing, China) and the stock solution was prepared by suspending 1.88 µg in 1 mL of dimethyl sulfoxide (DMSO). Stock suspension was sonicated (Sonics Vibra cell; Sonics & Material, Newtown, CT, USA) at 40°C for 10 minutes and aliquots were preserved at –80°C. Working solutions were prepared by taking 1 mL of the stock solution and adding it to 9 mL of $2\%$ DMSO. Further dilutions were made as necessary to achieve the required final concentration of PGN. Appendix 1 shows lack of antibacterial activity by DMSO on tested isolates.
## Bacterial strains and growth conditions
A panel of clinical MDR Gram-negative and Gram-positive bacterial strains, including P. aeruginosa, E. coli, K. pneumoniae, S. Typhi, S. aureus, S. saprophyticus, S. pyogenes, and E. faecalis, were used in this study (Table 1). These strains were grown in nutrient broth at 37°C for 24 h.
**Table 1**
| Bacterial strains | Sample source | Year of Isolation | Resistance |
| --- | --- | --- | --- |
| Pseudomonas aeruginosa | Wound | 2019 | Imipenem, Gentamicin, Ciprofloxacin |
| Escherichia coli | Urine | 2019 | Amoxicillin/clavulanic acid, Ceftazidime, Ciprofloxacin |
| Klebsiella pneumoniae | Blood | 2019 | Piperacillin/Tazobactam, Cefoxitin, Ciprofloxacin |
| Salmonella Typhi | Throat | 2019 | Ciprofloxacin, Cefepime, Amoxicillin/clavulanic acid |
| Staphylococcus aureus | Nasal | 2019 | Tetracycline, Ciprofloxacin, Trimethoprim-Sulfamethoxazol |
| Staphylococcus saprophyticus | Urethra | 2019 | Nitrofurantoin, Penicillin, Tetracycline |
| Streptococcus pyogenes | Sputum | 2019 | Tetracycline, Levofloxacin, Azithromycin |
| Enterococcus faecalis | Urethra | 2019 | Penicillin, Tetracycline, Ciprofloxacin |
## Antimicrobial susceptibility testing
A lawn culture of the bacterial inoculum (OD610 of 0.01) was grown on Mueller–Hinton agar (HiMedia Labs, Mumbai, India) with 14 disk antibiotics (Liofilchem, Livorno, Italy) in line with the guidelines of Clinical and Laboratory Standards Institute (CLSI) [16]. Following incubation for 24 hours (h) at 37°C, the diameter of the clear zone of inhibition was subsequently measured in millimeters. E. coli ATCC 25922 and S. aureus ATCC 25923 were used as control strains.
## Antibacterial susceptibility assay by agar well diffusion
Bacterial strains were grown to the logarithmic phase (OD610 of 0.4-0.6) in nutrient broth and then diluted to a theoretical OD610 of 0.01. Agar well diffusion was used to probe the antibacterial efficacy of PGN. 17 Briefly, wells of 6 mm diameter were formed in the nutrient agar using the cap of a sterile syringe and a lawn culture was formed on the agar from diluted cultures using a sterile cotton swab. Next, 20 µL of PGN (1.88 µg/mL) and DMSO were added to triplicate wells and the plates were incubated aerobically for 24 h at 37°C. Gentamicin and vancomycin were used as positive controls and distilled water as a negative control. The diameter of the clear zone of inhibition of bacterial growth including the well diameter was measured in millimeters.
## Determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC)
Minimum inhibitory concentration and MBC of PGN were determined as described by Wei et al 18 with modification. Plumbagin concentrations were diluted 2-fold and ranged from 1.88 µg/mL to 0.014 µg/mL. To determine MIC, cultures were first grown to logarithmic phase and then further diluted in Mueller-Hinton broth to a theoretical OD610 of 0.01. Subsequently, 180 µL of all cultures were transferred to 96-well plates, 20 µL from the 2-fold dilution of PGN were loaded in triplicate wells, and 20 µL of DMSO served as control. Following aerobic incubation for 24 h at 37°C, each well was supplemented with 20 µL of alamar blue (Thermo Fisher, Watham, MA, USA) and observed at 1-hour intervals for the development of a pink color. The lowest concentration that did not cause a color change was recorded as the MIC. For the determination of MBC, 10 µL from wells with no color change were sub-cultured on nutrient agar and incubated aerobically for 24 h at 37°C. The minimum concentration of PGN at which no growth was observed was taken as the MBC for the examined strains.
## Growth inhibition
A colony count method was employed to determine the bactericidal effect of PGN. Approximately 10 µL of PGN were added to bacterial suspensions plated on nutrient agar using a spread plate method together with a negative control. The plates were observed for growth inhibition by counting colonies after incubation for 24 h at 37°C. The percentage of loss in viable cells was determined using the following equation: Where I% = percentage of bacterial growth inhibition; µC = mean value of OD610 in control cells; and µT = mean value of OD610 in treated cells.
## Antibiofilm effect of PGN
Biofilm formation of the bacterial isolates was evaluated according Zhang et al 19 with minor modifications. Briefly, 180 µL of fresh bacterial suspension (OD610=1.0) was added to 20 µL of different concentrations of PGN (MIC x0.5, MIC x1, MIC x2) in a 96-well plate, and the suspension was incubated for 24 h at 30°C without shaking to induce biofilm assembly. Untreated bacterial cells were used in each set of investigations as a negative control. After incubation, crystal violet was added and the absorbance was recorded at 488 nm.
## Time-kill kinetics assay
To investigate the effect of PGN on tested bacterial cells over time, 180 µL of the bacterial culture (OD610 of 0.01) was treated with 20 µL of different concentrations of PGN (MIC x0.5, MIC x1, MIC x2). A culture well with 20 µL of DMSO was considered as the control. The plates were further incubated aerobically at 37°C and OD was measured at 610 nm at 2-h intervals. The mean OD readings were plotted against time.
## Statistical analysis
Experiments were performed three times, and the results were plotted as mean ± SD. GraphPad Prism 6.0 (GraphPad Software, Inc., San Diego, CA, USA) was used for statistical analysis. Variations between 2 groups were examined with the 2-tailed Student’s t test, while one-way analysis of variance with Dunnett’s correction compared more than two groups. A p-value of <0.05 was considered statistically significant.
## Antibiotic susceptibility assay of standard antibiotics
To determine antibiotic susceptibility, clinical isolates of Gram-negative and Gram-positive bacteria including P. aeruginosa, E. coli, K. pneumoniae, S. Typhi and S. aureus, S. saprophyticus, S. pyogenes, and E. faecalis were tested against antibiotics shown in Tables 1 & 2. All of the tested bacterial strains were resistant to 3 or more of the standard antibiotics (Appendix 2 & 3) which indicates that they are MDR. Antibacterial activity of PGN
**Table 2**
| Antibiotic group | Antibiotic name | Concentration (µg/disc) |
| --- | --- | --- |
| Penicillins | Penicillin | 10 |
| β-lactam/β-lactamase inhibitors combination | Amoxicillin-clavulanic acid | 20/10 |
| β-lactam/β-lactamase inhibitors combination | Piperacillin/Tazobactam | 100/10 |
| Aminoglycosides | Gentamicin | 10 |
| Second generation cephalosporins | Cefoxitin | 30 |
| Third generation cephalosporins | Ceftazidime | 30 |
| Fourth generation cephalosporins | Cefepime | 30 |
| Tetracyclines | Tetracycline | 30 |
| Carbapenems | Imipenem | 10 |
| Fluoroquinolones | Ciprofloxacin | 5 |
| Fluoroquinolones | Levofloxacin | 5 |
| Nitrofurans | Nitrofurantoin | 300 |
| Macrolides | Azithromycin | 15 |
| Folate pathway inhibitors | Trimethoprim-Sulfamethoxazol | 1.25/23.75 |
To assess the antibacterial activity of PGN, clinical isolates of Gram-negative and Gram-positive bacteria including P. aeruginosa, E. coli, K. pneumoniae, S. Typhi and S. aureus, S. saprophyticus, S. pyogenes, and E. faecalis were used. Susceptibility studies showed that PGN had a comparatively higher antibacterial activity against Gram-positive isolates. A zone size above 8 mm was considered significant based on the sensitivity of the bacterial strains to PGN. The zone of inhibition against Gram-positive bacteria was 28 to 35 mm in diameter, while that against Gram-negative bacteria ranged from 17 to 26 mm in diameter (Figure 2A-C).
**Figure 2:** *- Susceptibility of tested organisms to plumbagin (PGN). (A-C) Zone of inhibition is observed on plates supplemented with PGN against A) Pseudomonas aeruginosa, B) Escherichia coli, C) Klebsiella pneumoniae, D) Salmonella, E) Staphylococcus aureus, F) Staphylococcus saprophyticus, G) Streptococcus pyogenes, and H) Enterococcus faecalis. (D & E) MIC and MBC values of PGN against Gram-negative and Gram-positive isolates.*
In order to investigate MIC and MBC indices, selected bacterial strains were exposed to the aforementioned volume of PGN followed by an incubation period of 24 h. As shown in Figure 2, all bacterial strains tested were significantly susceptible to PGN. Bacterial growth was inhibited with higher zones of inhibition ranging from 17 to 35 mm, reflective of MIC values of 0.029-0.117 µg/mL and MBC values of 0.235-0.94 µg/mL (Figure 2D and 2E). For comparison, MIC and MBC values of gentamicin and vancomycin were used as controls against Gram-negative and Gram-positive isolates, respectively (Appendix 2).
## Plumbagin inhibits the viability of bacterial cells
Growth inhibitory effects of PGN on the tested bacterial strains were determined by counting the bacterial colonies after treating with PGN. The results of colony count on the tested bacterial strains showed a reduction in the number of colonies at the MBC concentration of PGN ranging from $79\%$ to $99\%$ (Figure 3). The lowest growth inhibition was found against S. Typhi (Figure 3A), whereas the highest was recorded against S. saprophyticus (Figure 3B).
**Figure 3:** *- Bacterial growth inhibition by plumbagin (PGN). Mean percentage of growth inhibition + SD by PGN at minimum bactericidal concentration for 24 h at 37°C for (A) Gram-negative and (B) Gram-positive isolates. Control bars indicate all untreated bacterial strains presented as 0% inhibition. CFU: colony-forming unit, P. aeruginosa: Pseudomonas aeruginosa, E. coli: Escherichia coli, K. pneumoniae: Klebsiella pneumoniae, S. aureus: Staphylococcus aureus, S. saprophyticus: Staphylococcus saprophyticus, S. pyogenes: Streptococcus pyogenes, E. faecalis: Enterococcus faecalis*
## Plumbagin inhibits bacterial biofilm formation
We attempted to investigate whether PGN could inhibit biofilm formation of the tested Gram-negative and Gram-positive bacterial strains. Bacteria were incubated with MIC x0.5, MIC x1, and MIC x2 of PGN for 24 h at 37°C. Furthermore, the rate of biofilm formation inhibition by PGN was also based on the concentration and treatment time.
The inhibition of biofilm formation was significant against all the tested bacteria but it was drastically reduced against S. saprophyticus and S. aureus in the wells treated with MIC x2 of PGN. At MIC x2 of PGN, quantitative estimation of biofilms formed by S. saprophyticus showed a 4.2-fold decrease and by S. aureus a 3.01-fold decrease, compared to control values (Figure 4 A&B). These results suggest that biofilm formation by all the tested bacteria was inhibited by PGN.
**Figure 4:** *- Plumbagin (PGN) reduces biofilm formation. (A) Gram-negative and (B) Gram-positive isolates were treated with different concentrations of PGN (minimum inhibitory concentration [MIC] x0.5, MIC x1 and MIC x2) under biofilm growing conditions for 24 hours and mean absorbance readings at 488 nm + SD were plotted. P. aeruginosa: Pseudomonas aeruginosa, E. coli: Escherichia coli, K. pneumoniae: Klebsiella pneumoniae, S. aureus: Staphylococcus aureus, S. saprophyticus: Staphylococcuss saprophyticus, S. pyogenes: Streptococcus pyogenes, E. faecalis: Enterococcus faecalis*
## Effect on bacterial growth
To determine time-killing kinetics of PGN, 180 µL of bacterial culture (OD610 of 0.01) were treated with 20 µL of PGN at different concentrations of MIC x0.5, MIC x1, and MIC x2. The growth of bacteria was observed at time intervals of 2 h (Figure 5). It is absolutely apparent that bacterial strain growth was repressed by treatment with PGN at different concentrations. Time killing kinetics indicated a dose dependent bactericidal effect of PGN on the tested bacteria. Our data distinctly indicates a strong bactericidal activity of PGN against the tested bacterial strains.
**Figure 5:** *- Kinetics of plumbagin (PGN)-induced growth inhibition. Representative (A) Gram-negative (a. Pseudomonas aeruginosa, b. Escherichia coli, c. Klebsiella pneumoniae, d. Salmonella) and (B) Gram-positive (a. Staphylococcus aureus, b. Staphylococcus saprophyticus, c. Streptococcus pyogenes, d. Enterococcus faecalis) strains were treated with different concentrations (MIC x0.5, MIC x1 and MIC x2) of PNG. Growth cycle of untreated organisms served as control. OD610 nm was measured at regular intervals of 2 hours and presented as mean + SD.*
## Discussion
Despite making great strides in the combat against microbial resistance, MDR remains a pivotal challenge in the development and validation of novel antimicrobial therapeutics. This is mainly due to the widespread inappropriate use of current antibiotics, in addition to the stagnated discovery of new efficacious chemotherapeutic agents. 9 Although the antimicrobial activity of P. zeylanica extracts is well established in the literature, the antibacterial activity of PGN, a major constituent of this plant, has not been thoroughly investigated. Herein, we demonstrate that PGN exhibits a potent, broad-spectrum antibacterial activity against clinical isolates of major worldwide concern.
The zones of inhibition against Gram-positive bacteria were larger than those against Gram-negative isolates (Figure 2), which could be attributed to the presence of a physical cell wall barrier in the latter as well as the presence of to efflux pumps. This is in parallel to the results obtained by Dissanayake et al 9 using P. indica extract. It is worth mentioning that standard antibiotics exhibit MIC values ranging from 15-107 µg/mL20 which, compared to our findings of 0.029-0.117 µg/mL (Figure 2), highlights the potent antibacterial activity of PGN. We have previously shown that thymoquinone had an MIC of 1.04-8.3 µg/mL, reflective of a much weaker activity. 4 Furthermore, our data indicate that PGN compares favorably and exceeds in antimicrobial potency a panel of alkaloid, terpenoid, phenolic, and thiophene natural products. 21 The highest susceptibility among the tested strains was observed against S. saprophyticus (Figure 2D). This coagulase-negative pathogen is responsible for urinary tract infections (UTIs) and related genitourinary complications in susceptible hosts with preexisting conditions. 22 Behind only E. coli, S. saprophyticus is the second most frequently encountered cause of uncomplicated community-acquired UTIs in females. 23 Currently, nitrofurantoin and a combination of trimethoprim-sulfamethoxazole are the antimicrobials of choice for S. saprophyticus infections. However, major side effects of these drugs include fatal pulmonary, dermal, gastrointestinal, hematological, and neurological symptoms. 24 Thus, in light of available evidence, PGN may offer a safer alternative for S. saprophyticus infections.
Conversely, although shown to be susceptible, E. coli, S. aureus, and E. faecalis required the highest concentrations of PGN compared to the other tested strains (Figure 2 D&E). This is in agreement with the consensus on these organisms being notorious for rapid acquisition of resistance. 4 Pathogenic E. coli, including intestinal and extraintenstinal strains, are behind the etiology of diarrhea, UTIs, septicemia, and meningitis, and adhesins, release of toxins, and tissue invasion form the major virulence factors mediating pathogenesis. MDR E. coli strains are responsible for both nosocomial and community-acquired infections, and they are often resistant to front-line agents, such as penicillin and third-generation cephalosporins. This resistance, mediated by extended-spectrum β-lactamase (ESBL), 25 has now increased due to the worldwide dissemination of the specific E. coli clone, E. coli ST131. 26 In an attempt to tackle this resistance issue, more powerful agents, such as carbapenems and polymyxins, have recently been relaunched as alternatives to inactive antibiotics. However, carbapenems suffer from poor absorbability, and cause pruritis, hepatotoxicity, diarrhea, vomiting, and nausea. 27 Virulent strains of S. aureuss cause a variety of fatal pathologies including osteoarthritis, endocarditis, sepsis, and prosthesis-related infections, among others. 28 Compared to glycopeptides such as vancomycin, methicillin-sensitive S. aureus responds well to β-lactam antibiotics, but this is not without relapse. 29,30 For MRSA, vancomycin and daptomycin remain the gold standard therapy. Nevertheless, the continuing emergence of resistant strains, combined with the established toxicity of these drugs and their prolonged use,underpins the search for alternative, fast-acting, and less harmful alternatives. Importantly, septic arthritis is often managed with immunosuppressants such as dexamethasone, which increases the patient’s susceptibility to infections and predisposes to hyperglycemia, an effect counteracted by PGN. 10,31,32 Clinical isolates of E. faecalis cause a host of ailments ranging from UTIs and bacteremia to endocarditis and meningitis. A recognized threat to public health, E. faecalis is most commonly resistant to rifampicin and erythromycin, in addition to vancomycin, which surprisingly may serve as a growth factor. 33,34 *Enterococcus faecalis* is also able to reside in biofilms, 35 further aggravating its virulence. Our results indicate that PGN possesses antibiofilm activity (Figure 4). Biofilm formation aids in immune evasion, which fosters prolonged colonization and tissue injury. Moreover, cells embedded in biofilms often remain in a dormant state, having their metabolism operating at a lower rate. As a consequence, biofilm-associated infections often respond variably to available therapy. These infections affect virtually all body systems, and, alarmingly, may promote tumorigenesis. 36 Therefore, modulating biofilm assembly and persistence, possibly through adhesins and quorum sensing, may prove to be efficacious against a multitude of life-threatening ailments. Of note, a number of polyphenols, including coumarins, anthocyanins, tannins, and flavonoids have also been demonstrated to interfere with biofilm formation in S. aureus, K. pneumoniae, P. aeruginosa, E. coli, and E. faecalis. 37 Development of pharmaceutical formulations relies on interaction of the active ingredient with excipients that could either enhance or suppress its efficacy. Notably, the bark of P. zeylanica has successfully been used to synthesize silver and gold nanoparticles with discernable antibacterial activity against P. aeruginosa and B. subtilis. 38 Therefore, evaluating the antimicrobial activity of PGN in nanoparticle formulations, and in the presence of common excipients such as polyethylene glycol, is also warranted.
## Study limitations
The current study lacks in vivo confirmation, effects of long-term exposure, and biochemical and molecular mechanisms, in addition to unknown additive or antagonistic interactions of PGN with other antibacterial compounds. Future studies should investigate the possible additive or synergistic role of PGN with standard antibiotics as well as the range of susceptible organisms and the mechanisms involved in its antimicrobial and anti-virulence activities both in vitro and in vivo.
In conclusion, since the majority of previous studies focused on plant extracts with mixed constituents, our report identifies PGN as a stand-alone broad-spectrum antibacterial agent effective against major clinical bacterial isolates from Saudi Arabia. Plumbagin is demonstrated to possess superior potency over numerous established and investigative antimicrobials, in addition to antibiofilm activity.
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|
---
title: Clinical significance of CA-125 in elderly patients with active pulmonary tuberculosis
authors:
- Ping Zhao
- Liang Chen
- Ze Q. Xie
- Ji Y. Jian
- Pan P. Sun
journal: Saudi Medical Journal
year: 2022
pmcid: PMC10043917
doi: 10.15537/smj.2022.43.11.20220460
license: CC BY 4.0
---
# Clinical significance of CA-125 in elderly patients with active pulmonary tuberculosis
## Body
Tuberculosis (TB) is a communicable bacterial disease that typically affects the lungs. As one of the top 10 causes of mortality in the world, it has been reported that approximately 10 million people suffered from TB in 2019 across the globe, of which 1.4 million people succumbed to the disease. 1 While the disease can affect any age group, TB in older people is becoming a major public health and clinical concern, particularly in light of the aging global population. TB-related morbidity in older adults is higher than that in other 10-year age interval. 2 Despite the availability of effective treatment, the proportion of elderly patients with active TB is still high and even rising in certain countries. 3,4 Furthermore, this rise in disease incidence is disproportionately greater than the overall increase in the elderly population of the world. 3,4 The reported incidence of TB in older people ranges from 13.2 per 100,000 people (Australia) to as high as 2194 cases per 100,000 (Cambodia) in some Asia-Pacific countries, which is several fold than that in the younger population. 4 According to an epidemiological survey of TB in China, the incidence of TB increased with age, peaking in the 75-79 years age group at 866 cases per 100,000 people. 5,6 Elderly patients with PTB experience atypical clinical symptoms and have a more complicated and longer disease course, a greater diversity of chest imaging findings with more extensive lesion distribution, and higher rates of misdiagnosis and missed diagnosis. 7 Additionally, adverse reactions to anti-tubercular agents are more likely to occur in elderly PTB patients than in other age groups. Since early detection of PTB in the elderly is clinically challenging, it leads to poorer treatment outcomes and a potential increase in mortality. Therefore, rapid and accurate diagnosis is of paramount importance for treating and preventing the further spread of the disease.
Chest radiographic examination, acid-fast bacilli (AFB) staining, and *Mycobacterium culture* are the most commonly used approaches to diagnose pulmonary tuberculosis (PTB). Although clinically efficient, these methods have concerns regarding sensitivity and variability. Other more sensitive and rapid molecular detection methods require advanced laboratory equipment and are more expensive, thereby limiting their application in resource-limited regions. Notably, in some patients with PTB, AFB staining, sputum cultures, and deoxyribonucleic acid (DNA) testing for *Mycobacterium tuberculosis* (M.tb) may be negative or unavailable, necessitating the need for other diagnostic methods. In such patients, serological rapid diagnostic methods may be beneficial. One such accurate and rapid serological method, Interferon-Gamma Release Assay (IGRA), has been used to diagnose M.tb infection; however, the detection results have not been associated with treatment response and disease activity. 8,9 Besides IGRA, CA-125, a high molecular weight glycoprotein expressed in the epithelial cells of the endometrium and fallopian tubes and mesothelial cells of the peritoneum, pericardium, and pleura, has been used as a diagnostic marker for PTB. 10 Previous studies have reported an association between serum CA-125 and active PTB, suggesting its clinical significance in diagnosing PTB. However, other authors have expressed their concerns regarding the results being biased toward patients with serous effusions. 11 The use of CA-125 in the diagnosis of PTB in the elderly population has been poorly studied. 11 Therefore, we conducted this study to assess the clinical value of CA-125 levels in diagnosing elderly PTB.
## Abstract
### Objectives:
To assess the clinical significance of serum CA-125 levels in elderly patients with pulmonary tuberculosis (PTB).
### Methods:
We retrospectively analyzed 1613 participants–patients (aged ≥60 years) admitted to the Beijing Shijitan Hospital, Beijing, China from February 2015 to January 2021 and healthy participants, divided into 4 groups: PTB (group 1), pulmonary malignancies (group 2), pulmonary non-malignant diseases (group 3), and healthy participants (group 4). Data concerning demographics, physical examination findings, computed tomography, histopathological examination, and laboratory tests for *Mycobacterium tuberculosis* and serum CA-125 levels were collected and analyzed.
### Results:
There were 720 healthy individuals and 893 patients in the study. The median levels and abnormal rates of CA-125 in groups 1 (42.5, $57.3\%$) and 2 (34.4, $49.5\%$) were higher than those in groups 3 (21.1, $29.2\%$) and 4 (8.6, $0.4\%$) ($p \leq 0.05$). The ordinal logistic regression analysis model revealed significant associations between CA-125 levels and PTB (OR and $95\%$ confidence interval [CI]: 2.749 (1.876-4.027)), hypoproteinemia [OR and $95\%$ CI: 1.519 (1.114-2.070)], serous effusion [OR and $95\%$ CI: 7.364 (5.346-10.143)], pulmonary malignancy [OR and $95\%$ CI: 2.206 (1.518-3.204)], respiratory failure [OR and $95\%$ CI: 3.216 (2.087-4.956)], and cor pulmonale [OR and $95\%$ CI: 2.990 (1.282-6.973)].
### Conclusion:
Although elevated CA-125 levels may serve as a potential marker for diagnosing PTB in the elderly, they are affected by multiple factors, including serous effusion. Hence, caution is warranted while using this marker.
## Methods
In this retrospective study, all participants were from the Beijing Shijitan Hospital, Beijing, China between February 2015 and January 2021 were enrolled using the following criteria: i) aged ≥60 years; ii) inpatients with complete clinical data available; iii) patients having active PTB as determined by at least 2 AFB-positive sputum smears, a positive Mycobacterium culture, or positive DNA testing for M.tb; iv) patients with pulmonary malignancy or non-malignant diseases diagnosed by clinical signs and symptoms, radiographic pulmonary abnormalities, and histopathology; v) healthy participants from health examination center of the Beijing Shijitan Hospital without respiratory diseases and any malignant diseases. Outpatients and patients with gynecologic conditions were excluded.
The study was carried out according to the principles of the Helsinki Declaration and was approved by the Ethics Committee of Beijing Shijitan Hospital, Beijing, China. The need for informed consent was waived off by the committee owing to the retrospective nature of the study.
All participants were classified into 4 groups: group 1–patients with newly diagnosed PTB and PTB combined with non-malignant pulmonary diseases; group 2–patients with pulmonary malignancies with or without concomitant non-malignant pulmonary diseases; group 3–patients with non-malignant pulmonary disorders other than PTB; and group 4–healthy participants. For this study, pulmonary non-malignant diseases included pneumonia, pulmonary emphysema, bronchial infection, pulmonary interstitial fibrosis, and chronic obstructive pulmonary disease (COPD). Each patient with PTB was randomly matched with 4 to 5 patients with pulmonary non-TB diseases and 4 to 5 healthy participants.
All patient data were extracted from the Hospital Information System of the Beijing Shijitan Hospital, Beijing, China. The data collection sheet included information about age, gender, current diseases, medical history, complications, and serum CA-125 levels. A value of ≥35 U/mL for serum CA-125 levels was considered abnormal.
## Statistical analysis
Datasets were analyzed by using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, N.Y., USA). Categorical variables were expressed as frequency (n) and percentages (%), and median [25th percentile (P25), 75th (P75)] or mean±standard deviation (x±s) was used for continuous variables. CA-125 levels were divided into 4 grades: <35 U/mL, 35–69 U/mL, 70–139 U/mL, and >140 U/mL, and factors influencing CA-125 levels were identified by the ordinal logistic model.
## Results
We enrolled a total of 1613 study participants, which included 720 healthy individuals (group 4) and 893 patients (groups 1 through 3). Group 1 included 20 patients with PTB and 137 patients with PTB complicated with non-malignant pulmonary diseases. Group 2 included 113 patients with pulmonary malignancies and 196 patients with pulmonary malignancies combined with non-malignant pulmonary diseases. Group 3 included 415 patients with non-malignant pulmonary diseases. In addition to groups 1, 2, and 3, two cases of PTB combined with pulmonary malignancies and ten cases of PTB complicated with pulmonary malignancies and non-malignant diseases were also enrolled in multivariate analysis. Figure 1 presents typical radiographic features of patients belonging to groups 1 to 3.
**Figure 1:** *- Representative radiographic figures of patients. A) Patients form group 1, without serous effusion, AFB: 3+, serum CA-135 level; B) Patient from group 1, with serous effusion, AFB: 2+, serim CA-125 level: 250.7 U/mL; C) Patient from group 2, without serous effucion, serum CA-125 level: 186.0 U/mL; D) Patient from group 2, with serous effusion, serum CA-125 level: 195.5 U/mL; E) Patient fromo group 3, wihtout serous effusion, serum CA-125 level: 7.1 U/mL; F) Patient from gruop 3, with serous effusion, serum CA-125 level: 120.0 U/mL. AF: acid-fats bacilli.*
## Group-wise comparison of demographic characteristics and CA-125 levels
In all participants (in groups 1 to 4), the differences in gender, age, and levels and abnormal rate of serum CA-125 among the 4 groups were statistically significant (Table 1).
**Table 1**
| Group | Gender (n) | Gender (n).1 | Age (x±s) | CA-125 [Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| --- | --- | --- | --- | --- | --- |
| Group | Male | Female | Age (x±s) | CA-125 [Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| Group 1 | 101 | 56 | 76.8±10.5 | 42.5 (21.0, 86.2) | 57.3 |
| Group 2 | 204 | 105 | 69.1±8.2 | 34.4 (14.0, 105.4) | 49.5 |
| Group 3 | 234 | 181 | 77.4±10.1 | 21.1 (12.7, 40.5) | 29.2 |
| Group 4 | 445 | 275 | 69.0±8.1 | 8.6 (1.2, 12.1) | 0.4 |
| Total test statistic | 7.806 | 7.806 | 104.106 | 735.183 | 444.372 |
| Total p-value | 0.05 | 0.05 | <0.001 | <0.001 | <0.001 |
| Group 1 versus group 2 | χ 2 =0.131, p=0.717 | χ 2 =0.131, p=0.717 | t=7.670, p p <0.001 | H=101.997, p=0.146 | χ 2 =2.545, p=0.111 |
| Group 1 versus group 3 | χ 2 =2.964, p=0.085 | χ 2 =2.964, p=0.085 | t=0.625, p=0.454 | H=223.156, p<0.001 | χ 2 =38.822, p<0.001 |
| Group 1 versus group 4 | χ 2 =0.350, p=0.554 | χ 2 =0.350, p=0.554 | t=7.778, p<0.001 | H=757.531, p<0.001 | χ 2 =440.334, p<0.001 |
| Group 2 versus group 3 | χ 2 =6.879, p=0.009 | χ 2 =6.879, p=0.009 | t=8.295, p<0.001 | H=121.160, p=0.003 | χ 2 =31.207, p<0.001 |
| Group 2 versus group 4 | χ 2 =1.648, p=0.199 | χ 2 =1.648, p=0.199 | t=0.109, p=0.858 | H=655.535, p<0.001 | χ 2 =405.222, p<0.001 |
| Group 3 versus group 4 | χ 2 =3.218, p=0.073 | χ 2 =3.218, p=0.073 | t=8.403, p<0.001 | H=534.375, p<0.001 | χ 2 =223.448, p<0.001 |
Additionally, the patients were further divided into groups based on the presence or absence of serous effusion along with the disease. In participants with serous effusion (in groups 1 to 3), the differences in gender, age, and levels and abnormal rate of serum CA-125 among the 3 groups were statistically significant (Table 2).
**Table 2**
| Group | Gender (n) | Gender (n).1 | Age (x±s) | CA-125[Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| --- | --- | --- | --- | --- | --- |
| Group | Male | Female | Age (x±s) | CA-125[Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| Group 1 | 43 | 12 | 78.7±10.2 | 71.2 (38.8, 173.9) | 81.8 |
| Group 2 | 73 | 55 | 71.0±8.9 | 99.8 (44.1, 175.4) | 79.7 |
| Group 3 | 56 | 47 | 80.9±9.8 | 52.0 (24.7, 107.4) | 62.1 |
| Total test statistic | 9.415 | 9.415 | 34.109 | 13.182 | 11.364 |
| Total p-value | 0.009 | 0.009 | <0.001 | 0.001 | 0.003 |
| Group 1 versus group 2 | χ 2 =7.415, p=0.006 | χ 2 =7.415, p=0.006 | t=7.717, p<0.001 | H=14.551, p=0.826 | χ 2 =0.111, p=0.740 |
| Group 1 versus group 3 | χ 2 =8.689, p=0.003 | χ 2 =8.689, p=0.003 | t=2.233, p=0.159 | H=25.100, p=0.208 | χ 2 =6.492, p=0.011 |
| Group 2 versus group 3 | χ 2 =0.164, p=0.685 | χ 2 =0.164, p=0.685 | t=9.950, p<0.001 | H=39.651, p=0.001 | χ 2 =8.695, p=0.003 |
Likewise, in participants without serous effusion (in groups 1 to 4), the differences in gender, age, and levels and abnormal rate of serum CA-125 among the 4 groups were statistically significant (Table 3).
**Table 3**
| Group | Gender (n) | Gender (n).1 | Age (x±s) | CA-125[Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| --- | --- | --- | --- | --- | --- |
| Group | Male | Female | Age (x±s) | CA-125[Median (P25, P75)] | Abnormal rate of CA-125 (%) |
| Group 1 | 58 | 44 | 75.8±10.5 | 31.2 (18.1-68.3) | 44.1 |
| Group 2 | 131 | 50 | 67.8±7.3 | 18.0 (10.7-40.0) | 28.2 |
| Group 3 | 178 | 134 | 76.3±10.0 | 17.6 (11.3-30.1) | 18.3 |
| Group 4 | 445 | 275 | 69.0±8.1 | 8.6 (1.2, 12.1) | 0.4 |
| Total test statistic | 12.601 | 12.601 | 69.316 | 487.826 | 250.03 |
| Total p-value | 0.006 | 0.006 | <0.001 | <0.001 | <0.001 |
| Group 1 versus group 2 | χ 2 =7.077, p=0.008 | χ 2 =7.077, p=0.008 | t=7.956, p<0.001 | H=183.969, p=0.001 | χ2=7.396, p=0.007 |
| Group 1 versus group 3 | χ 2 =0.001, p=0.973 | χ 2 =0.001, p=0.973 | t=0.491, p=0.619 | H=198.197, p<0.001 | χ2=27.661, p<0.001 |
| Group 1 versus group 4 | χ 2 =0.919, p=0.338 | χ 2 =0.919, p=0.338 | t=6.754, p<0.001 | H=613.759, p<0.001 | χ2=310.316, p<0.001 |
| Group 2 versus group 3 | χ 2 =11.499, p=0.001 | χ 2 =11.499, p=0.001 | t=8.447, p<0.001 | H=14.228, p=1.000 | χ2=6.572, p=0.010 |
| Group 2 versus group 4 | χ 2 =7.008, p=0.008 | χ 2 =7.008, p=0.008 | t=1.202, p=0.096 | H=429.790, p<0.001 | χ2=197.834, p<0.001 |
| Group 3 versus group 4 | χ 2 =2.056, p=0.152 | χ 2 =2.056, p=0.152 | t=7.245, p<0.001 | H=415.562, p<0.001 | χ2=126.692, p<0.001 |
## Factors influencing the CA-125 levels
The association between serum CA-125 levels and patients’ gender, age, diagnoses, and complications were analyzed using an ordinal logistic regression model; the factors and assignment are shown in Table 4. There was no collinearity among the variables (the tolerance of each variable >0.1, variance inflation factor <5), and the test of parallel lines revealed that the regression equations were parallel to each other (χ2=50.411, $$p \leq 0.537$$), so the present data could be analyzed by ordinal logistic regression. Based on this, we found that significant associations exist between serum CA-125 levels and PTB, hypoproteinemia, serous effusion, pulmonary malignancy, respiratory failure, and cor pulmonale (Table 5).
## Discussion
Typically, the presence of M.tb or its DNA in respiratory samples is used to confirm the diagnosis of PTB. However, in some patients, M.tb may not be detectable in the respiratory sample, or a sample may not be available; in these patients, serological tests, such as the detection of CA-125, can be used to assist the diagnostic process.
Owing to the physiological aging of different organs and a concomitant decline in immunity, PTB in the elderly is associated with several complications and atypical symptoms. 7 The primary clinical symptoms and signs of PTB in elderly patients include cough, expectoration, hemoptysis, weight loss, night sweats, fatigue, dyspnea, and chest pain. 4,12 However, these clinical manifestations are non-specific, and a similar clinical picture may be encountered in pulmonary malignancies or non-malignant pulmonary diseases other than PTB. Furthermore, the chest imaging findings in the elderly may not be typical of PTB in terms of the location and pattern of the lesions, and the sensitivity of bacteriological examination is low and variable. 13-15 Notably, it is also difficult for elderly PTB patients to produce high-quality respiratory specimens for bacteriological detection. Previous studies have stated that, in the elderly, the sensitivity of tuberculin skin test (TST) in diagnosing latent TB infection (LTBI) is lower, and there are more indeterminate results for IGRA in diagnosing LTBI. 13-16 In addition, the positivity rate of IGRA in the diagnosis of TB is lower in older adults than in young people, and the sensitivity decreases with age. 17,18 In the present study, we assessed the clinical value of serum CA-125 levels in elderly PTB patients (≥60 years old). We found that the median levels and abnormal rates of serum CA-125 were higher in PTB patients and in patients with pulmonary malignancies than in patients with non-malignant pulmonary disorders and in healthy participants ($p \leq 0.05$). However, the differences in level and abnormal rate of serum CA-125 between PTB patients and patients with pulmonary malignancies were not statistically significant ($p \leq 0.05$). We also found that in participants with serous effusion, the median level and abnormal rate of serum CA-125 were higher in patients with pulmonary malignancies than in patients with non-malignant pulmonary disorders ($p \leq 0.05$). However, the differences in serum CA-125 levels between PTB patients and patients with pulmonary malignancies, and between PTB patients and patients with non-malignant pulmonary disorders were not statistically significant ($p \leq 0.05$). The abnormal rate of serum CA-125 in PTB patients was also higher than that in patients with non-malignant pulmonary disorders ($p \leq 0.05$); likewise, the difference between PTB patients and patients with pulmonary malignancies was also not statistically significant ($p \leq 0.05$). In participants without serous effusion, the median level and abnormal rate of serum CA-125 were also higher in PTB patients than in the other 3 groups ($p \leq 0.05$). These results indicate that serum CA-125 levels were elevated in elderly patients with PTB, and CA-125 test may be beneficial for diagnosing elderly PTB; however, accurate detection may be influenced by serous effusion. In addition, using different classifications (study cohort as a whole, with serous effusion, and without serous effusion), the differences in gender and age were statistically significant among all groups, which seemed to influence the CA-125 levels. However, our subsequent multivariate analysis suggested that gender and age were not the influencing factors for elevated CA-125 levels.
CA-125 was initially identified by molecular cloning as a high molecular weight glycoprotein that promotes ovarian cancer cell growth and is also present in the epithelium of tracheal, bronchial, bronchiolar, terminal bronchioles, in the glands of trachea and bronchi, and the mesothelium of pleura. 19,20 Previous studies have highlighted the potential role of CA-125 levels in diagnosing PTB and monitoring the therapeutic efficacy of anti-TB treatment; notably, these levels were also associated with the severity of PTB. 10,11,21 A possible explanation for these high levels of CA-125 in PTB patients is that PTB is associated with the destruction of bronchial epithelial cells, which induces increased CA-125 levels in these patients. However, some studies also reported a lower proportion ($38\%$-$48.7\%$) of PTB patients with high levels of CA-125, indicating that the levels of CA-125 may not necessarily be elevated in most PTB patients. 11 Du et al 10 immunohistochemically stained CA-125 in 6 tissue specimens from patients with active PTB and revealed that 3 specimens ($50\%$) were positive for CA-125. In our study, $81.8\%$ of PTB patients with serous effusion had abnormal CA-125 levels, while only $44.1\%$ of PTB patients without serous effusion had elevated levels. Therefore, caution is warranted when applying CA-125 detection methods for diagnosing PTB in the elderly.
In addition to the significant effect of serous effusion and PTB on the CA-125 levels, we also found that hypoproteinemia, pulmonary malignancies, respiratory failure, and cor pulmonale may elevate the levels of CA-125 in elderly patients; however, age and gender did not influence these levels. Since CA-125 is expressed in the mesothelial cells of the peritoneum and pericardium, increased CA-125 levels can also be observed in diseases involving the gastrointestinal and circulatory systems. Therefore, serous effusions from those structures may have led to elevated CA-125 levels. 11,22,23 Adenocarcinomas and diabetes mellitus may also elevate CA-125 levels. 10 *In this* study, pulmonary malignancies were found to elevate serum CA-125 levels, but no such association was seen in diabetic patients. Further research exploring the association between hypoproteinemia, respiratory failure, cor pulmonale, and CA-125 is needed.
## Study limitations
Since the study was a retrospective analysis, it may suffer from selection bias. The levels of CA-125 were not monitored during and after treating patients with PTB or pulmonary malignancies, which may weaken the said association between CA-125 and PTB. Lastly, there may be other factors affecting CA-125 levels that were not included in the analysis such as multiple lobar lesions. 10 In conclusion, the findings confirm that the levels and abnormal rates of CA-125 in elderly people with PTB are significantly higher than those observed in elderly people with other pulmonary diseases. These results highlight the use of serum CA-125 in diagnosing PTB in older adults and that the combined application of serum CA-125 and IGRA may further improve diagnostic efficiency. However, the rates of abnormal CA-125 levels were not as high in elderly PTB patients without serous effusions as anticipated (only $44.1\%$) since CA-125 levels can be influenced by many factors. Therefore, despite its potential clinical significance in the diagnosis of PTB in the elderly, it is necessary to be cautious when incorporating CA-125 levels into clinical practice.
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|
---
title: Correlating excessive daytime sleepiness with body mass index, waist circumference,
and lipid profile in shift workers
authors:
- Diah Kurnia Mirawati
- Naomi Ditya Sari
- Ervina Arta Jayanti Hutabarat
- Yetty Hambarsari
- Hanindia Riani Prabaningtyas
- Pepi Budianto
- Muhammad Hafizhan
- Stefanus Erdana Putra
journal: Saudi Medical Journal
year: 2022
pmcid: PMC10043919
doi: 10.15537/smj.2022.43.11.20220529
license: CC BY 4.0
---
# Correlating excessive daytime sleepiness with body mass index, waist circumference, and lipid profile in shift workers
## Body
Sleep is essential for human health and wellbeing. The sleep-wake cycle is regulated by a complex circadian rhythm, which is repeated approximately every 24 hours and is influenced by light and hormonal changes. Circadian rhythm disturbances can be caused by intrinsic or extrinsic factors. Intrinsic circadian rhythm disturbances are caused by dysregulation of the internal circadian system due to genetic disorders, resulting in asynchrony between the patient’s sleep/wake cycle and the day/night cycle. In extrinsic circadian rhythm disorders, sleep/wake cycle disturbances are associated with activities such as night shift work or trans-meridian travel. 1 Shift work is a main cause of circadian rhythm disturbance and sleep deprivation. In the United States, 22 million adults work in shifts, 3.8 million of whom work on regular night shifts. Healthcare workers, including nurses, are required to work on a shift-based schedule to provide 24-hour care for patients. Shift work creates a serious public health problem and negatively affects the quality of life. Shift work can lead to insomnia, sleep insufficiency, psychological stress, increased risk of work and traffic accidents, decreased social interactions, and excessive daytime sleepiness (EDS). 2,3 Excessive daytime sleepiness is a condition characterized by intense sleepiness with a general lack of energy during the day, resulting in disturbances in work performance, behavior, and quality of life, as well as an increased rates of workplace accidents. 4 Sleep deprivation also increases the risk of several diseases such as gastrointestinal disorders, cardiovascular disorders, depression, and cancer. Furthermore, it can cause metabolic disorders since sleep plays an important role as a modulator of neuroendocrine function and glucose metabolism. Sleep deprivation is associated with impaired glucose tolerance and impaired regulation of ghrelin and leptin, which regulate appetite. Thus, sleep insufficiency can cause weight gain and obesity. 2,5 A study of 500 participants in Italy revealed that obesity, elevated cholesterol levels, and elevated triglyceride levels, were significantly increased in shift workers compared to the corresponding levels in regular daytime workers. Another study also found that shift workers had significantly higher body mass indices (BMIs) than those of their colleagues working only during the day. Furthermore, the prevalence of diabetes mellitus and markers of insulin resistance were more frequent in shift workers than in daily workers. 6,7 This study aimed to understand the effect of shift work by analyzing the correlation among EDS, BMI, waist circumference, and lipid profile of shift workers at Dr. Moewardi General Hospital, Surakarta, Idonesia.
## Abstract
### Objectives:
To determine the correlation between excessive daytime sleepiness and body mass index, waist circumference, and lipid profile of shift workers at Dr. Moewardi General Hospital, Surakarta.
### Methods:
This cross-sectional study was carried out at the Dr. Moewardi Hospital, Surakarta, Indonesia between October 2018 and July 2019. The participants were recruited using purposive sampling. Multiple linear regression with backward elimination was performed to identify the odds ratios between Epworth Sleepiness Scale scores, anthropometric measurements, and lipid profiles. A p-value of <0.05 indicated statistically significant correlations.
### Results:
Of the 150 included participants, 127 ($84.67\%$) were women. Statistical analyses revealed odds ratios of 2.38 ($95\%$ confidence interval [CI] 1.14-4.89, $$p \leq 0.000$$) for daytime sleepiness severity and total cholesterol levels, and 2.45 ($95\%$ CI 1.36-4.98, $$p \leq 0.020$$) for daytime sleepiness severity and high-density lipoprotein levels.
### Conclusion:
Increased total cholesterol and decreased high-density lipoprotein levels increase the risk of excessive daytime sleepiness in shift workers.
## Methods
This cross-sectional study was carried out at Dr. Moewardi General Hospital, Surakarta, Indonesia. The participants in this study were nurses who worked in wards, emergency rooms, and intensive care units. The inclusion criteria were male or female nurses, aged 20-50 years old, work in a shift schedule for at least one year, and agree to participate in this study. The exclusion criteria were the presence of diabetes mellitus, dyslipidemia, metabolic syndrome, hypertension, heart disease, obstructive sleep apnea, narcolepsy, depression, epilepsy and thyroid disease in the participants, as well as post-menopausal nurses.
Excessive daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS), which comprises 8 questions that evaluate participant sleepiness in various daily settings. Participants were asked to rate every question on a 4-point scale, from 0 to 3, regarding their chances of experiencing sleepiness in a daily setting. A higher score indicates more severe daytime sleepiness. The duration of shift work was classified as <10 or ≥10 years. Patients were classified into non-EDS (ESS ≤10), mild-to-moderate EDS (11 ≤ ESS ≤ 16), and severe EDS (ESS ≥17) groups. 7,8 Body height and weight were calculated to determine the BMIs. Body mass indices <18.5 kg/m 2 are classified as underweight; BMIs of 18.5–24.9 kg/m 2 are classified as normo-weight; BMIs of 25.0–29.9 kg/m 2 are classified as overweight; and BMIs ≥30 kg/m 2 are classified as obese. 9 We also measured the participants’ waist circumferences and used cutoff values >90 cm for men and >80 cm for women, to be considered abnormal. 10 Laboratory parameters analyzed in this study were lipid profile, consisting of total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides with normal ranges of <200, <130, >40, and <150 mg/dL, respectively. 11 Laboratory results were obtained by a certified pathologist.
Data distribution was analyzed using the Kolmogorov–Smirnov test. The correlation between shift work durations and ESS scores was determined using Spearman correlation analysis. Correlations between EDS and BMI, waist circumference, and lipid profile were then analyzed using multiple linear regression to determine the odds ratio (ORs). Variables that were statistically significant in the linear regression were then further analyzed with backward elimination to determine the determination coefficient.
## Statistical analysis
Statistical significance was set at a p-value of <0.05. All analyses were performed using SPSS version 22 (IBMCorp, Armonk, NY, USA).
## Results
This study was conducted from October, 2018 to July, 2019 at the Dr. Moewardi General Hospital, Surakarta, Indonesia. The total sample size was 154, and 4 participants were excluded from this study. The participants were nurses working in wards, emergency rooms, and intensive care units working in shifts. A total of 150 nurses were assessed for EDS using ESS, BMI, waist circumference, and lipid profiles. The majority of participants were women ($84.7\%$), with a mean age of 32.58±6.003 (range 23-48) years. The Kolmogorov–Smirnov test was used to determine the data distribution. The participants’ demographics, ESS scores, anthropometric data, laboratory data, and data distribution are detailed in Table 1.
**Table 1**
| Characteristic | Case | Case.1 | Descriptive | Descriptive.1 | Descriptive.2 | Descriptive.3 |
| --- | --- | --- | --- | --- | --- | --- |
| Characteristic | n | % | Min | Max | Mean± D | P-value |
| Gender | | | | | | |
| Male | 23 | 15.3 | | | | |
| Female | 127 | 84.7 | | | | |
| Age | | | 23 | 48 | 32.59±6.003 | |
| Duration of shift work | | | | | | |
| <10 years | 68 | 45.3 | 1 | 25 | 7.98±5.182 | |
| ≥10 years | 82 | 54.7 | 1 | 25 | 7.98±5.182 | |
| ESS Score | | | | | | |
| No EDS | 129 | 86 | 0 | 19 | 4.59±4.154 | 0.000 |
| EDS | 21 | 14 | 0 | 19 | 4.59±4.154 | 0.000 |
| BMI | | | | | | |
| Underweight | 11 | 7.3 | 16.0 | 33.8 | 23.84±3.669 | 0.003 |
| Normal | 93 | 62.0 | 16.0 | 33.8 | 23.84±3.669 | 0.003 |
| Overweight | 33 | 22.0 | 16.0 | 33.8 | 23.84±3.669 | 0.003 |
| Obesity | 13 | 8.7 | 16.0 | 33.8 | 23.84±3.669 | 0.003 |
| Waist circumference | | | | | | |
| Normal | 63 | 42 | 67 | 116 | 85.39±10.01 | 0.033 |
| Abnormal | 87 | 58 | 67 | 116 | 85.39±10.01 | 0.033 |
| Total cholesterol | | | | | | |
| Normal | 115 | 76.7 | 121 | 299 | 181.08±31.39 | 0.011 |
| High | 35 | 23.3 | 121 | 299 | 181.08±31.39 | 0.011 |
| LDL | | | | | | |
| Normal | 103 | 68.7 | 70 | 212 | 118.88±29.27 | 0.001 |
| High | 47 | 31.3 | 70 | 212 | 118.88±29.27 | 0.001 |
| HDL | | | | | | |
| Normal | 135 | 90 | 12 | 86 | 52.84±10.73 | 0.200* |
| Low | 15 | 10 | 12 | 86 | 52.84±10.73 | 0.200* |
| Triglyceride | | | | | | |
| Normal | 143 | 95.3 | 32 | 290 | 88.28±41.58 | 0.000 |
| High | 7 | 4.7 | 32 | 290 | 88.28±41.58 | 0.000 |
From the data distribution test, HDL was normally distributed with $$p \leq 0.200$$, while ESS, BMI, waist circumference, total cholesterol, LDL, and triglyceride data were not normally distributed. Spearman’s correlation analysis between the duration of shift work and ESS revealed a correlation coefficient of 0.678 ($$p \leq 0.002$$). In the linear regression analysis, the total cholesterol and HDL levels were statistically significant, indicating that these parameters were the most important variables correlated with EDS. We identified p-values of 0.000 and 0.040 for the total cholesterol and HDL levels, respectively. We then performed further analysis with backward elimination to determine the determination coefficient between EDS, total cholesterol, and HDL. Odds ratio for daytime sleepiness severity and total cholesterol level were 2.38 ($95\%$ Confidence Interval [CI] 1.14-4.89, $$p \leq 0.000$$), and ORs for daytime sleepiness severity and HDL level were 2.45 ($95\%$ CI 1.36-4.98, $$p \leq 0.020$$). The linear regression analysis and backward elimination results are presented in Table 2. The coefficient of determination in the multiple linear regression analysis was 0.583, indicating that $41.7\%$ of daytime sleepiness severity was determined by factors other than total cholesterol and HDL levels.
**Table 2**
| Variable | B Coefficient | SE (β) | P-value | ORs (95% CI) |
| --- | --- | --- | --- | --- |
| First analysis | | | | |
| BMI | 0.14 | 0.012 | 0.165 | 2.53 (1.29-4.97) |
| Waist circumference | 0.085 | 0.005 | 0.423 | 2.19 (1.11-4.34) |
| Total cholesterol | 0.833 | 0.004 | 0.0 | 2.12 (1.08-4.18) |
| HDL | -0.641 | 0.001 | 0.04 | 2.23 (1.19-4.23) |
| LDL | 0.508 | 0.002 | 0.612 | 1.87 (1.01-4.10) |
| Triglyceride | 0.098 | 0.069 | 0.286 | 2.02 (1.09-4.31) |
| Second analysis | | | | |
| Total cholesterol | 0.791 | 0.002 | 0.0 | 2.38 (1.14-4.89) |
| HDL | -0.116 | 0.001 | 0.02 | 2.45 (1.36-4.98) |
## Discussion
A survey by the Ministry of Health, Republic of Indonesia, in 2018 revealed that $71\%$ of nurses are female,which was represented in our study population. 12 The present study identified a $14\%$ prevalence of EDS with an average ESS score of 4.59±4.15. The prevalence of EDS in the young adult population varies greatly between studies; however, approximately $20\%$ of the young adult population reportedly suffers from EDS. 4 The prevalence of overweight in this study was $22\%$ and obesity was $8.7\%$, similar to the prevalence of overweight in the general population in Indonesia, based on Indonesian National Health Research, which was $19.7\%$ in men and $32.9\%$ in women. 13 The prevalence of overweight in this study was lower that reported by Gadallah et al 14 who evaluated the relationship between shift work and lipid profiles in 86 nurses at the Egyptian University Hospital, identifying a prevalence of $41.9\%$. Furthermore, the prevalence of an abnormal waist circumference was $58\%$ ranging from to 67–116 cm. Approximately $42\%$ of participants had a normal waist circumference, similar to the values reported by Marqueze et al 15 evaluating the effect of shift work on cardiovascular risk factors in 30 truck drivers in Brazil, where the prevalence of an abnormal waist circumference was $42.3\%$.
In this study, 2 anthropometric measurements, BMI and waist circumference, were used to assess the proportion of body fat. This study revealed that the prevalence of an abnormal waist circumference was almost twice as high as the prevalence of overweight BMI. This is because BMI, unlike waist circumference, does not reflect the body fat distribution. The American Heart Association, National Heart, Lung, and Blood Institute, and the International Diabetes Federation recommend the use of waist circumference as a screening tool for diagnosing metabolic syndrome. Waist circumference can also serve as an indicator of central obesity and a better predictor of cardiovascular diseases compared to BMI. 16 However, the WHO still recommends measuring both BMI and waist circumference to assess metabolic syndrome. 9 *In this* study, $23.3\%$ of the participants had total cholesterol levels >200 mg/dL. The total cholesterol level in our study ranged from 121-299 mg/dL with an average of 185.08±31.4 mg/dL, which was higher than the values reported by other studies, which reported high total cholesterol prevalence values of approximately $21.7\%$. 15 Moreover, $31.3\%$ of the present study’s participants had high LDL levels, which conformed with Marqueze’s et al 15 study, who found high LDL levels in approximately $34.8\%$ shift worker. A study carried out at a teaching hospital in Egypt, attempting to understand the relationship between shift work and lipid profile in 86 nurses, found high LDL levels in $43\%$ of participants. High LDL levels indicate high tissue cholesterol transport, which increases the risk of cardiovascular and cerebrovascular diseases. 14 Additionally, $10\%$ of the present study’s participants had low HDL levels. The mean HDL level in our study was 52.84±10.73 (range 12-86) mg/dL. This percentage is much lower than that reported by Marqueze et al 15 on shift workers, who reported a low prevalence of HDL (approximately $34.8\%$). Similarly, another study on the relationship between shift work and lipid profile in 86 nurses found that $30.2\%$ of the participants had low HDL. 14 High-density lipoprotein is a lipoprotein that transports excess lipids from the tissues to the liver for excretion and reuse. Low HDL levels increase the risk of heart disease, hypertension, and cerebrovascular diseases. The risk of coronary heart disease and hypertension increases by 2 to $3\%$ for every 1.0 mg/dL decrease in HDL cholesterol levels. 17 High triglyceride levels were found in $4.7\%$ of participants. The average triglyceride was 52.84±10.73 mg/dL, ranging from 32 to 290 mg/dL. This prevalence is lower than the high triglyceride prevalence of $30.4\%$ in shift workers reported by Marqueze ey al. 15 A higher prevalence of high triglyceride levels in shift workers was reported by Gadallah et al 14 in Egypt ($59.3\%$) and Biggi et al 18 in Italy ($41.5\%$).
Spearman correlation analysis revealed a correlation coefficient of 0.678 ($$p \leq 0.002$$), indicating a strong correlation between shift work duration and ESS. Sleep is vital for proper functioning in humans. Many shift workers have irregular sleep schedules, which have many consequences such as impacting working and learning performance. The sleep-wake cycle is regulated by the circadian rhythm, a 24-hour cycle that is part of the human internal clock. Light exposure during the day triggers the central clock in the suprachiasmatic nuclei of the hypothalamus and suppresses the pineal gland to reduce melatonin secretion. At night, shift workers are exposed to light, which disrupts their circadian rhythm. Unmet sleep needs lead to sleepiness after night shifts. Workers performing long shifts, which are greater than 12 hours, tend to experience greater sleepiness and performance impairment. 19,20 Our multiple regression analysis revealed that the total cholesterol level was statistically significant with p-value of 0.000 and HDL level was statistically significant with p-value of 0.040. This finding indicates that these parameters are the most important variables correlated with the EDS. We further analyzed these data with multiple linear regression to determine the ORs. The ORs for daytime sleepiness severity and total cholesterol levels were 2.38 ($95\%$ CI 1.14-4.89, $$p \leq 0.000$$), indicating that increased total cholesterol level is associated with 2.38-fold increased risk of daytime sleepiness. Furthermore, the ORs for daytime sleepiness severity and HDL level were 2.45 ($95\%$ CI 1.36-4.98, $$p \leq 0.020$$) indicating that decreased HDL levels are associated with 2.45–fold increased risk of daytime sleepiness. Increasing levels of total cholesterol, LDL, and triglycerides, and decreased HDL levels in EDS are caused by decreasing energy expenditure due to alteration of sympathetic nerves, resulting in a decrease in leptin and changes in diet due to increasing ghrelin levels. Two possible mechanisms may cause an increase in the lipid profile in EDS. The first mechanism is the disruption of the circadian rhythm through impaired melatonin production due to sleep insufficiency. Disruption of melatonin production causes Circadian Locomotor Output Cycles Kaput protein deficiency, resulting in increased absorption of carbohydrates and fats in the intestines throughout the day, and triggers fat accumulation in the liver. 21,22 The second mechanism is a change in diet. Research has revealed that night shift workers have an appetite for foods that are high in simple carbohydrates and saturated fats. Moreover, shift workers tend to have low fiber and high saccharose intake, which may affect serum lipoprotein levels. 23 The coefficient of determination in the multiple linear regression analysis was 0.583, indicating that $41.7\%$ of daytime sleepiness severity was determined by factors other than total cholesterol and HDL levels. Several factors, such as sleep deprivation, insomnia, and female sex are more prone to EDS. Sleep problems such as talking, waking up, and teeth grinding also increase daytime sleepiness. 24,25 These findings underline the risk of dyslipidemia in nurses working on shift schedules. Shift schedules also pose risks to patient safety owing to decreased focus and executive function. To mitigate these risks, hospital administrators could arrange nurses’ shifts to take at least 2 days off after 2 consecutive night shifts, before starting a day shift. Chang et al 26 determined that one day off after 2 night shifts is insufficient to maintain visual attention performance and executive function ability. Another possible measure is to assign more nurses to night shifts, to enable nurses to take a nap during their shift. A recent study revealed that a quick, 30-minute nap during a night shift can result in less fatigue, less sleepiness, reduced medical errors, and increased psychomotor performance. 27-29
Due to the high comorbidity that causes EDS in shift workers, other factors that can worsen this condition must be considered. Further research should explore and analyze other influential factors, such as daily activities or stressful conditions that may cause EDS in shift workers.
## Study limitations
This study had some biases that might not have been excluded. We used a self-administered questionnaire, which could have been affected by recall bias. Moreover, the sample size was relatively small; therefore, analyses based on gender could not be performed. Further research following a longitudinal cohort design may be able to include more samples because the number of shift workers is large.
In conclusion, increased total cholesterol and decreased HDL levels were associated with an increased risk of EDS in shift workers at the Dr. Moewardi Hospital Surakarta.
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|
---
title: 'Accurate Post-Calibration Predictions for Noninvasive
Glucose Measurements in People Using Confocal Raman Spectroscopy'
authors:
- Anders Pors
- Kaspar G. Rasmussen
- Rune Inglev
- Nina Jendrike
- Amalie Philipps
- Ajenthen G. Ranjan
- Vibe Vestergaard
- Jan E. Henriksen
- Kirsten Nørgaard
- Guido Freckmann
- Karl D. Hepp
- Michael C. Gerstenberg
- Anders Weber
journal: ACS Sensors
year: 2023
pmcid: PMC10043934
doi: 10.1021/acssensors.2c02756
license: CC BY 4.0
---
# Accurate Post-Calibration Predictions for Noninvasive
Glucose Measurements in People Using Confocal Raman Spectroscopy
## Body
In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Among non-invasive technologies such as electrical, thermal, acoustical, and optical methodologies, light offers the least intrusive probing of all technologies investigated. Raman spectroscopy in the near infrared has shown a consistent path of improvement, driven by advances in lasers, optics, detectors, and algorithms. Furthermore, direct manifestation of physiological glucose in *Raman spectra* has been demonstrated, testifying that Raman spectroscopy measures glucose in skin at physiological concentrations.1,2 Despite these encouraging trends, a clinically useful embodiment of this method has not yet materialized.3,4 Accuracy, calibration stability, and general robustness have been persistent challenges for non-invasive glucose monitoring.3−5 Chemometrics and machine learning algorithms are generally used to build multivariate regression models that are subsequently used to predict the glucose concentration. Most attempts, irrespective of the underlying technology, involve brief study periods, typically not more than a few hours, under controlled conditions, and the demarcation between calibration and validation has often not been distinct.6−8 It has not been demonstrated whether these encouraging in-clinic results, acquired under controlled and supervised conditions, can be generalized to real-life conditions, extended measurement periods, and usage by lay person.
We have successfully bridged the gap from technical proof of principle to a safe and reliable device, which can be operated by non-specialists at home. Previously, we reported our first successful development of a Raman spectroscopic prototype in persons with diabetes and described a critical depth for the confocal glucose determination in human skin9 and the performance during glucose challenge.10 In the latter study, the prototype demonstrated glucose kinetics akin to invasive continuous glucose monitors,11 thus suggesting glucose measurements in the interstitial compartment. The glucose in the interstitial fluid arrives primary through diffusion from the capillaries and, thus, represents a time-delayed version of the blood counterpart.12 The use of the interstitial compartment may influence the measurement accuracy, but it is generally not considered a significant obstacle for practical glucose monitoring.13 The purpose of this paper is to present the key elements of the prediction algorithm development, the clinical evidence of the performance and calibration stability, and the utility of this serially manufactured Raman non-invasive glucose monitoring (NIGM) device in subjects with type 1 and type 2 diabetes on insulin therapy.
## Abstract
In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with $99.8\%$ of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of $14.3\%$. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.
## Design of the
Non-Invasive Glucose Sensor
The sensor for non-invasive glucose determination is depicted in Figure 1a, where the hand is positioned as intended during use. The top cover functions both as a mechanical safety feature (for laser light irradiation) and screening of external light during measurements. It is worth noting that the sensor is portable, battery-driven, and with built-in safety measures, graphical user interface, and Wi-Fi connectivity. Moreover, the sensor is safe in use, which is corroborated by the fact that no serious reactions or scarring of the skin of the thenar (base of the thumb) was observed during the extensive clinical study.
**Figure 1:** *Non-invasive glucose sensor. (a) Novel,
production-ready, portable,
stand-alone, and Raman-based device configured for NIGM. (b) Schematic
optical layout. (c) Examples of recorded thenar spectra from five
subjects with different skin colors, according to the Fitzpatrick
scale, where type I and type V correspond to the lightest and the
darkest skin complexions, respectively. Spectra are vertically offset
for clarity.*
The sensor’s optical components are presented schematically in Figure 1b and described in detail in the Materials and Methods section. Essentially, the optical hardware functions as a confocal near-infrared Raman spectrometer that is configured for maximum spatial sensitivity at 280 μm away from the glass window with a sensitivity profile featuring a full-width-at-half-maximum of 250 μm. With the thenar positioned on the window (Figure 1a), the confocal setup ensures that the backscattered Raman signal, arising from the interaction of the 830 nm laser illumination and the skin constituents, originates from the upper living skin layers (i.e., living epidermis and upper part of the dermis), while the signal from the dead outer skin layer (stratum corneum) is suppressed.9 Additionally, the confocality is helpful in reducing the dependency of the device-skin interface on the collected Raman signal, which amounts in more consistent Raman spectra.
The backscattered Raman signal is collected and dispersed by a spectrometer in the range of 300–1615 cm–1 with a spectral resolution of ∼10 cm–1. Figure 1c shows examples of recorded thenar *Raman spectra* from subjects with different skin colors (measured on a Fitzpatrick scale). It is comforting to see that despite slightly less pronounced Raman peaks for the darker skin colors (type IV and V), owing to an increased fluorescence background, the thenar spectra do not markedly differ, thus illustrating a relatively consistent response from all subjects. It is important to realize that the information of the physiological glucose concentrations resides in the thenar spectra, which can be quantified by the use of multivariate regression techniques. Details of the employed predictive algorithm, including pre-processing of spectra, detection of outliers, and training of calibration models, can be found in the Materials and Methods section.
## Maintenance of Calibration
For a sensor to be considered applicable for practical non-invasive glucose monitoring, it is necessary to show calibration stability. Thus, accuracy should not depend on frequent recalibration but remain stable over days and weeks. In the present work, we have achieved measurement stability over a period of 15 days after finalized calibration. Figure 2 shows the time course of the daily root-mean-squared error (RMSE), mean glucose measurement, and reference value in the validation phase of 15 days for all subjects at home or work, without close professional supervision. The measurement values are seen to closely follow the reference values within 0.2 mmol/L. In the entire 15 day validation period, the measurement accuracy remained stable, with only a slight increase in RMSE from 1.68 to 1.84 mmol/L, thus corresponding to a reduction in measurement accuracy of $9.5\%$.
**Figure 2:** *Calibration
stability. Comparison between the daily mean of the
measured and reference glucose value and the subject-wise, average
RMSE for the 160 subjects for a validation period of 15 days. The
bars on the RMSE curve represent the standard deviation.*
## Performance in Subjects with Type 1 and Type 2 Diabetes
The clinical study involved 160 subjects, 137 with type 1 on intensive insulin therapy or insulin pumps and 23 with type 2 diabetes on insulin or antidiabetic medication. For the first group with type 1 diabetes, the overall accuracy of measurements is given in the consensus error plot of Figure 3a, where $96.5\%$ of the points fall into zones A + B, while the typical indices of accuracy, the mean absolute relative difference (MARD) and RMSE, over the 15 days were $19.9\%$ and 1.9 mmol/L, respectively. For the cohort of type 2 diabetes subjects, NIGM measurements showed points within A + B in a consensus error grid, MARD, and RMSE of $99.8\%$, $14.3\%$, and 1.6 mmol/L, respectively. As shown in Table 1, RMSE and MARD were strongly dependent on the range of the glucose concentration. This is particularly emphasized by the MARD for the group of subjects with type 1 diabetes on intensive insulin therapy with the glucose values below 3.9 mmol/L (i.e., hypoglycemia). However, this is a feature of the MARD metric accentuating the performance in the lower glucose ranges.14 The total collective of 160 subjects of the study (all types of diabetes and forms of therapy) was grouped in age ranges, gender, and skin colors according to the scale of Fitzpatrick. As Table 2 shows, there are no major changes in the indices of performance for these parameters. In view of the limited numbers, these data need further confirmation.
**Figure 3:** *Measured
glucose concentrations plotted as a function of reference
values in a consensus error grid for all type 1 (a) and type 2 subjects
(b). The reference glucose value is obtained as the average of two
blood glucose measurements (Contour Next One, Ascensia), whereas the
corresponding glucose measurement is the result of the PLS regression
model applied to three pre-processed NIGM spectra.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## Individual Performance
The above results describe the performance of pooled data, acquired by uniting measurements from enrolled subjects. To assess the homogeneity of performance in the two subject collectives, histograms were established for subject-wise RMSE, as shown in Figure 4. Noticeable variation exists in RMSE, where subjects with type 1 diabetes feature an RMSE of 1.9 ± 0.5 mmol/L (mean ± standard deviation). The subjects with type 2 diabetes show a slightly more consistent performance, with an RMSE of 1.6 ± 0.4 mmol/L. It should be noted that with the available metadata at hand (such as gender, age, and skin color; see Table 2), we have only been able to establish a clear relation between performance and the type of diabetes. The intra-group performance variations are a result of many influential parameters, where particularly the subject-specific glucose dynamics and biological properties are recognized as some of the key factors. For example, the thickness of the outer most skin layer, the dead stratum corneum layer, is 166 ± 40 μm on thenar,9 meaning that the *Raman spectra* from different subjects feature different proportions of the signal from the dead and living parts of the skin, which influences the raw signal-to-noise ratio. We note that the inter-subject variation of stratum corneum contribution to the Raman signal can, in principle, be mitigated by adjusting the confocal collection depth for each sensor to the specific subject. However, it was the purpose of this study to test one nominal sensor configuration (collection depth of 280 μm) to be used by all. As another example of biological variation, skin autofluorescence is a noticeable contributor to the in vivo Raman spectra, and as the fluorescence level is subject-dependent (see Figure 1c), it contributes to the shot-noise in different ways from subject to subject. To complicate matters, the fluorescence level is also subject to photobleaching during a measurement, and this fluorescence decay is seen to vary both within and between subjects (for illustrative examples, see Figure S1).
**Figure 4:** *Histograms of subject-wise RMSE values
for (a) 137 subjects with
type 1 diabetes and (b) 23 subjects with type 2 diabetes.*
## Regression Vector of the Calibration Model
By controlling all external factors that may influence a Raman spectrum, such as temperature, skin inhomogeneity, and body movement, it has previously been shown that the glucose fingerprint though weak is directly visible in in vivo skin spectra.2 *In this* study, the influence of a multitude of external perturbations precludes the possibility to directly view the change in the skin *Raman spectrum* as implied by a change in the glucose concentration. Instead, insight into the relation between spectral and glucose changes can be sought via interpretable multivariate regression techniques. In our case, we based the subject-wise calibration models on partial least squares (PLS) regression in which the associated regression vector represents the importance of the different regions of the *Raman spectrum* when correlating with glucose reference values. Figure 5 shows the regression vectors of the 160 PLS models (one for each subject), which convincingly demonstrates that despite noticeable inter-subject spectral variation, the PLS algorithm shows a consistent regression vector, thus underlining that the spectrum-glucose correlation is not spurious but a consequence of the glucose fingerprint present in all measured skin spectra. Furthermore, by comparing the subject-averaged regression vector with a *Raman spectrum* of a glucose solution, it is evident that the most influential spectral areas (i.e., where the regression vector has the largest absolute values) coincide with the main Raman peaks of glucose. In this regard, it is important to recognize that the regression vector should not simply mirror the glucose spectrum as the complex matrix of the skin requires the regression vector to account for non-glucose spectral features and variations.
**Figure 5:** *Regression vectors from the individual prediction
models. The top
shows the Raman spectrum for glucose and the average regression vector
obtained from the PLS prediction models. The regression vector is
seen to mimic the significant peaks in the glucose spectrum. This
is consistent for all 160 subjects as demonstrated on the color-coded
map.*
It is worth noting that despite the similarity between the individual regression vectors, the underlying PLS regression models do not necessarily feature the same number of latent variables. For the 160 subjects, the distribution of the number of latent variables is 20.8 ± 2.7, while subgroup analysis regarding diabetes type shows 20.6 ± 2.8 and 21.7 ± 2.0 for type 1 and 2, respectively. In all cases, the number of latent variables is high, which is a signature of the complexity of the problem with a small glucose signal residing in a largely varying thenar Raman spectrum.
## Discussion
Discomfort and burden from multiple daily skin punctures have for years been a strong motivation to develop technologies for non-invasive glucose determination.15 While the minimal-invasive enzyme electrode, after about 40 years of research and development, found its way into practical routine,16−19 non-invasive glucose testing, despite the many different, technically sophisticated approaches,20 has not yet progressed to widespread practical use.
Here, we present a portable instrument manufactured in series that gives a satisfactory performance in the hands of subjects with type 1 and type 2 diabetes. Its accuracy, as demonstrated on consensus error grids and by MARD and RMSE values, is comparable to what was found in early continuous glucose monitoring studies with enzyme electrodes, where MARD values between 8.8 and $19.9\%$ have been reported in home use.14 One must also take into account that in continuous monitoring, glucose kinetics are subject to mathematical correction to counteract the time lag associated with glucose transfer from capillary to interstitial space.11 Contemporary CGMs employ trend information to correct for the time delay.11 This type of correction is not possible with the current intermittent measurements of NIGM. However, when NIGM is operated in a semi-continuous mode, there is no principal hindrance to improve accuracy by time-series analysis and consideration of glucose dynamics.21 In fact, an observed accuracy difference of ∼0.3 mmol/L between groups with type 1 and type 2 diabetes (see Figure 3) is mainly ascribed to the former group experiencing larger and steeper glucose fluctuations, which could be alleviated by such corrective means.
As observed in CGM, MARD as the index for accuracy is influenced by the range as well as by rapid changes of the glucose concentration.14 *Our data* suggest similar effects in NIGM. The group of subjects with type 1 diabetes was a considerable part of the glucose measurements in the hypoglycemic range below 3.9 mmol/L, contributing to a relatively high MARD value of $19.9\%$. In contrast, the 23 subjects with type 2 diabetes showed a smaller glucose spread and had only $0.12\%$ of points under 3.9 mmol/L. The encouraging performance metrics of $99.8\%$ of points in zones A + B on the consensus error plot of Figure 3b, an RMSE of 1.6 mmol/L, and MARD of $14.3\%$ suggest the qualification of the instrument for use in type 2 diabetes.
The obtained accuracies for the subjects with type 1 and type 2 diabetes are in the upper and the middle of the MARD range as previously reported for CGM with enzyme electrodes in home use.14 The presented performance metrics represent average values over the entire glucose range of reference values (∼2 to 30 mmol/L). It is important to clarify that individual PLS models are built on data available from the calibration days, meaning that the subject-specific distribution of glucose reference values dictates how the models emphasize different glucose intervals. Thus, the best measurement accuracy is achieved for glucose values around 8 mmol/L, which coincides with the glucose value most frequently occurring in the calibration data (see Table 1 and Figure S2). Though not sought in this work, we note that the dependence of accuracy on the glucose value can be eliminated by constructing regression models on a controlled distribution of reference values.22 The presented NIGM sensor technology is based on a confocal Raman setup that converts recorded thenar spectra to quantitative glucose values through the use of chemometrics. This approach is fundamentally different from commercial, home-use (invasive) glucose monitors that are based on enzymatic electrochemical technology, where a generated electrical current is proportional to the surrounding glucose level.23 *This is* also the basic working principle of novel wearable sweat glucose sensors.24,25 The electrochemical technology is therefore based on univariate regression, meaning that any spurious chemical activity, adding or subtracting from the primary process of chemical conversion of glucose by the enzyme, biases the glucose measurement. As such, the issue of interferences must be combated on the hardware level, which is typically achieved by using an enzyme that is highly sensitive to glucose and by coating the electrode with a permselective membrane.23 The Raman + chemometrics approach is altogether different as no single point in the spectrum determines the glucose value, but a multitude of signals contributes to the determination of a single glucose value. The sensitivity toward glucose is not inherently present in the thenar spectra, but the specificity is achieved through training of the multivariate regression model. By feeding the model with a multitude of paired spectra and reference glucose values acquired over many days, the influences from natural biological variation and environmental conditions are separated from the glucose variability, hence creating robust models that are sensitive to glucose. The robustness and glucose sensitivity, as demonstrated by the 15 day calibration stability and accurate glucose measurements, are thus achieved through mathematical means.
The 15 day stability of calibration, the consistent spectrum-glucose correlation, and the lack of major effects of age, gender, and skin color on performance, as shown in Table 2, unequivocally demonstrate that the combination of Raman spectroscopy and chemometrics can be configured for practical use. The presented results are based on PLS models that are built on 26 days of calibration data. The extended calibration period originates from the study setup, featuring six measurements per day, and the requirement of a certain amount of data to ensure predictive power and calibration stability of the subject-wise PLS models.26 That said, it is important to emphasize that the PLS models are not crucially sensitive to the 26 days of calibration. For example, the number of calibration days (and the size of the calibration set accordingly) can be reduced to 20 or 14 days with a respective increase in the average, subject-wise RMSE over the 15 day validation period of 2.4 and $10.7\%$ (for further details, see Figure S3). It is expected that the calibration requirement can be significantly reduced by utilizing calibration transfer techniques and/or creating robust regression models by combining data from multiple subjects and devices.27
## Conclusions
We have shown that Raman spectroscopy, coupled with multivariate data analysis, is well suited for home-use non-invasive glucose monitoring in people with diabetes. We developed a robust Raman-based, portable sensor for intermittent glucose determination that has proven to be successful in the hands of lay people, irrespective of age, gender, and skin color. Crucially, the sensor technology can be calibrated for real-life usage, which in this work is demonstrated by a measurement accuracy that remains stable over a 15 day validation period. The glucose sensor is still in development, and our focus is on further miniaturization, further improvement of accuracy, extended calibration stability, and a reduced calibration scheme. As a final remark in relation to the ever-present discussion of the beginning of the non-invasive era in diabetes management, it is interesting to note that the presented results convincingly corroborate with a recent review that foresees Raman spectroscopy to be the most promising technology for non-invasive glucose monitoring.28
## Instrumentation
The spectral acquisition was performed using a custom-built confocal Raman setup of external dimensions of 168 mm (l) × 130 mm (w) × 62 mm (h). The optical module, as depicted in Figure 1b, consists of a spectrometer and a probe assembled into one unit (Wasatch Photonics, USA). The thenar of the hand was placed on a 500 μm-thick magnesium fluoride window for the measurements. The output from the continuous-wave diode laser (Beijing RealLight Technology, China), emitting light at a wavelength of 830 nm with a power of 300 mW, was first collimated, and unwanted spectral side lobes and fluorescence were removed by a clean-up filter. The laser light was then transmitted by the dichroic mirror and finally focused (by the f/0.55 lens) just below the skin surface. Meanwhile, both the intense reflected/scattered light, fluorescence, and generated Raman photons were collected by the f/0.55 lens; the dichroic mirror and the long-pass filter ensure that only the latter two contributions to the spectrum were focused by the lens on the entrance slit of the spectrometer that also functions as the pinhole in our confocal setup. The spectrometer has an f-number of 1.3 with a spectral resolution better than 1 nm in the measurement range of ∼850 to 960 nm. Finally, the dispersed light was recorded by a CCD image sensor (Hamamatsu, Japan) that was temperature-stabilized at 20 °C.
## Participants
The clinical study was performed at Institute for Diabetes Technology at University of Ulm, Germany, Steno Diabetes Center Copenhagen and Steno Diabetes Center Odense, Denmark according to the Declaration of Helsinki and the Guidelines for Good Clinical Practice. One hundred sixty consecutive persons with manifested type 1 and type 2 were recruited and asked for written consent. Exclusion criteria were severe hypoglycemia in the past 3 months; hypoglycemia unawareness; severe diabetes-related complications (e.g., advanced autonomic neuropathy, kidney disease, foot ulcers, legal blindness, or symptomatic cardiovascular disease as evidenced by a history of cardiovascular episode(s)); systemic or topical administration of glucocorticoids for the past 7 days; pregnancy or lactation period; known severe allergy to medical-grade adhesive or isopropyl alcohol (used to clean the skin); inability to comply with the study procedures (due to, e.g., psychiatric diagnoses, lack of cognitive ability, alcohol dependency, drug use, or psychosocial overload); inability to hold the arm or hand still (including tremors and Parkinson’s disease); and extensive skin changes, tattoos, or diseases on the right thenar. All subjects were screened with a skin tone sensor (DEESS Demi II GP531, Shenzen GSD Tech Co., Ltd., China) for skin color I to V according to the Fitzpatrick scale.29 One hundred thirty-seven persons with type 1 diabetes, used to intensive treatment with blood glucose self-monitoring 4–6 times per day, rapid mealtime insulin, and long-acting insulin at bedtime or pump use, were instructed in the use of the device. The cohort of 23 subjects with type 2 diabetes on oral antidiabetic drugs and/or insulin was under a similar test regimen as the group with type 1 diabetes.
## Ethical Standards
The study was approved by the local ethical committees, the German Federal Institute for Drugs and Medical Devices, and the Danish Medicines Agency. It was registered as no. 2020040420 (DK) and with EUDAMED no. CIV-20-04-032405.
## Study Design
The study period was 41 days, where the first 26 days of the study were used for calibration, while the remaining 15 days were used for validation. On each day, subjects performed six measurement units, each comprising two reference capillary tests and three NIGM scans in the sequence BGM reference, NIGM, NIGM, BGM reference, and last, NIGM. The NIGM scans lasted for 75 s each, but the measurement time can easily be reduced without noticeably affecting measurement performance (see Figure S4). All subjects remained unaware of the NIGM readings. After instruction on the use of the NIGM device, there was no further professional supervision during the sessions for the days at home or work. Capillary glucose, as standard for calibration and parallel measurement with NIGM, was measured with the Contour Next One system (Ascensia, Switzerland). Accuracy in the hands of subjects was found to correspond to a MARD of $5.6\%$.30 Control solution measurements were performed on the test strips for every new strip vial opened before handing test strips to subjects. The raw data were transmitted to RSP Systems, Odense, Denmark for further evaluation.
## Data Analysis
The relationship between recorded *Raman spectra* and associated BGM references was established through PLS regression.31 *The data* analysis was centralized in Python using the scikit-learn package. A single NIGM scan involved a series of recorded Raman spectra, while a single measurement unit comprised three NIGM scans. The study comprised 41 measurement days, where each day encompassed six measurement units. Thus, the starting point of the data analysis was a large database of thenar *Raman spectra* that initially underwent cleaning/filtering. The cleaning step involved removal of saturated spectra, spike removal, and deletion of NIGM units in which the difference in the two BGM reference values was above 1.5 mmol/L. The latter represented an unusual high variation in consecutive BGM references and, for this reason, was treated as error-prone reference values. After the initial cleaning, the spectra of each scan were averaged to a single spectrum, normalized to unit Euclidean norm, and aligned to a *Raman axis* of 300–1615 cm–1 in 700 equidistant points (i.e., spectral features). The spectra were further processed by Savitzky–Golay smoothing (five-point window, first-order polynomial) and corrected for varying fluorescence backgrounds by second-order extended multiplicative scatter correction (EMSC).32 The BGM reference of a measurement unit was found by simple averaging of the two reference values.
To improve model construction and prediction, the dataset was analyzed for the presence of outliers. Spectral outliers were identified by calculation of the Q-residuals and Hotelling’s T2s and subsequently compared to the $99\%$ confidence intervals.33 If more than one scan of a measurement unit was identified as an outlier, then the whole unit was removed. As the final preprocessing step, the spectra and reference values were mean-centered. The PLS regression model was trained on the preprocessed scan spectra, where the three spectra of a measurement unit refer to the same reference value. The number of PLS components was determined from minimization of the root-mean-squared error (RMSE) of 20-fold, contiguous cross-validation. During validation of the calibration model, the prediction of a measurement unit was obtained by averaging the underlying scan predictions, as obtained by entering the scan spectra into the PLS model. It is important to recognize that the dataset consists of 26 and 15 days of calibration and validation data, respectively, that were kept separate during the data analysis. For example, the extended multiplicative scatter correction reference, outlier model, mean-center reference, and PLS regression model were all based on calibration data, while the validation data was solely used for independent validation of the predictive performance.
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|
---
title: 'Ratiometric Two-Photon
Near-Infrared Probe to Detect
DPP IV in Human Plasma, Living Cells, Human Tissues, and Whole Organisms
Using Zebrafish'
authors:
- Javier Valverde-Pozo
- Jose M. Paredes
- Thomas J. Widmann
- Carmen Griñan-Lison
- Giada Ceccarelli
- Antimo Gioiello
- M. Eugenia Garcia-Rubiño
- Juan A. Marchal
- Jose M. Alvarez-Pez
- Eva M. Talavera
journal: ACS Sensors
year: 2023
pmcid: PMC10043939
doi: 10.1021/acssensors.2c02025
license: CC BY 4.0
---
# Ratiometric Two-Photon
Near-Infrared Probe to Detect
DPP IV in Human Plasma, Living Cells, Human Tissues, and Whole Organisms
Using Zebrafish
## Body
*The* general principle of an enzyme activity assay is the use of specific enzyme substrates that interact with the enzyme, identifying the changes in substrate concentration by means of the appearance of some signal. Among the employed signals, fluorescence microscopy imaging has been considered a favorable method for detecting enzyme activity in vivo and is broadly used in preclinical research due to its ability to achieve real-time investigation of physiological and pathological processes with high sensitivity and high spatiotemporal resolution,1−3 making it possible to map enzymes in their native environment with high specificity.4 Therefore, microscopy imaging can help to detect unusual enzyme expression before changes in the morphology of unhealthy tissue occur and aid in the early diagnosis of diseases.
Fluorescence microscopy imaging requires appropriate probes that can be activated by specific enzymes to generate analytical signals.5 Therefore, the development of highly specific and high-resolution imaging probes is crucial for the precise detection of enzyme activity. In a large number of cases, enzymatic fluorescent probes requiring ultraviolet–visible (UV–vis) light excitation have been employed with one-photon microscopy (OPM). This practically excludes their use for investigating enzyme activities in vivo since it is difficult to obtain clear images of enzyme distribution in biological samples at a certain depth due to the absorption of the excitation light, the scattering properties of the tissues, and their autofluorescence, which all result in degradation of image resolution.6 Therefore, it is an essential requisite that both the incident and emitted light be of a long wavelength (red or near-infrared [NIR]) to achieve deep penetration inside the interior of a living organism, as well as lower fluorescence background from the organism, which allows a better three-dimensional localization of the probe.7 To avoid these drawbacks, a technique can be used that consists of the excitation of the fluorophore by means of two simultaneous photons of a wavelength that doubles or exceeds the wavelength needed to excite the same fluorophore with a single photon.8,9 Two-photon absorption was predicted by Nobel laureate Maria Goeppert Mayer in 193110 and was applied by Webb in a cellular environment in 1990.11 Two-photon microscopy (TPM) uses NIR photons as an excitation source for fluorophores, resulting in deeper tissue images in biological systems. Moreover, TPM is suitable for three-dimensional resolution for in vivo enzyme activity studies, so TPM is gaining immense support for clinical optical imaging applications.12 New advances in microscopy instrumentation have made it possible to overcome the resolution limit, which has allowed fluorescence microscopy images to be brought to the nanometer scale. STED (stimulated emission depletion) microscopy with DCM-NH2 as a fluorophore was proposed in our laboratory to locate hot spots images of catalytic activity in Gram-negative bacteria that express peptidase N (pepN).4 DPP IV, also known as CD26 lymphocyte T surface antigen, was first reported as glycylproline naphthylamidase by Hopsu-Havu and Glenner.13 DPP IV is a transmembrane glycoprotein of 110 kDa MW expressed constitutively in a dimeric form (220 kDa) in a variety of cell types (prostate, kidney, liver, and epithelial cells). DPP IV is anchored in the plasma membrane with a type II orientation by means of a short cytoplasmic tail (amino acids 1–6), a transmembrane domain (TMD) (amino acids 7–28), a flexible region (residues 29–39), and a C-terminal extracellular domain (residues 40–766) containing catalytic activity.14,15 In addition to being membrane-bound, DPP IV is also found in circulation. Soluble DPP IV present in the plasma lacks the intracellular tail and transmembrane domain of the protein but retains substantial enzymatic activity.16 DPP IV shows enzymatic activity, being specific for a proline (Pro) residue at the penultimate position of the peptide chain, and hydrolyzes on the carboxyl side of this residue. The Pro residue can be substituted by alanine (Ala) or hydroxyproline (Hyp), although the rates of hydrolysis for these substrates are much lower than the rates of hydrolysis for the corresponding substrate containing Pro.17,18
DPP IV, involved in numerous pathological processes by regulating T-cell stimulation, plays an important role in several routes, such as glucose metabolism; therefore, DPP IV has been considered a target for the treatment of type 2 diabetes.19 Their inhibitors have been introduced to clinics as a class of oral hypoglycemic drugs (called gliptins) that are commonly used to treat type 2 diabetes mellitus and have been demonstrated to efficiently enhance endogenous insulin secretion.20 DPP IV has previously been associated with the start and progression of several human cancer types; hence, it is considered an important molecular marker and therapeutic target for cancer.21,22 DPP IV is overexpressed in human renal carcinoma tissues, and its blockage reduced several cancer-related processes in the human renal carcinoma cell line Caki-2.23 DPP IV has also been shown to be overexpressed in several human colon cancer tissues and in the human Caco-2 colorectal cancer cell line.24,25 *In cancerous* prostate, the DPP IV activity was enlarged 2-fold versus benign prostatic hyperplasia. Moreover, an elevation of its activity was also found in the peripheral zone of the prostate, where most prostate cancers arise.26 In humans, higher DPP IV levels in cancerous versus normal prostate tissue were correlated with prostate-specific antigen (PSA) level, cancer phase, and both tumor residue and size.27 In addition, DPP IV activity has been proposed as a marker for thyroid carcinomas28 and can also serve as a first-level diagnostic procedure to recognize patients with lysosomal storage diseases.29 Several methods for evaluating DPP IV activity have been established. Spectrophotometric activity assays can be performed by hydrolyzing the chromogenic substrate glycyl-prolyl-β-naphthylamide13 or through the measurement of the p-nitroaniline liberated from glycylproline p-nitroanilide.30,31 Fluorogenic substrates such as bis-(Ala-Pro)2-Rhod11032 or glycylprolylglycylprolyl-9-di-3-sulfonyl-propylaminobenza[a]phenoxazonium perchlorate,33 lanthanide metal ions for time-resolved fluorescence probes,34 or fluorescent probes with aggregation-induced emission (AIE) characteristics have been developed for DPP IV inhibitor screening due to the pharmacotherapeutic interest that inhibitors of DPP IV have in the treatment of type 2 diabetes.35 Therefore, a ratiometric two-photon (TP) fluorescent probe whose hydrolysis releases N-butyl-4-amino-1,8-naphthalimide and fluoresces with a maximum at 535 nm was developed by Zou et al.36 To monitor the in vivo DPP IV enzyme activity in biological systems in real time and with three-dimensional resolution, Guo and co-workers37 recently proposed an NIR fluorescent probe composed of a glycyl-prolyl peptide and a hemicyanine dye. However, neither of the fluorescent probes meet the three characteristics of being ratiometric, excitable by two photons and emitting NIR fluorescence.
Herein, a highly selective ratiometric two-photon NIR fluorescent probe that fluoresces with a maximum at 662 nm was designed, synthesized, and photophysically characterized. The new probe has been used for imaging DPP IV in human plasma, living Caco-2 colorectal cancer cells, tumor-bearing pancreatic tissue, and zebrafish embryos and larvae. The probe response is reached by the assembly of an enzyme-recognizing group (Gly-Pro) on the TP fluorophore (dicyanomethylene-4H-pyran derivative, DCM-NH2), forming an enzyme-sensitive donor–acceptor (D–A) system showing high ratiometric fluorescence output. Moreover, the possibility of nonlinear two-photon excitation along with ratiometric detection has been exploited to analyze DPP IV in raw plasma.
## Abstract
DPP IV, otherwise known as CD26 lymphocyte T surface antigen, is a transmembrane glycoprotein also found in circulation in the blood. It plays an important role in several processes like glucose metabolism and T-cell stimulation. Moreover, it is overexpressed in renal, colon, prostate, and thyroid human carcinoma tissues. It can also serve as a diagnostic in patients with lysosomal storage diseases. The biological and clinical importance of having readouts for the activity of this enzyme, in physiological and disease conditions, has led us to design a near-infrared (NIR) fluorimetric probe that also has the characteristics of being ratiometric and excitable by two simultaneous NIR photons. The probe consists of assembling an enzyme recognition group (Gly-Pro) (Mentlein, 1999; Klemann et al., 2016) on the two-photon (TP) fluorophore (derivative of dicyanomethylene-4H-pyran, DCM-NH2) disturbing its NIR characteristic internal charge transfer (ICT) emission spectrum. When the dipeptide group is released by the DPP IV-specific enzymatic action, the donor–acceptor DCM-NH2 is restored, forming a system that shows high ratiometric fluorescence output. With this new probe, we have been able to detect, quickly and efficiently, the enzymatic activity of DPP IV in living cells, human tissues, and whole organisms, using zebrafish. In addition, due to the possibility of being excited by two photons, we can avoid the autofluorescence and subsequent photobleaching that the raw plasma has when it is excited by visible light, achieving detection of the activity of DPP IV in that medium without interference.
## Reagents
and Standards
Dimethyl sulfoxide (DMSO), phosphate-buffered saline (PBS), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and the reagents and solvents used for DCM-NH-Pro-*Gly synthesis* were purchased from Sigma-Aldrich (St. Louis, MO). With the exception of the enzyme alanine aminopeptidase (ANEP), which was produced and purified as previously described,4 all other enzymes used, DPP IV, tyrosinase (TYR), acetylcholinesterase (AChE), lipase (PNLIP), dipeptidyl peptidase VIII (DPP VIII), fibroblast activation protein α (FAP), and leucine aminopeptidase (LAP) plus the enzyme inhibitor sitagliptin, were purchased commercially from Sigma-Aldrich. All of them were of the highest-quality grade.
## Sample Preparation
A 0.5 mM stock solution of DCM-NH-Pro-Gly dye was prepared in deuterated DMSO for purity testing by nuclear magnetic resonance. Unless otherwise indicated, the experimental samples were prepared in a $\frac{7}{3}$ v/v mixture of PBS/DMSO buffer solution.
## Instrumentation
Steady-state fluorescence emission spectra and kinetics were obtained by a Jasco FP-8 300 spectrofluorometer (Jasco, Tokyo, Japan). UV–visible absorption spectrophotometry was carried out using a Cary 60 UV–visible spectrophotometer (Agilent, Santa Clara, CA). Both the fluorometer and the absorption spectrophotometer have a temperature controller.
Single-photon images were collected with a confocal microscope (Abberior Instruments GmbH, Heidelberg, Germany) supplied with a pulsed excitation laser (450 nm, 40 MHz) and a pulsed STED laser (775 nm, 40 MHz). The microscope has a UPlanSApo 1.4 NA, 100× objective oil immersion. The pinhole size was set to 1 Airy Unit. The collected fluorescence was separated by a 560LP dichroic directed to an avalanche-photodiode (APD) and hybrid photomultiplier tube (HPMT) detectors after passing through $\frac{685}{75}$ and $\frac{545}{25}$ filters, respectively.
Two-photon imaging was performed using a confocal MicroTime 200 fluorescence microscope system (PicoQuant GmbH, Berlin, Germany). The excitation source was a Chameleon Discovery NX tunable laser (Coherent Laser Group, Santa Clara, CA) used at an excitation wavelength of 800 nm. The repetition rate was modified by a pulse selector (APE Angewandte Physik & Elektronik GmbH, Berlin, Germany) using two acoustic-optic Bragg cells to reduce the frequency from 80 MHz to 40 MHz. The excitation beam passed through an achromatic quarter-wave filter (AQWP05-M-600, Thorlabs, Jessup, MD) and was directed by an F73–705SG dichroic mirror (AHF/Chroma, Tübingen, Germany) to an inverted microscope system (IX-71, Olympus, Tokyo, Japan) with an oil immersion objective (1.4 NA, 100×). Fluorescence emission was collected with a 550 nm longpass filter (AHF/Chroma, Germany) and directed to a 150 μm pinhole. The emission from the sample was split into two detection channels after passing through a 600 DCXR dichroic beam splitter (AHF/Chroma), and then through bandpass filters, $\frac{685}{70}$ (Semrock/AHF) until one detector and through $\frac{520}{35}$ filter (Semrock/AHF) to the other detector. The detectors used were two different single-photon avalanche diodes (SPADs) (SPCM-AQR 14, PerkinElmer, Waltham, MA).
Zebrafish embryos were imaged on a Nikon SMZ18 fluorescent stereo microscope with a color DS-Ri2 digital camera (16.25 megapixels) using filter settings for red fluorescent protein (RFP) and under a Zeiss LSM710 confocal microscope (Jena, Germany) with a 10x objective, recording brightfield and red and infrared fluorescent light (540 and 680 nm) after excitation with a 458 nm laser.
## Clog P Calculation and Image Processing
The Clog P values of both compounds were calculated using ChemDraw Professional v20 (PerkinElmer, Waltham, MA). Every image was exported as matrix data and analyzed using Fiji Is Just ImageJ.38 *The analysis* was performed taking two channels separately. Ratiometric values between the red and green channels were obtained by homemade macros described in the SI.
## Cell Line and Cell Culture
The human colon cancer Caco-2 cell line was obtained from American Type Culture Collection (ATCC, Manassas, VA). The cell line was cultured in Minimum Essential Medium Eagle (EMEM; Sigma-Aldrich, St. Louis, MO) supplemented with $10\%$ heat-inactivated fetal bovine serum (FBS) (BioWhittaker; Lonza, Basel, Switzerland) and with $1\%$ of a solution of penicillin/streptomycin (10 000 U mL–1 penicillin G and 10 mg mL–1 of streptomycin; Sigma-Aldrich, St. Louis, MO), and the solution was maintained at 37 °C in an atmosphere containing $5\%$ CO2.
## Cytotoxicity Assay In Vitro
Caco-2 (colon cancer) and BxPC-3 (pancreatic cancer) cell lines were seeded in 96-well plates in a concentration of 3000 cells/well in EMEM and Roswell Park Memorial Institute (RPMI) medium. After 3 days, cells were treated with the DCM-NH-Pro-Gly (2.5, 5, and 10 μM involving a percentage of DMSO equal to 0.5, 1, and $2\%$, respectively), other wells were treated with DMSO, in the same percentages (0.5, 1 and $2\%$) being both incubated for 15, 30 and 60 min. Moreover, cells not treated were used as a control. After these times, all cells were incubated with MTT39 for 4 h and the absorbance was measured at 570 nm on a Microplate reader Synergy HT, BIO-TEK.
## Generation of Subcutaneous Xenograft Tumors
To establish subcutaneous xenograft tumors, an eight-week-old male NODSCID γ mouse (NOD. Cg-Prkdcscid Il2rgtm1 Wjl/SzJ, NSG) was used. All procedures were approved by the Institutional Animal Care and Use Committee at the University of Granada (ethical code: $\frac{03}{07}$/$\frac{2017}{086}$). The mice were housed and maintained at 20–24 °C, $50\%$ relative humidity (RH), and a 10:14 h light/dark cycle with food and water provided ad libitum. A human pancreatic cancer cell line, BxPC-3, was used to generate subcutaneous xenograft tumors, by injection of 1 × 106 cells in 0.05 mL of Matrigel and 0.05 mL of Roswell Park Memorial Institute (RPMI) medium using 26-gauge needles. When the tumor reached 300 mm3, the mouse was euthanized by cervical dislocation. For analysis by TPM, tumors were excised, fixed in $4\%$ paraformaldehyde (PFA), embedded in optimal cutting temperature (OTC) compound, and selected using a cryotome at a thickness of 10 mm for further analysis.
## In Vivo Imaging of DPP IV
in Zebrafish
Zebrafish (Danio rerio) were kept in fish tanks with constant water flow at 28 °C, following maintenance and breeding recommendations from the zebrafish handbook (https://zfin.org/zf_info/zfbook/zfbk.html). Wild-type males and females were set up in a breeding tank. Egg laying occurred shortly after the onset of light in the morning on the following day. Embryos were raised in E3 embryo medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, 0.33 mM MgSO4, 10–$5\%$ methylene blue) at 28 °C until the desired stage (1, 3, 5, and 7 days postfertilization (dpf)). Embryos of the desired stage were incubated in E3 medium with 5 μM DCM-NH-Pro-Gly (1:100 of stock solution at 500 μM in DMSO) for 2 h and then briefly washed with fresh E3 medium. Controls were incubated in E3 medium with DMSO (1:100). Subsequently, anesthetized (0.1 to 0.15 mg mL–1 MS222) live embryos were imaged under the stereo microscope. For confocal microscopy, embryos were mounted in low-melting agarose in E3 medium and subsequently imaged. Three to four tile scans of z-stacks (z-distance 5 μm) per zebrafish embryo were stitched together using Fiji Is Just ImageJ.38 Maximum projections of z-stacks (z-distance 5 μm) of fluorescent signals from zebrafish embryos are shown.
## Synthesis
The design strategy of the probe DCM-NH-Pro-Gly was to react the carboxyl group of the dipeptide Gly-Pro with the amine group of DCM-NH2, perturbing the internal charge transfer (ICT) state that controls the spectral properties of DCM-NH2 and causing the shift of its absorption and emission spectra toward shorter wavelengths due to the effect of electron-withdrawing from the amide bond. Therefore, the catalytic action of DPP IV will restore the ICT in the DCM-NH2 molecule, providing remarkable ratiometric fluorescence between the peaks of the bands from DCM-NH2 and DCM-NH-Pro-Gly.
DCM-NH2, [1], was first synthesized according to the literature.40 The reaction of [1] with N-Boc-Gly-Pro gave rise to the corresponding amide [2], which was modified into DCM-NH-Pro-Gly [3] after deprotection of the tert-butyloxycarbonyl (BOC)-protecting group with trifluoroacetic acid (TFA) (Scheme 1 and Figure S1 in the SI) (more details are available in the SI).
**Scheme 1:** *Synthesis
of DCM-NH-Pro-Gly*
The synthetic method and the purification method are also detailed in the SI. The final product was characterized by 1H nuclear magnetic resonance (NMR), 13C NMR, and time-of-flight mass spectrometry (TOF MS), as shown in the SI.
## General Procedures for the DPP IV Activity
Assay
Enzyme kinetic assays were carried out in PBS 100 mM (pH = 7.4)/DMSO, $\frac{7}{3}$ v/v at 37 °C with shaking, adding the corresponding volumes from stock solutions to achieve the desired final concentration of enzyme and DCM-NH-Pro-Gly.
The enzyme inhibition study was performed using different concentrations of sitagliptin (0, 10, 100, and 250 μM) added to a PBS/DMSO ($\frac{7}{3}$, v/v), 100 mM, pH = 7.4 solution of 10 μM DCM-NH-Pro-Gly with 5 μg mL–1 DPP IV.
The DCM-NH-Pro-Gly enzyme selectivity assay was performed in PBS/DMSO $\frac{7}{3}$ using different enzymes at a concentration of 5 μg mL–1.
In plasma, kinetic studies were carried out by adding only $15\%$ DMSO. Plasma was collected from healthy volunteers and diabetic patients.
All cellular and in vivo studies were performed at a concentration of 5 μM DCM-NH-Pro-Gly. Enzyme inhibition studies in cells were carried out at a concentration of 5 μM DCM-NH-Pro-Gly and 50 μM sitagliptin.
## Spectral
Properties of the Probe and Its Response toward DPP
IV
First, we performed spectral characterization of both the probe (DCM-NH-Pro-Gly) and the reaction product (DCM-NH2). Absorption spectra show maxima at 442 and 480 nm in PBS/DMSO ($\frac{7}{3}$, v/v) (see Figure S2 in the SI). The emission of both compounds was characterized by maxima at 550 nm (Φ = 0.10 ± $0.01\%$) and 662 nm (Φ = 0.63 ± $0.09\%$) under the same conditions.
The influence of the solvent on the photophysics of dicyanomethylene-4H-pyran (DCM) has been previously studied.41 Thus, the redshift of the emission maximum with increasing solvent polarity is caused by an intramolecular charge transfer and concomitant increase of the dipole moment upon excitation. Fluorescence lifetime studies of DCM in a large number of solvents further confirmed that the nature of the solvent plays an important role in the decay processes of the excited state;42 therefore, the solvent choice for the in vitro studies with DCM is of great importance.43 Based on the previous work and the results summarized in Figure S3, DMSO appears to be best suited in view of the longer fluorescence decay time and low photoisomerization efficiency.42 *With a* QY of Φ = 5.73 ± $0.09\%$ for DCM-NH2 in DMSO, the presence of DMSO as a co-solvent is therefore well justified for the in vitro studies. Previous studies showed that in PBS buffer, the fluorescence signals decreased when less than $30\%$ DMSO was used, further precipitating the dye shortly after dissolution.4 Next, we studied the response of the probe to the enzyme DPP IV. Figure 1 shows the absorption and emission spectra of PBS/DMSO ($\frac{7}{3}$, v/v), 100 mM, pH = 7.4 solutions of DCM-NH-Pro-Gly, every 30 min after DPP IV addition. As depicted in Figure 1A, the probe exhibited an absorption band with a sharp peak at ∼440 nm. Upon the addition of DPP IV, the absorption is red-shifted, forming a new band with a maximum at approximately 480 nm, characteristic of DCM-NH2,4 giving rise to an isosbestic point at 463 nm. Figure 1B shows the absorbance at the wavelengths of interest (440 and 480 nm). Figure S2A in the SI shows the absorptivity of both compounds. Our data indicate that at a very long reaction time, the absorbance of the reaction product (ε = 37 700 ± 1600 L mol–1 cm–1, see Figure S2A in the SI) should overcome the absorbance of the probe (ε = 17 600 ± 300 L mol–1 cm–1, see Figure S2A in the SI).
**Figure 1:** *(A) Evolution of the absorption spectra of DCM-NH-Pro-Gly
(10 μM)
with DPP IV (10 μg mL–1) in PBS/DMSO
(7/3, v/v) every 30 min for 8.5 h at 37 °C. (B)
Maximum absorbance values at 440 nm (black line) and 500 nm
(blue line) vs time. (C) Evolution of the emission spectra of DCM-NH-Pro-Gly
(10 μM) with DPP IV (10 μg mL–1) observed every 30 min for 8.5 h by excitation at
463 nm at 37 °C. (D) Maximum fluorescence intensity
at 662 nm (red line) and the intensity decrease at 550 nm
(black line) vs time. (E) Ratiometric measurements of fluorescence
signals of I662/I550 of DCM-NH-Pro-Gly (10 μM) with different enzyme concentrations
vs time. (F) Initial rates from ratiometric measurements vs enzyme
concentrations.*
Concomitantly, the emission spectrum (λex 463 nm, at the isosbestic point) of the probe shows a relatively low fluorescence band centered at 550 nm that, after the addition of DPP IV, gradually disappears over time and gives rise to another very intense emission band with a peak at 662 nm, as shown in Figure 1C. The decrease in the maximum of DCM-NH-Pro-Gly (λem = 550 nm) was approximately 2.5 times, while the increase at 662 nm achieved ∼6.8 times after 8.5 h of incubation. The emission spectra recorded after adding the enzyme are equal to the emission spectra shown from solutions of free DCM-NH2. Figure 1D shows the fluorescence intensity at the fluorescence maxima wavelengths. Notably, the ratio between the fluorescence signals at 662 and 550 nm, I662/I550, increased over time after the addition of DPP IV. To verify whether the ratio can be used as a measure to quantify enzymatic activity, we measured different kinetic curves every 30 min for 8.5 h using different DPP IV concentrations. Figure 1E represents the ratio values. This parameter is dependent on the DPP IV concentration, allowing its use to determine enzymatic activity.35,44,45 In addition, following Michaelis–Menten theory, the initial rates from the ratiometric measurements should depend on the enzyme concentration of the sample. We confirm that the initial rates of the ratiometric measurements depend on the DPP IV concentration. Figure 1F shows the graphical representations of the initial rates and the enzyme concentration showing an excellent linear relationship (R2 = 0.998) in the range of concentrations measured (0.5 and 10 μg mL–1).
To confirm that the enzymatic reaction releases DCM-NH2, we first performed an MS of the dye (see Figure S4). After identifying the peak M+, we incubated DCM-NH-Pro-Gly with DPP IV and monitored the increase of the M+ peak at different time points by an HPLC-MS (Figure S5) showing an increase over time. Finally, we made a calibration curve using different DCM-NH2 concentrations (see Figure S6A) and we calculated the DCM-NH2 released at different times (see Figure S6B). Our data reflect a plateau at approximately 300 min, a similar value as obtained by absorption and emission spectroscopy. Additionally, we also determined whether the increase in fluorescence was due to the action of DPP IV performing inhibition studies using sitagliptin, a selective inhibitor of DPP IV46,47 (see Figure S7 in the SI). Figure S8 in the SI depicts the I662/I550 ratios. As observed, the incorporation of sitagliptin slows the reaction rate and produces a decrease in the 662 nm peak, showing that sitagliptin inhibits the action of the enzyme. Therefore, since sitagliptin scarcely affects the emission of DCM-NH2, the substantial NIR fluorescent band with a maximum at 662 nm must be attributed to DCM-NH2 released by specific cleavage of the amide bond due to DPP IV activity. Moreover, we checked the efficiency of the enzyme activity at different pH values and temperatures (Figure S9), showing good catalytic activity at pH 7.5 and 37 °C.
## Selectivity
of DCM-NH-Pro-Gly
To explore the applicability of DCM-NH-Pro-Gly for sensing DPP IV in biological samples, the specificity of the sensor was investigated. In Figure 2, the fluorescence I662/I550 ratio enhancement in the presence of DPP IV is compared to increases in the fluorescence I662/I550 ratio calculated when the samples were incubated with other related enzymes, such as ANEP, TYR, AChE, PNLIP, DPP VIII, FAP, and LAP. The evident augmentation of the I662/I550 ratio in the samples incubated in the presence of DPP IV versus the same fluorescence ratio from the samples incubated with the other enzymes unambiguously demonstrates the high specificity of our probe toward the enzymatic action of DPP IV.
**Figure 2:** *Ratiometric measurements
of fluorescence signals of I662/I550 of DCM-NH-Pro-Gly
(10 μM) in PBS/DMSO (7/3, v/v) after 80 min of incubation in
the presence of different enzymes at the same concentration (5 μg
mL–1) by excitation at 463 nm at 37 °C.*
## Enzymatic Kinetics Parameters of DPP IV-Mediated
DCM-NH-Pro-Gly
Hydrolysis According to the Michaelis–Menten Model
To avoid tedious corrections, the solutions used in the calculation of the characteristic parameters of the reaction kinetics were excited at 550 nm wavelength, which practically excites only the reaction product, generating fluorescence signals proportional to the concentration of the reaction product DCM-NH2. We elaborate a Kavanagh law (see Figure S10 in the SI) to determine the DCM-NH2 concentration released in the course of the reaction.
To resolve the enzyme kinetics following the Michaelis–Menten model, after transforming the fluorescence intensity into reaction product concentration, the initial rates of the enzymatic reaction at different initial substrate concentrations were calculated from the recorded fluorescence signal from DCM-NH2 (Figures S11 and S12 in the SI). The Michaelis–Menten model is represented in Figure S13 in the SI. Linear regression fitting based on the Lineweaver–Burk equation provided the following values: KM = 486 ± 46 μM and vmax = 0.588 ± 0.044 μM min–1 mg–1 (Figure S14 in the SI). As a result, the kcatalytic that we obtained is kcat = 5.9 × 10–4 ± 0.2 × 10–4 μM min–1 μg–1 mL.
## Enzymatic Kinetics
of DPP IV-Mediated DCM-NH-Pro-Gly Hydrolysis
in Human Plasma
Due to the great importance of DPP IV as a potential biomarker in the diagnosis and treatment of tumors, type 2 diabetes mellitus, and other serious diseases, as well as in the development of hypoglycemic drugs, it is very interesting to investigate the applicability of DCM-NH-Pro-Gly for the quantitative and precise determination of DPP IV activity in human plasma.
To test the activity of DPP IV in plasma with our probe, we collected samples from healthy individuals and those with diabetes mellitus. Due to the native fluorescence of the plasma from the endogenous fluorophores that it has in solution, it is necessary to perform a study of both excitation and emission spectra to find the most favorable conditions for the analysis of the activity of DPP IV in raw plasma.
The excitation spectrum of plasma, in the visible region, with detection at 580 nm (in which the probe and the product of enzymatic reaction show an isoemissive point), consists of three bands with maxima at approximately 350, 440, and 510 nm (Figure S15 in the SI). The wavelength of 480 nm, which is where the DCM-NH2 probe has the absorption maximum, is located between two excitation peaks of the plasma so that in the activity analyses performed, the sample has always been excited at 480 nm to obtain maximum fluorescence intensity.
In Figure S16, the emission spectrum from the plasma under the experimental conditions used in our experiments (λex = 480 nm) is represented, showing an emission peak ca. 550 nm, in concordance with the average native fluorescence spectrum of blood plasma from normal human subjects. Usually, human plasma shows two emission bands in the visible spectrum, one of them with a maximum at approximately 460 nm (attributed to fluorophores such as riboflavinoproteins, vitamin A, bilirubin, and lipoproteins) and another in the spectral range between 550 and 650 nm, which can be attributed to the presence of endogenous porphyrins,48 such as hematoporphyrin and protoporphyrin, which fluoresce in that wavelength range depending on the solvent polarity.49 On the other hand, the presence of a certain percentage of DMSO enhances the fluorescence of DCM-NH2.4 Therefore, we added different amounts of DMSO to a solution containing the products of the enzymatic reaction between DPP IV and our probe in plasma after reacting for 48 h at 37 °C. Figure S17 shows that the maximum emission was obtained when 15 or $20\%$ DMSO was added, while the addition of $30\%$ resulted in loss of the fluorescent signal and precipitation of some plasma components. To reduce the possible denaturation of DPP IV and achieve the maximum possible sensitivity, we used a percentage of DMSO equal to $15\%$ in all our following experiments.
In addition to these spectral features, a strong photobleaching effect on porphyrins at light power densities that were not too high has been reported,50 such as we have observed in experiments in which plasma spectra have been collected at the same times as in experiments with DPP IV and the probe. Figure S18 represents the spectra showing the considerable effect of $50\%$ photobleaching in the fluorescence band ca. 550 nm of the plasma after 24 h by collecting spectra every hour. Therefore, in experiments on the kinetics of the enzymatic reaction, the samples from healthy subjects were divided into two aliquots. DPP IV and DMSO up to $15\%$ were added to one of the probes. The other aliquot was composed only of plasma at the same dilution and $15\%$ DMSO. Emission spectra (excited at 480 nm) from the two aliquots were recorded every hour for 24 h. In Figure S19, raw spectra from the aliquot with probe and DPP IV are represented, and in Figure S20, the spectra corresponding to plasma/DMSO were subtracted, at each time, from those collected from the solution of DCM-NH-Pro-Gly 10 μM and DPP IV 10 μg mL–1 in plasma/DMSO. As seen in Figure S20, the calculation performed shows a fluorescence band with a maximum at approximately 635 nm with a shape similar to the shape of DCM-NH2 but slightly blue-shifted, probably due to the lower polarity of the medium.
A similar set of experiments was carried out with plasma from healthy subjects without adding external enzymes and from diabetic patients. The crude spectra collected corresponding to both samples of blood plasma with the probe are depicted in Figures S21 and S22, respectively. In both sets of experiments, those carried out with plasma from healthy subjects and plasma from diabetic patients, a new emission band appears with a maximum at approximately 635 nm that belongs to the product of the reaction with the probe that acts as a substrate of the enzymatic reaction with DPP IV. Importantly, in Figure S23, we compare the I635/I540 ratio derived from the spectra measured under the same conditions from healthy subjects and diabetic patients. Our results show a higher increase in intensity at 635 nm due to the higher presence of DPP IV in the diabetic plasma. Significantly, the slight increase in fluorescence intensity at 635 nm in the plasma from healthy subjects demonstrates that our probe is a useful tool as a sensor for the analysis of trace amounts of DPP IV in human plasma.
From the above discussion, it follows that the main drawback to measure the DPP IV activity in plasma by means of steady-state fluorescence is the strong plasma autofluorescence that appears at approximately 540 nm and its photobleaching with irradiation time (see Figure S18), making it impossible to perform reliable ratiometric measurements between the fluorescence at 635 and 540 nm.
To remove the autofluorescence coming from plasma, we considered using two-photon excitation to minimize that fluorescence. To check this idea, first, we checked the optimal excitation wavelength to achieve the maximum signal at each channel. For this, we recorded the fluorescent images of DCM-NH2 and DCM-NH-Pro-Gly at different excitation wavelengths from 720 to 1000 nm. We obtained the highest signal from both compounds at 800 nm (Figure S24).
Therefore, we performed experiments to measure the intensities in plasma using both detection channels (green and red) under the aforementioned experimental conditions. In Figure 3A, the intensities measured in both channels are represented using excitation wavelengths of 488 and 800 nm. As can be clearly observed, the use of two-photon excitation remarkably reduces the plasma autofluorescence in both detection channels, where the fluorescence measured when 800 nm was used as the excitation wavelength is negligible, probably due to a very low two-photon cross section from the fluorescent components of the plasma.
**Figure 3:** *(A) Intensity values
of green (λem = 533–557
nm) and red (λem = 648–722 nm) channels and
ratio maps using one-photon excitation at 488 and two-photon excitation
at 800 nm of blood plasma sample. Whiskers represent the standard
error (SE). (B) Representative ratio R/G maps of healthy human blood
plasma with DCM-NH-Pro-Gly with DPP IV (10 μg mL–1) at different incubation times (λex = 800 nm).
C) Ratio R/G average values of blood plasma with the probe DCM-NH-Pro-Gly
images registered at different incubation times (λex = 800 nm). Line represents a visual aid. Whiskers represent the
SE.*
Once it was shown that excitation at 800 nm practically eliminated plasma autofluorescence, DCM-NH2 dye was added to another plasma sample. For microscopy experiments, we collected the fluorescence emission in two different channels (red channel, for the emission of the released product of the reaction, DCM-NH2, and the green channel for the emission coming mainly from DCM-NH-Pro-Gly). Thus, the ratio values were measured through the quotient of both channels (ratio R/G). Figure S25 shows the recovered fluorescence signals in both channels along with the ratio R/G, which corresponds to the unperturbed emission of the compound. We also performed experiments in which the DCM-NH-Pro-Gly probe was added to the plasma (Figure S26), obtaining the expected ratio corresponding to the unreacted probe, i.e., lower red fluorescence intensity, higher green fluorescence intensity and lower R/G ratio than DCM-NH2.
Finally, we carried out experiments in which the probe and the enzyme were added to the plasma, allowing them to react for 24 h and taking samples at various times for their measurement. Some representative ratio R/G images are depicted in Figure 3B. In this figure, it is possible to visualize color differences in the images at different incubation times, where the color change is caused by the cleavage of DCM-NH-Pro-Gly into DCM-NH2. The results of measuring different samples are represented in Figure 3C, which shows that the R/G ratio increases gradually over time. The rate of appearance of the products of the enzymatic reaction between DPP IV and a certain substrate has been used to calculate the activity of DPP IV in plasma from patients with diabetes.36,51 The effectiveness of the methodology developed with the DCM-NH-Pro-Gly probe opens a door to its future use in the in situ detection of DPP IV, allowing the diagnosis of diseases in which the enzyme is overexpressed in the blood. Of course, the proposed methodology must be optimized to provide quantification of enzyme activity. Currently, the necessary experiments for this purpose are being designed in our laboratories and will be published elsewhere.
## Fluorescence Imaging of DPP IV in Living Cells
One of the main advantages of optical probes is the possibility of measuring them in real time, in vivo, and in situ. These characteristics make them very attractive for use in biological samples. To prove the efficiency of DCM-NH-Pro-Gly as a DPP IV intracellular sensor, we selected the Caco-2 cell line as a cellular culture of interest. Caco-2 cells are from human colorectal adenocarcinoma, a disease reported to have higher DPP IV activity.
After the addition of DCM-NH-Pro-Gly to extracellular media, the probe penetrates inside the cells rapidly and spontaneously, as do similar chemical probes described in the literature.4,52,53 As soon as we added the probe, we observed an increase in the emission recorded in the red channel (Figure 4A), whereas the green channel registered a very slight decrease. Despite the relatively low QY of the dye, the acquired images show a good signal-to-noise ratio, which could be due to the large environment dependence of the quantum yield. Similar to DMSO, which yielded an order of magnitude increased QY relative to PBS/DMSO $\frac{7}{3}$ (v/v) buffer, the lower polarity environment inside cells might also result in a significantly higher QY and an overall increased brightness. Consequently, the ratio R/G images showed an evident increase (Figure 4A). In Figure 4B, we represent the average ratio R/G values with respect to time. As expected, the kinetics showed a good intracellular growth pattern. The representation of these data on a double-logarithmic scale (Figure 4C) fits remarkably well with a linear fit, as evidence indicates that the R/G ratio arises only from probe cleavage and not from slow entry of the probe into the cells, confirming our hypothesis of fast intracellular penetration of the probe. The good cell membrane permeability can also be confirmed with the calculation of the Clog P values (the coefficient between n-octanol and water, a well-established measure of the hydrophilicity of the compound). Our obtained values were 2.615 and 2.877 for DCM-NH-Pro-Gly and DCM-NH2, respectively. Our data are inside the range of the Lipinski rule for substances with good cell permeability (−0.5 < log $P \leq 5$).54
**Figure 4:** *(A)
Images of a representative sample of the live Caco-2 cell line
incubated with DCM-NH-Pro-Gly (2.5 μM) recorded in the red (left,
λex = 450 nm, λem = 648–722
nm) and green (middle, λex = 450 nm, λem = 533–557 nm) channels at six different time points.
Ratio R/G images (right) obtained at the same times. (B) Representation
of the average values of the ratio R/G from five independent experiments.
Error bars represent SE. (C) Double-logarithmic representation of
the kinetics. Error bars represent SE. The red line is a linear fit
with an intercept of 0.964 ± 0.004 and a slope of 0.215 ±
0.003 (R2 = 0.995).*
We compared the proliferation rate in two cell lines (Caco-2, and BxPC-3) after treatment with different concentrations of DCM-NH-Pro-Gly and DMSO at several times (see Figure S27). From the results obtained, we conclude that the DCM-NH-Pro-Gly compound is not toxic in Caco-2 cells, while for BxPC-3 cells the possible minimal toxicity is due to the DMSO in which DCM-NH-Pro-*Gly is* dissolved, and not to the chemical structure of this sensor itself.
Finally, to confirm that DPP IV activity is responsible for the generation of red fluorescence, we measured the kinetics of the R/G ratio over 30 min in live Caco-2 cells in the presence and absence of sitagliptin. Figure 5A shows the ratio R/G images of a representative sample. A lower transition to high values is observable in the color scale selected in the cell culture with sitagliptin. Figure 5B represents the increase in the R/G ratio with respect to time. The addition of the inhibitor produces a slower rate reaction, as can be observed by the initial slope of both curves, and the production of a lower quantity of the product of the reaction, DCM-NH2, was observable in the final plateau value achieved in both cases.
**Figure 5:** *(A) R/G ratio maps of the live Caco-2 cell line at different
times
after adding DCM-NH-Pro-Gly (5 μM) without (top) and with (bottom)
the DPP IV inhibitor sitagliptin (50 μM). λex = 450 nm. Ratio image is calculated by dividing red (λem = 648–722 nm) and green (λem = 533–557
nm) channels. (B) Representation of the R/G ratios from microscopy
images without (circles) and with (squares) sitagliptin. Scale bars
represent 10 μm.*
DCM-NH2 was reported to be usable in superresolution imaging and using two-photon excitation in bacterial bodies.4 With respect to superresolution, we checked the ability to use them in eukaryotic cells. The good resolution achieved with confocal microscopy due to the excellent fluorescent response of the probe makes the improvement in resolution accomplished with superresolution microscopy only slightly higher (see Figure S28 in the SI). On the other hand, we confirmed its ability to use in TPM by measuring live Caco-2 cells at an excitation wavelength of 800 nm. Figure 6A shows representative two-photon excitation images obtained. All of the images obtained by the various fluorescence microscopy techniques used show an intracellular accumulation in organelles of DCM-NH2, as described in the literature.55
**Figure 6:** *(A) Images obtained from live Caco-2 cells with two-photon excitation
at 800 nm (λem = 650–720 nm). Scale bars represent
5 μm. (B) Representative image of the red channel obtained from
BxPC-3 tumors using two-photon microscopy with excitation at 800 nm
(λem = 650–720 nm). Scale bars are 10 μm.
(C) Representative images of the intensity red (λem = 650–720 nm) and green (λem = 502–538
nm) channels and the ratio R/G images of BxPC-3 tumors after adding
DCM-NH-Pro-Gly (10 μM) using two-photon microscopy with excitation
at 800 nm. Scale bars are 10 μm. (D) Representation of the R/G
ratio values from microscopy images. Boxes represent the 25th, 50th,
and 75th percentiles. Whiskers represent the SE.*
Therefore, our imaging experiments with cell cultures, confirm the applicability of this probe as an intracellular sensor of DPP IV activity. These findings, combining the ability for two-photon excitation and the NIR emission of DCM-NH2, are promising characteristics to go further in biological applications.
## Imaging of
DPP IV in Human Tumor-Bearing Pancreas Tissues
The imaging applied in tissues can provide important utilities in multiple aspects, including medical applications such as diagnosis or surgery. Taking advantage of this probe, we examined the ability of DCM-NH-Pro-Gly to detect DPP IV activity in tissues. For this purpose, we selected BxPC-3 tumors to test whether we could detect the NIR emission of DCM-NH2. We incubated the tissues in PBS with a solution of DCM-NH2 for 24 h. After this period, we washed with PBS and measured the emission intensity with two-photon excitation at 800 nm. Our data show a strong time-dependent increase of the average fluorescence intensity compared to control samples, which showed an almost negligible NIR emission (see Figure 6B,C).
Once we checked that we were able to recover the emission of DCM-NH2, we next incubated the tissues with the DCM-NH-Pro-Gly probe, and we measured the green and NIR emission just after 1, 2, and 6 h of incubation.
We observed again a negligible autofluorescence, whereas the probe at the initial time (1 h) showed a green and NIR emission with a similar intensity. However, after 2 h of incubation, we measured an increase in the NIR emission, whereas the green channel keeps with similar fluorescence. This behavior continues after 6 h of incubation. This increase should correspond with the cleavage of the probe DCM-NH-Pro-Gly due to the presence of DPP IV activity in the cell of the tissue releasing the compound DCM-NH2 (Figure 6C).
Finally, we represented the red-to-green ratio images at different incubation times with DCM-NH-Pro-Gly. As shown in Figure 6C, and as expected, the images showed a change in the ratio. Figure 6D shows the changes in the ratio values at these three incubation times. In a period of 6 h, we measured an increase in the ratio from ∼1.0 to ∼1.8.
Therefore, our results confirm the successful use of DCM-NH-Pro-Gly to detect DPP IV activity in ex vivo tissues.
## In Vivo Imaging of DPP IV in Zebrafish
Recently, DPP IV activity was found in zebrafish embryos and larvae; however, in this study, DPP IV activity was determined only qualitatively.36,51 *In this* work, we have gone farther, and we have completed, as far we know, for the first time, a quantitative study of the differences in DPP IV activity during 1, 3, 5, and 7 dpf in zebrafish embryos and larvae.
While zebrafish embryos and larvae control incubated with DMSO showed very little red autofluorescence in the yolk sac and eye (Figure S29 in the SI), all zebrafish stages incubated with the probe metabolized the original green substrate to a strong red and far red fluorescent derivative in both yolk sac and embryonic tissues (Figure 7). Zebrafish embryos at 1 dpf showed red fluorescence mainly in the yolk sac (Figure 7A, upper line), indicating high activity of DPP IV and/or high permeability of the yolk membrane for the probe. Zebrafish larvae at 3 dpf showed red fluorescence in the yolk sac and start to display an accumulation of red fluorescent metabolites in other larval tissues (Figure 7A, second line). In the older developmental stage, we measured (5 dpf, 7 dpf), the more red fluorescent derivative was generated in the fish (Figure 7A, third and fourth lines), indicating an increase in the levels of DPP IV activity with developmental stage. The most affected larval tissues are the central nervous system (CNS), especially the midbrain, hindbrain and spinal cord, eye, and inner organs, such as the throat and digestive tract. In contrast, skin and muscles did not show strong red fluorescent signals (Figure 7A,B). DPP IV is therefore predicted to be active in the yolk sac from zebrafish embryonic stages onward, while in larval stages, the enzyme is possibly active in the yolk sac, inner organs, and the CNS. The same pattern of red metabolite accumulation was also observed by confocal microscopy measurements (see Figures S30 and S31).
**Figure 7:** *(A) Living zebrafish
embryos and larvae incubated with 5 μM
DCM-NH-Pro-Gly for 2 h at different dpf; red fluorescent (left), brightfield
(center), and merge (right) images are shown measured by the stereo
microscope (λex = 458 nm, λem =
680 nm). Scale bars: 1 dpf: 250 μm, 3–7 dpf: 500 μm.
(B) Detail (head with central nervous system) of a living zebrafish
larva at 5 dpf. Scale bars: 200 μm. (C) Living zebrafish larva
at 5 dpf preincubated for 3 h with 250 μM sitagliptin and incubated
with 5 μM DCM-NH-Pro-Gly for 2 h. (D) Intensity values of NIR
emission of zebrafish at different dpf, incubated with 5 μM
DCM-NH-Pro-Gly in the presence or absence of the inhibitor sitagliptin.
Boxes represent the 25th, 50th, and 75th percentiles. Whiskers represent
the SE.*
Additionally, a suitable control was performed with sitagliptin, as a DPP IV inhibitor, to be certain that the red fluorescence is due to the product generated after the enzyme action. For this purpose, we used a 5 dpf zebrafish that was preincubated with the inhibitor and then incubated with the same substrate concentration and for the same time. Indeed, the images obtained with the 5 dpf zebrafish that was not inhibited prior to the addition of the substrate display a strongly reduced red fluorescence (Figure 7C).
In addition to qualitatively describing the accumulation of red DCM-NH2 released in the different zebrafish tissues, we quantified the intensity coming from the NIR emission of the compound from the images recovered with the stereo microscope at different dpf. Our data showed a similar activity (with negligible differences) at 1 and 3 dpf, though a slight increment of the red emission in the 3 dpf with respect to the 1 dpf can be appreciated, which could indicate a small increase of DPP IV activity with respect to the previous stage.
However, the highest difference in red emission appears during 5 and 7 dpf. Our analysis showed a significant increase in DPP IV activity with respect to the previous stages. The 5 dpf timepoint showed a robust increase in NIR emission intensity over 3 dpf, and we measured the highest intensity value achieved at 7 dpf. Our data are compatible with an increase in DPP IV activity at 5 dpf larvae with a maximum enzymatic activity at 7 dpf larvae (see Figure 7D).
The inhibited 5 dpf zebrafish shows a lower red NIR intensity than the same stage one without inhibitor (see Figure 7D).
## Conclusions
By conjugating the enzyme-recognizing group (Gly-Pro) to the fluorophore (dicyanomethylene-4H-pyran derivative, DCM-NH2), we synthesized a DPP IV-sensitive and highly specific fluorescent substrate. When the dipeptide group is released from the probe, the donor–acceptor DCM-NH2 system is restored, showing the NIR characteristic ICT emission spectrum, which allows us to obtain a ratiometric fluorescence output between the green fluorescent signal of the substrate and the NIR signal of DCM-NH2.
Both the substrate and probe are capable of being excited by two NIR photons, which has made it possible to eliminate the green and red bands of autofluorescence from raw plasma and to propose a unique and novel methodology to analyze the activity of DPP IV in raw plasma from diabetic patients. Moreover, the applicability of this new probe as an intracellular in vivo sensor of DPP IV activity, as well as its ability to obtain clear fluorescence microscopy images of tumor tissues when excited by two photons, has been confirmed. In addition, the fluorophore released by enzymatic action has been found to be suitable for superresolution fluorescence microscopy imaging. Finally, zebrafish embryos and larvae at 1, 3, 5, and 7 dpf were incubated with the probe. Zebrafish embryos at 1 dpf show red fluorescence mainly in the yolk sac, while older zebrafish larvae show red fluorescence in the yolk sac and in the central nervous system, eye, throat, and digestive tract. We quantified the NIR intensity values at the different stages, with the oldest fish being the most fluorescent in the red, indicating an increase in DPP IV levels with developmental stages. All of these findings make it promising to apply our probe in other biologically relevant situations where DPP IV is overexpressed, like cancer and diabetes.
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|
---
title: Total and cause-specific mortality associated with meat intake in a large cohort
study in Korea
authors:
- Anthony Kityo
- Sang-Ah Lee
- Daehee Kang
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10043978
doi: 10.3389/fnut.2023.1138102
license: CC BY 4.0
---
# Total and cause-specific mortality associated with meat intake in a large cohort study in Korea
## Abstract
### Background
Asia has experienced a large increase in meat intake in the past decade, yet the health impact of meat intake is not well studied.
### Objective
We examined the association of meat intake with all-cause, cancer and cardiovascular disease (CVD) mortality in an Asian country.
### Methods
Participants were 113,568 adults with dietary data at recruitment (2004–2013) of the Health Examinees-Gem (HEXA-G) study, a prospective cohort study conducted in 8 regions of Korea. Participants were followed until 31 December 2020. Total, red, white, and organ meat intake were computed based on a 106-item questionnaire. Multivariable Cox proportional hazard models were implemented using the lowest quintile of meat intake as the reference category.
### Findings
For 1,205,236 person-years, 3,454 deaths were recorded. High intake of processed red meat was positively associated with all-cause mortality [men: hazard ratio (HR) 1.21, $95\%$ confidence interval ($95\%$ CI) 1.07–1.37; women: HR 1.32, $95\%$ CI 1.12–1.56]. Increased risk of all-cause mortality (HR 1.21, $95\%$ CI 1.05–1.39) and cancer mortality (HR 1.24, $95\%$ CI 1.03–1.50) was observed in women with high intake of organ meat. Moderate intake of pork belly was associated with reduced risk of all-cause mortality in men (HR 0.76, $95\%$ CI 0.62–0.93) and women (HR 0.83, $95\%$ 0.69–0.98) but high intake was associated with increased risk of CVD mortality in women (HR 1.84, $95\%$ CI 1.20–2.82). Low beef intake decreased the risk of CVD mortality in men (HR 0.58, $95\%$ CI 0.40–0.84), but roasted pork increased cancer mortality in women (HR 1.26, $95\%$ CI 1.05–1.52).
### Conclusion
There was increased risk of all-cause mortality associated with intake of processed red meat in men and women, increased risk of all-cause and cancer mortality with intake of organ meat in women, and increased risk of cancer mortality with intake of roasted pork intake in women. High intake of pork belly increased the risk of CVD mortality in women, but moderate intake was inversely associated with mortality from all-causes in both men and women.
## 1. Introduction
Estimates from the 2022 Global Nutrition Report indicated that close to two-thirds of avoidable deaths were attributed to sub-optimal dietary composition, including $9\%$ attributed to high intake of meat, and $8\%$ attributed to processed meat [1]. The Asian region has experienced the third largest increase in red and processed red meat intake in the past decade [1], suggesting that this region is experiencing a nutritional transition toward a Westernized dietary pattern. Nonetheless, various aspects of the traditional dietary pattern that emphasize high intake of plant foods and moderate intake of fat have been retained in Asian countries like Korea [2, 3]. Accordingly, there is need to understand the health impacts of meat intake in Asian countries considering their unique dietary culture. Cohort studies and meta-analyses have reported a positive association of red and processed meat intake with all-cause and cause-specific mortality mainly in Europe and North America (4–12).
However, pooled data from Asian countries indicated an inverse association between red meat intake and mortality [13]. Conversely a recent cohort study from Japan reported an increased risk of total and CVD mortality with heavy intake of total and red meat, a low risk of stroke mortality with high intake of total meat and a reduced risk of all-cause and cancer mortality in men with moderate intake of processed meat [14]. Different results between Asian and Western countries have been explained by low intake of meat in Asian vs. Western countries [11, 13, 14]. However, red and processed meat intake in an Adventist population with low intake of meat, increased the risk of mortality [4]. Previous studies in Asian populations neither differentiated processed and unprocessed red meat [13], nor accounted for methods of preparation which are also different from those in the West (15–17). On the other hand, the association between white meat consumption and mortality is inconclusive with some studies reporting inverse associations [7, 8, 10, 13, 14] and others reporting null associations [9, 11, 12].
The Health Examinees (HEXA) cohort is a large prospective study of over 170,000 adults aged above 40 years who were recruited from 38 regions of Korea between 2004 and 2013. Using this data set, we comprehensively examined meat intake by type, degree of processing, and method of preparation in relation to death from all-causes and specific causes.
## 2.1. The Health Examinees-Gem Study
We conducted an observational analysis of the HEXA cohort, a prospective population-based sub-cohort within the Korean Genome and Epidemiology study (KoGEs) that was established to investigate the etiological factors of complex diseases [18]. The HEXA recruited participants between 2004 and 2013 at 38 health examination centers and training hospitals located in the eight regions of Korea. The study details have been published elsewhere [19]. Ethical approval was obtained from the Ethics Committee of the Korean Health and Genomic Study of the Korean National Institute of Health and the Institutional Review Boards of all participating hospitals (IRB no. E-1503-103- 657). All participants provided written informed consent before their participation.
## 2.2.1. Assessment of dietary intake
Dietary intake was assessed once at baseline using a 106-food item semiquantitative food frequency questionnaire (SQFFQ) that had been tested for reproducibility and validity using 12-day dietary records obtained from 124 participants [18]. Food consumption frequencies were classified into nine levels (from “never” to “three times or more a day”), and portion sizes were classified into three levels (one-half, one, and one and a half servings). Energy and macronutrient content of each item was estimated using a food composition table developed by the Korean Rural Development Administration (RDA) [19]. Meat items on the SQFFQ were pork (roasted and braised), and pork belly; edible viscera/organ meat; processed meat; beef (steak/roasted), beef soup with bones, and beef soup with vegetables; dog meat; and chicken (fried/stew). For mixed dishes which contained meat, we extracted meat weight by applying weights using the food recipe information. The applied weights represent the % weight contributed by a meat item to the mixed dish. Red meat included beef, pork, and dog meat while white meat included chicken.
## 2.2.2. Baseline covariates
Educational level, and household income were assessed in addition to demographic characteristics such as age and sex. Other covariates included lifestyle factors such as: smoking, where current smokers were defined as participants who had smoked more than four hundred cigarettes during their lifetime and were still smoking [20]; drinking-categorized into current alcohol drinkers, past drinkers and never drinkers. Current alcohol drinkers were those who reported that they had ever drunk alcohol and were still drinking at the time of the interview. Regular physical exercise was assessed by asking participants to report [1] whether they engage in regular physical exercise that causes body sweating; [2] the number of times they engage in these exercises in a week (1–2 times/week to everyday); and [3] the duration of the exercise. Regular exercise was defined as engaging in activities that caused body sweating for at least five times a week lasting at least 30 min per session.
Weight and height were objectively measured at baseline by trained medical staff. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2). BMI was categorized into four classes based on the WHO classification of BMI for Asian adults: < 18.5, 18.5–22.9, 23–24.9, 25.0–29.9, and ≥30.0 kg/m2 [21]. The information about diseases and use of medication was reported by participants through a standardized questionnaire that was administered by trained staff, and was used to define prevalent cancer, cardiovascular, cerebral vascular, respiratory and gastrointestinal diseases. Chronic kidney disease was diagnosed using estimated glomerular filtration rate (eGFR < 60 mL/min/1.73 m2) that was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) [22]. Diabetes was defined as fasting blood glucose ≥126 mg/dL or drug treatment for elevated fasting blood glucose, hypertension was defined as systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or drug treatment for elevated blood pressure. Abdominal obesity was defined as waist circumference ≥90 cm for men and ≥80 cm for women [23]. The National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria was used to define metabolic syndrome [24]. For each chronic disease, participants were assigned a score of 1 (one) in the presence and a score of zero [0] in the absence of each disease. These scores were summed across all the diseases to create a disease score. The disease score was then classified as: zero (no disease) and ≥1 (having atleast a disease).
## 2.2.3. Ascertainment of mortality
The date and causes of death from 2004 to 31 December 2020, were ascertained through linkage to the death certificate data base of the Korean National Statistical Office. The deaths of participants on Medicaid were ascertained through linkage to the National Health Insurance Service. Participants' unique identifiers were used to add mortality data from Statistics Korea. For cause-specific mortality, the 10th revision of the International Classification of Disease (ICD-10) codes was used. ICD-10 code C00-C97 and I00-I99 were used to classify cancer and CVD-specific deaths, respectively.
## 2.2.4. Statistical analysis
Participants were divided into sex-specific quintiles of total meat intake and intake of meat subtypes. The main outcome variable was death from any cause, cancer or CVD. Missing data were replaced by the mode for categorical variables and by the median for BMI. The distribution of participant characteristics according to quintiles of total meat intake was described using percentages for categorical variables or least square means for continuous variables and was stratified by sex.
Follow-up time was calculated for each participant starting from the date of recruitment until the date of death. Participants who did not experience the event were censored on 31st December 2020. We examined the association of meat intake with all-cause, cancer and CVD mortality using multivariable cox proportional hazard models. Adjusted hazard ratios (HRs) and $95\%$ confidence intervals for meat intake, using quartile one (Q1) as the reference category, were computed and adjusted for age (continuous), demographic factors (marital status, educational level, job, and monthly household income); lifestyle factors (total energy intake, smoking, drinking, and regular physical exercise); and health-related factors (BMI and history of chronic disease). The proportional hazards assumption was assessed by including multiplicative terms between variables and follow-up time and was not violated ($p \leq 0.05$ for all categories).
We stratified the analyses by sex since meat intake is higher in men than women. In addition, we conducted sensitivity analysis [1] by excluding participants who died within 1 year of follow-up, to avoid latent period bias and reverse causation; [2] adjusting for alcohol intake in grams/day (continuous) to test residual confounding from alcohol intake; and [3] adjusting for selected dietary variables that are associated with meat intake (vegetables, fish, seafood, and legumes).
All data were analyzed using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA), and $P \leq 0.05$ was used to define statistical significance.
## 3. Results
The HEXA-Gem (HEXA-G) ($$n = 141$$,968) sample was derived from the original HEXA study ($$n = 173$$,195) by excluding: [1] sites that only participated in the pilot study from 2004 to 2006; [2] sites that did not meet the HEXA standards for biospecimen quality control; [3] sites that participated in the study for < 2 years [25] ($$n = 31$$,306), those who withdrew from the study ($$n = 5$$), and those younger than 40 or older than 69 years ($$n = 2$$,626). A total of 22,578 participants did not consent to record linkage and were excluded from the analysis. Furthermore, 3,112 participants were excluded due to missing data on dietary intake ($$n = 1$$,379) and implausible energy reporting ($$n = 1$$,733) leaving 113,568 participants for primary analysis (Figure 1).
**Figure 1:** *Selection of study participants.*
The mean age at baseline was 53.5 years (SE 8.2), 38,847 ($34\%$) were men and 74,721 ($66\%$) were women. For 1,205,236 person-years [median follow-up of 10.6 (9.5–11.9) years], 3,454, 1,720, and 539 total, cancer and CVD deaths, respectively, were recorded. The highest consumers of total meat were more likely to be younger, highly educated, and had high income. In addition, individuals in the highest quartiles of total meat intake were current drinkers, less likely to engage in regular exercise and had a higher BMI in men and low BMI in women (Table 1).
**Table 1**
| Unnamed: 0 | Quintiles of total meat intake (g/day) | Quintiles of total meat intake (g/day).1 | Quintiles of total meat intake (g/day).2 | Quintiles of total meat intake (g/day).3 | Quintiles of total meat intake (g/day).4 | Quintiles of total meat intake (g/day).5 | Quintiles of total meat intake (g/day).6 | Quintiles of total meat intake (g/day).7 | Quintiles of total meat intake (g/day).8 | Unnamed: 10 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Men | Men | Men | Men | Men | Women | Women | Women | Women | Women |
| | Q1 | Q2 | Q3 | Q4 | Q5 | Q1 | Q2 | Q3 | Q4 | Q5 |
| n | 7756 | 7783 | 7769 | 7770 | 7769 | 14945 | 14943 | 14942 | 14940 | 14951 |
| Age, years, mean ± SEM | 56.8 ± 0.1 | 54.6 ± 0.1 | 53.2 ± 0.1 | 52.3 ± 0.1 | 51.7 ± 0.1 | 55.4 ± 0.1 | 53.4 ± 0.1 | 52 ± 0.1 | 51 ± 0.1 | 50.3 ± 0.1 |
| Education, ≥college | 35.4 | 40.3 | 43.9 | 45.4 | 44.2 | 16.3 | 20.0 | 23.5 | 27.0 | 27.8 |
| Income, ≥3,000 USD | 37.6 | 44.5 | 49.5 | 51.0 | 51.3 | 29.2 | 36.6 | 42.0 | 46.3 | 46.2 |
| Married | 94.1 | 94.3 | 95.0 | 94.0 | 94.3 | 82.3 | 86.4 | 88.0 | 89.1 | 90.5 |
| Seoul resident | 32.8 | 37.9 | 39.5 | 38.8 | 36.3 | 30.2 | 33.7 | 35.7 | 37.3 | 34.5 |
| Current smoker | 44.0 | 42.6 | 41.4 | 40.1 | 37.7 | 1.2 | 1.1 | 1.2 | 1.3 | 1.4 |
| Past smoker | 24.4 | 28.8 | 31.7 | 33.8 | 37.8 | 1.9 | 1.9 | 2.4 | 2.3 | 2.9 |
| Current drinker | 60.1 | 70.7 | 76.4 | 77.0 | 79.6 | 19.6 | 27.6 | 32.3 | 35.8 | 38.6 |
| Past drinker | 10.9 | 7.8 | 6.0 | 5.4 | 5.4 | 2.2 | 1.8 | 1.7 | 1.8 | 1.8 |
| Regular exercise | 39.4 | 36.0 | 36.0 | 34.4 | 33.5 | 38.1 | 37.1 | 36.1 | 35.2 | 33.1 |
| BMI, ≥25.0 kg/m2 | 36.7 | 37.8 | 39.4 | 41.3 | 43.9 | 30.1 | 28.5 | 27.6 | 27.1 | 26.9 |
| History of disease | History of disease | History of disease | History of disease | History of disease | History of disease | History of disease | History of disease | History of disease | History of disease | History of disease |
| Cancer | 3.6 | 2.4 | 1.9 | 1.7 | 1.6 | 5.6 | 4.4 | 3.2 | 3.0 | 2.7 |
| Hypertension | 54.5 | 53.2 | 51.8 | 52 | 50.9 | 44.5 | 38.9 | 36.1 | 33.3 | 32.2 |
| Diabetes | 12.9 | 11.7 | 10.5 | 9.6 | 10.7 | 7.9 | 5.8 | 5.4 | 4.7 | 4.6 |
| Metabolic syndrome | 20.4 | 19.8 | 20.0 | 20.2 | 20.5 | 23.6 | 19.5 | 17.9 | 16.0 | 15.6 |
| CKD | 7.5 | 6.5 | 5.6 | 5.6 | 5.3 | 7.5 | 6.5 | 5.6 | 5.6 | 5.3 |
| Heart disease | 1.5 | 0.9 | 0.7 | 0.8 | 0.6 | 0.8 | 0.4 | 0.3 | 0.3 | 0.2 |
| Disease score, 1 | 6.8 | 5.0 | 4.1 | 3.8 | 3.0 | 6.8 | 5.0 | 4.1 | 3.8 | 3.0 |
| Total deaths, n | 566 | 424 | 372 | 396 | 308 | 395 | 278 | 264 | 228 | 223 |
| Cancer deaths, n | 251 | 199 | 176 | 191 | 135 | 198 | 152 | 151 | 137 | 130 |
| CVD deaths, n | 86 | 63 | 65 | 69 | 50 | 62 | 40 | 36 | 35 | 33 |
| Energy intake, Kcal/d, mean | 1569.8 | 1682.1 | 1801.6 | 1941.8 | 2214.9 | 1423.5 | 1528 | 1634.5 | 1766.8 | 2044.3 |
| Total meat, g/d, median | 12.7 | 28.2 | 44.3 | 68.7 | 123.1 | 6.3 | 17.5 | 29.8 | 48.8 | 94.3 |
| Menopausal women | | | | | | 77.3 | 68.0 | 60.6 | 55.4 | 51.5 |
| Hormonal therapy user | | | | | | 4.5 | 4.2 | 3.9 | 4.0 | 3.7 |
| Oral contraceptive user | | | | | | 82.0 | 82.5 | 83.5 | 84 | 84.6 |
Total energy intake was high in the highest quintile of meat intake. The proportion of individuals with chronic diseases, menopausal women, and users of postmenopausal hormonal therapy was low among highest consumers of total meat (Table 1). In addition, vegetable, fish, and sea food intake were high among highest consumers of total meat (Supplementary Table 1). The distribution of meat intake by sex are displayed in Supplementary Table 2. The median intake of total meat was 44.3 g/day in men and 29.7 g/day in women. Red meat contributed over $90\%$ to total meat intake, and $98\%$ of red meat intake was unprocessed in both genders.
Total meat, total red meat or white meat intake was not associated with all-cause mortality in both men and women (Figures 2, 3). However, high intake of processed red meat was positively associated with all-cause mortality in men [hazard ratio (HR) 1.21, $95\%$ confidence interval ($95\%$ CI) 1.07–1.37] and women (HR 1.32, $95\%$ CI 1.12–1.56). Moderate intake of pork belly was inversely associated with all-cause mortality (men, HR 0.76, $95\%$ CI 0.62–0.93; women, HR 0.82, $95\%$ CI 0.69–0.89) (Figures 2, 3), but high intake was positively associated with CVD mortality in women (HR 1.84, $95\%$ CI 1.20–2.82) (Figure 3). There was a low risk of CVD mortality in men with low beef intake (HR 0.58, $95\%$ CI 0.40–0.84). However, women with high intake of organ meat/meat viscera had a high risk of all-cause (HR 1.21, $95\%$ CI 1.05–1.39) and cancer mortality (HR 1.24, $95\%$ CI) 1.03–1.50 (Figure 3). These associations persisted in sensitivity analyses (Supplementary Tables 7, 8).
**Figure 2:** *Associations of meat intake with all-cause and cause-specific mortality in the HEXA-G study in men. Points are hazard ratios, and lines represent 95% confidence interval. Models were adjusted for baseline age, marital status, education, income, job, smoking, drinking, regular physical exercise, total energy intake, total meat intake (for specific meat types), history of chronic diseases, and body mass index. RM, Red meat. 95% confidence intervals, sample sizes and person years are shown in Supplementary Table 3.* **Figure 3:** *Associations of meat intake with all-cause and cause-specific mortality in the HEXA-G study in women. Points are hazard ratios, and lines represent 95% confidence interval. Models were adjusted for baseline age, marital status, education, income, job, smoking, drinking, regular physical exercise, total energy intake, total meat intake (for specific meat types), history of chronic diseases, body mass index, and menopausal status. RM, Red meat. 95% confidence intervals, sample sizes and person-years are shown in Supplementary Table 4.*
When the analyses were extended to meat intake according to preparation method, a high risk of cancer mortality was observed in women with the highest intake of roasted pork (HR 1.26) (Table 2).
**Table 2**
| Unnamed: 0 | All-cause mortality | All-cause mortality.1 | All-cause mortality.2 | Cancer mortality | Cancer mortality.1 | CVD mortality | CVD mortality.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Men | Person-years | Deaths | HR (95% CI) | Deaths | HR (95% CI) | Deaths | HR (95% CI) |
| Roasted pork | 89519 | 362 | 0.95 (0.82–1.09) | 163 | 0.92 (0.75–1.13) | 57 | 0.81 (0.57–1.14) |
| Braised pork | 100294 | 416 | 1.06 (0.94–1.19) | 197 | 1.12 (0.94–1.33) | 69 | 1.05 (0.78–1.42) |
| Beef steak | 106160 | 448 | 0.97 (0.86–1.10) | 221 | 1.06 (0.89–1.27) | 66 | 0.83 (0.60–1.13) |
| Beef soup | 126615 | 483 | 1.00 (0.89–1.13) | 225 | 0.93 (0.78–1.11) | 85 | 1.01 (0.76–1.36) |
| Beef in vegetable soup | 85649 | 404 | 1.01 (0.88–1.15) | 136 | 0.99 (0.81–1.20) | 69 | 1.12 (0.80–1.57) |
| Fried chicken | 131345 | 505 | 1.02 (0.90–1.17) | 163 | 0.96 (0.79–1.17) | 94 | 1.25 (0.91–1.72) |
| Women | Women | Women | Women | Women | Women | Women | Women |
| Roasted pork | 240774 | 362 | 1.10 (0.95–1.26) | 222 | 1.26 (1.05–1.52) | 53 | 1.16 (0.81–1.67) |
| Braised pork | 327391 | 191 | 1.05 (0.93–1.19) | 284 | 1.08 (0.92–1.26) | 66 | 1.01 (0.74–1.38) |
| Beef steak | 190597 | 283 | 0.98 (0.84–1.14) | 168 | 0.99 (0.81–1.20) | 39 | 1.12 (0.75–1.67) |
| Beef soup | 340717 | 207 | 1.04 (0.93–1.17) | 328 | 1.06 (0.91–1.23) | 79 | 0.99 (0.74–1.33) |
| Beef in vegetable soup | 137751 | 227 | 1.02 (0.86–1.20) | 207 | 1.06 (0.85–1.32) | 29 | 0.96 (0.61–1.50) |
| Fried chicken | 209790 | 280 | 0.93 (0.79–1.09) | 178 | 0.96 (0.77–1.20) | 41 | 1.07 (0.70–1.62) |
## 4. Discussion
Although the associations of meat intake with mortality, particularly red meat and processed meat are well recognized in Western populations where meat intake is a dominant part of the diet, the relationship between meat intake and mortality in Asian populations that have low absolute meat intake and diverse meat preparation methods is not well characterized. In this large cohort from an Asian population, the novel aspect is the evaluation of meat intake by type, and preparation method in relation to total and cause-specific mortality.
High intake of processed red meat was positively associated with all-cause mortality in men and women, and high intake of organ meat was positively associated with all cause and cancer specific mortality in women. However, moderate intake of pork belly was inversely associated with all-cause mortality, but high intake was positively associated with a high risk of CVD mortality in men. Moreover, low beef intake was inversely associated with CVD mortality in men. Considering different meat dishes, roasted pork ribs and pork belly were associated with an increased risk of cancer mortality in women. No evidence of association was reported with white meat.
Total meat was not associated with all-cause mortality in both men and women in line with pooled data [13] and previous meta-analyses in Asian populations [7, 11]. Thus, it has been suggested that meat types should be treated separately when analyzing their health effects [7, 11]. Total red meat intake was inversely associated with all-cause mortality using pooled data from Asian studies [13] but was not associated with mortality in our analysis. In the former, the study did not distinguish processed from unprocessed red meat, and failed to account for differences in preparation methods. In previous studies, the low intake of red meat and processed meat in Asia has been suggested as one of the possible explanations for null or inverse associations between meat intake and health outcomes [11, 26, 27], with some authors arguing that the consumption of processed and red meat in *Korea is* not a cause for health concerns. Yet, even in the Adventist community with low intake of meat, the intake of red and processed meat was associated with all-cause and CVD mortality [4]. In the current study using data from the Korean population with low meat intake, processed red meat was positively associated with all-cause mortality suggesting that processed meat may have detrimental health effects even at lower intake amounts. The positive association of processed meat with all-cause and CVD mortality is widely reported in Western populations (4, 6, 10–12, 27, 28). Processed, but not unprocessed red meat intake was associated with total and CVD mortality using data from 21 countries [9], and with all-cause mortality in two meta-analyses [27, 28].
Iron mutagens generated by high-temperature cooking [29, 30], N-nitroso compounds formed in processed meat and endogenously from heme iron [31, 32] are some of the mechanisms that may explain the detrimental health impacts of processed red meat. In animal studies, metabolism by intestinal microbiota of dietary L-carnitine-a trimethylamine abundant in red meat, also produces Trimethylamine Oxide (TMAO) and accelerates atherosclerosis [33]. Processed red meats are also high in food additives especially nitrite/nitrates. Nitrates/nitrites in processed meat mediated up to $72\%$ of the association between processed meat intake and mortality [10].
Moderate intake of pork belly was inversely associated with all-cause mortality, and low intake of beef appeared to be protective against CVD mortality in men. These associations could be attributed to beef preparation methods in the Korean population. Meat is consumed roasted or in soup or stews, preferably with soybean paste. Beef is popularly prepared as “Bulgogi”- grilled beef flavored with garlic, onions, soy sauce, and sesame oil; and pork is prepared by steaming, stewing, boiling, or smoking [16]. In Korea, pork is the most consumed red meat and there is a unique preference for pork belly (“Sam-gyeop-sal”) among Korean consumers [34]. In Western countries, pork belly is primarily cured and processed as bacon, but consumers in South Korea favor grilled or roasted bellies rather than cured or processed bacon [16]. Thus, pork belly consumed in South *Korea is* lower in saturated fat than that consumed in Western countries due to different preparation methods [34]. It should also be noted that consumption of pork belly and beef are common at social gatherings among the middle- and high-income class in the Korean society. Thus, the consumption of these meats could reflect high socio-economic status and high social capital in the Korean population. Our results suggest that moderate intake of unprocessed pork belly and beef may offer protective benefits against premature mortality.
When we considered meat preparation methods, the intake of roasted pork increased the risk of cancer mortality in women. Iron mutagens generated by high-temperature cooking are possible explanations of these associations [29, 30]. In addition, direct frying or grilling of meat generates mutagenic Heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs) [30] which have been linked to the development of several cancers (35–40).
Results from a recent meta-analysis reported a $6\%$ reduction in all-cause mortality with high intake of white meat and a null association with CVD-mortality in Asian populations [7]. Furthermore, a recent study from Japan reported a reduced risk of cancer mortality with increased intake of chicken in men [14]. In the NIH-AARP Diet and Health Study, white meat intake was inversely associated with all-cause and cause-specific mortality [10]. Poultry meat contains more unsaturated fat, and has a lower content of saturated fatty acids, heme iron, glycotoxins and sodium, which may be involved in oxidative stress, and atherosclerosis [10, 31, 32, 41]. Unlike red meat, white meat does not form N-nitroso compounds [42, 43], and it has been suggested that this could possibly explain the inverse association between white meat intake and mortality risk. Our finding that white meat intake is not associated with mortality does not agree with pooled data from Asian studies that showed that chicken intake was inversely associated with reduced risk of all-cause mortality in men [13], but agrees with several studies that reported a null association between white meat intake and all-cause or CVD mortality [6, 9, 11, 12].
Several limitations should be considered while interpreting these findings. The observational study design precludes confirmation of causal relationships. The inclusion of old adults limits generalizability of our findings to young individuals. The possibility of residual confounding cannot be ruled out even though we adjusted for multiple confounders. We relied on a single dietary assessment at recruitment, and changes in meat intake over time were not evaluated. Nevertheless, we conducted sensitivity analyses by excluding participants with shorter follow-up durations. Dietary data were self-reported, which could have introduced measurement error and biased our results toward the null.
The main strengths of our study include the use of a large sample size from a population-based survey which increases generalizability to the Korean population, the comprehensive evaluation of meat intake by type, cooking and degree of processing in a less-studied population with low meat intake, the prospective study design, adjustment for potential confounding variables, use of a validated SQFFQ, and conducting several sensitivity analyses.
## 5. Conclusion
This study highlighted the unique features of meat consumption patterns in the Korean population, and that the type of red meat, and the preparation methods should be considered in future studies and in designing public health guidelines pertaining to meat intake in this population. The results suggested that processed red meat increased mortality risk from all-causes in men and women, and high intake of organ meat is positively associated with all-cause and cancer specific mortality in women. However, moderate intake of pork belly was inversely associated with all-cause mortality, and low beef intake was inversely associated with CVD mortality in men. Considering different meat dishes, roasted pork ribs and pork belly were associated with increased risk of cancer in women. No evidence of association was reported with white meat.
## Data availability statement
The datasets presented in this article are not readily available because the dataset used for the analysis in this study is maintained and managed by the Division of Population Health Research at the National Institute of Health, Korea Centers for Disease Control and Prevention. The Health Examinees Study dataset has been merged with the cancer registry data provided by National Cancer Center of Korea in a collaborative agreement. It contains some personal data that may potentially be sensitive to the patients, even though researchers are provided with an anonymized dataset that excludes resident registration numbers. Other researchers may request access to the data by contacting the following individuals at the Division of Population Health Research, National Institute of Health, Korea Centers for Disease Control and Prevention: Requests to access the datasets should be directed to Dr. Kyoungho Lee (khlee3789@korea.kr).
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the Korean Health and Genomic Study of the Korean National Institute of Health and the Institutional Review Boards of all participating hospitals (IRB no. E-1503-103- 657). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AK, S-AL, and DK designed the research. AK conducted the research, analyzed the data, and wrote the paper. S-AL and DK had primary responsibility for final content. All authors approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1138102/full#supplementary-material
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32. Hughes R, Cross AJ, Pollock JRA, Bingham S. **Dose-dependent effect of dietary meat on endogenous colonic N-nitrosation**. *Carcinogenesis.* (2001.0) **22** 199-202. DOI: 10.1093/carcin/22.1.199
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|
---
title: Patient factors associated with receipt of psychological and pharmacological
treatments among individuals with common mental disorders in a Swedish primary care
setting
authors:
- Alexis E. Cullen
- Elin Lindsäter
- Syed Rahman
- Heidi Taipale
- Antti Tanskanen
- Ellenor Mittendorfer-Rutz
- Magnus Helgesson
journal: BJPsych Open
year: 2023
pmcid: PMC10044006
doi: 10.1192/bjo.2023.8
license: CC BY 4.0
---
# Patient factors associated with receipt of psychological and pharmacological treatments among individuals with common mental disorders in a Swedish primary care setting
## Body
Common mental disorders (CMDs), including depression, anxiety and stress-related disorders, affect around $30\%$ of the world's population at some point during life.1 These disorders are extremely disabling, with depression and anxiety both ranked within the top 15 most burdensome diseases for individuals aged 25–49 years.2 Psychological and pharmacological therapies, which have been shown to be effective in reducing target symptoms in individuals with depression, anxiety, obsessive–compulsive disorder and post-traumatic stress disorder (albeit with small-to-moderate effect sizes and substantial heterogeneity),3 are the recommended first-line treatments for CMDs in many countries.4–11However, community-based surveys have identified a substantial ‘treatment gap’ (defined as the proportion of individuals who require treatment but do not receive it) among individuals with CMDs. In a 2004 review of these studies, 50–$60\%$ of individuals meeting major depression, generalised anxiety disorder and obsessive–compulsive disorder criteria had not received medical or professional services.12 More recent population surveys conducted in the UK have reported similar treatment gaps (ranging from $26\%$ to $76\%$) among those with CMDs.13–16 Such surveys have shown that factors including age, gender, ethnic minority and migrant status, socioeconomic disadvantage (e.g. lower education levels, unemployment, low income and receipt of government pensions), type of living area and diagnosis are associated with the likelihood of accessing psychological and pharmacological therapies.15–21 However, findings have been inconsistent across studies, many of which did not restrict analyses to individuals meeting CMD criteria (instead controlling for symptom presence and/or severity). Moreover, as such studies include members of the general population, these treatment gaps capture healthcare access (i.e. the extent to which an individual is able and/or willing to access healthcare services in general)22 and utilisation of these treatments (i.e. receipt of specific treatments offered by healthcare providers).
## Abstract
### Background
Psychological and pharmacological therapies are the recommended first-line treatments for common mental disorders (CMDs) but may not be universally accessible or utilised.
### Aims
To determine the extent to which primary care patients with CMDs receive treatment and the impact of sociodemographic, work-related and clinical factors on treatment receipt.
### Method
National registers were used to identify all Stockholm County residents aged 19–64 years who had received at least one CMD diagnosis (depression, anxiety, stress-related) in primary care between 2014 and 2018. Individuals were followed from the date of their first observed CMD diagnosis until the end of 2019 to determine treatment receipt. Associations between patient factors and treatment group were examined using multinomial logistic regression.
### Results
Among 223 271 individuals with CMDs, $30.6\%$ received pharmacotherapy only, $16.5\%$ received psychological therapy only, $43.1\%$ received both and $9.8\%$ had no treatment. The odds of receiving any treatment were lower among males (odds ratio (OR) range = 0.76 to 0.92, $95\%$ CI[minimum, maximum] 0.74 to 0.95), individuals born outside of Sweden (OR range = 0.67 to 0.93, $95\%$ CI[minimum, maximum] 0.65 to 0.99) and those with stress-related disorders only (OR range = 0.21 to 0.51, $95\%$ CI[minimum, maximum] 0.20 to 0.53). Among the patient factors examined, CMD diagnostic group, prior treatment in secondary psychiatric care and age made the largest contributions to the model (R2 difference: $16.05\%$, $1.72\%$ and $1.61\%$, respectively).
### Conclusions
Although over $90\%$ of primary care patients with CMDs received pharmacological and/or psychological therapy, specific patient groups were less likely to receive treatment.
## Treatment utilisation in healthcare settings
Identifying factors associated with treatment utilisation is particularly important, as healthcare providers can play a crucial part in developing strategies to ensure equity of treatment provision.16 Given that the vast majority of individuals who present to healthcare services with CMDs are treated exclusively within primary care,23 it is important to determine the extent to which these patients receive recommended first-line treatments. Previous studies of primary care patients with CMDs have reported highly variable rates of treatment receipt (range: 36–$82\%$).23–28 This variation appears to be partially attributable to CMD diagnostic group: for example, two studies using the same UK primary care sample observed that pharmacological treatments (most commonly antidepressants) were received by $82\%$ and $63\%$ of those with depressive and anxiety disorders, respectively, within 3 months of their first diagnosis.24,25 Similarly, a Swedish register study observed that antidepressants were used by 47–$79\%$ of primary care patients with CMDs (depression, anxiety, obsessive–compulsive and adjustment disorders), whereas anxiolytics were used by 43–$63\%$,23 with the lowest rates of pharmacotherapy treatment (irrespective of type) found in adjustment disorder patients. Treatment receipt rates also vary by treatment modality, with several studies showing that individuals with CMDs are more likely to receive pharmacotherapy than psychological therapies.27–29 *Whether this* differs by CMD diagnostic group is unclear. Consistent with the general population surveys described above, several sociodemographic factors (including age, sex, education, marital status and ethnic minority status) have been reported to be associated with treatment receipt in primary care samples.26–30 However, findings have been inconsistent, potentially owing to differences in study methodology, treatment modality and patient factors examined. Indeed, the largest studies in this field have typically used a single healthcare register (without linkage to other registers providing important confounding variables) and have examined only one treatment modality (most commonly pharmacotherapy).
To address these knowledge gaps, the present study used administrative healthcare data from Region Stockholm's VAL database31 to examine receipt of first-line recommended treatments among individuals diagnosed with CMDs in primary care. A major strength of the VAL database, which contains pseudonymised individual-level data for visits to primary care clinics in Stockholm County, is that it captures delivery of both systematic (e.g. cognitive–behavioural therapy) and non-systematic (e.g. counselling) psychological therapies and multiple diagnoses.32 Moreover, these data can be linked to the extensive national registers available in Sweden,33 which provide detailed, high-quality information for a range of factors, including sociodemographic characteristics, medication dispensations, use of secondary healthcare services, employment and work disability. We aimed to determine the proportion of individuals with CMDs who received psychological and/or pharmacological treatments and investigate whether patient sociodemographic, work-related and clinical factors were associated with treatment receipt in this setting.
## Data sources
Data were obtained from the national registers collected by various Swedish health and social insurance agencies, linked by the (pseudonymised) unique personal identification number assigned to all Swedish residents at birth or migration. According to current Swedish regulations, the use of national data for research purposes does not require informed consent from individuals whose data are held in these registers.34 The Longitudinal Integrated Database for Health Insurance and Labour Market Studies (LISA),35 maintained by Statistics Sweden, was used to identify the source population and obtain sociodemographic variables and unemployment. LISA has collated annual data for the entire adult population (age ≥16 years) of Sweden since 1990; data for the years 2013–2018 were used to enable determination of sociodemographic characteristics in the year prior to cohort entry for the selected sample (who could enter the cohort between 2014 and 2018). Detailed region-of-birth data (reported in Supplementary Material only, available at https://doi.org/10.1192/bjo.2023.8) were obtained from the Longitudinal Database for Integration Studies (STATIV) register,36 developed by Statistics Sweden and the Swedish Integration Board, available from 1997 to 2006. The VAL database,37 administered by Region Stockholm, was used to determine diagnoses in primary care, defined according to the ICD-10,38 and receipt of psychological therapy. The VAL database includes data on primary care contacts (date, healthcare professional and action code) for all public clinics and most privately owned and/or operated clinics from 2003, with diagnoses included since 2014.39 The present study used data from 2014 to 2019 (the first available year with full coverage of diagnoses to the most recently available year). Medication dispensations (Anatomical Therapeutic Chemical (ATC) code and date when prescribed) between 2014 and 2019 were obtained from the Prescribed Drug Register (PDR, administered by the National Board of Health and Welfare),40 which includes all prescribed and dispensed medications (except for those administered in hospital) since 2005. Treatment in secondary psychiatric care (date and ICD-10 diagnosis) for the years 2009–2019 was obtained from the National Patient Register (NPR, National Board of Health and Welfare),41,42 which has captured all in-patient and specialist out-patient care since 1987 and 2001, respectively. We determined work disability (periods of sickness absence and disability pension) for the years 2013–2018 using the Micro-Data for Analyses of Social Insurance (MiDAS, Swedish Social Insurance Agency)43 and death during the study period (2014–2019) using the Cause of Death Register (National Board of Health and Welfare).44
## Study design and source population
This population-based cohort study included all individuals in LISA who were resident in Stockholm County for every year between 2014 and 2019; those who moved out of the area or died during this period were excluded. Around one-fifth of the population of Sweden resides in this region, which includes the capital city of Stockholm, several other cities and towns and large rural areas.45 Using VAL, we identified individuals who between 1 January 2014 and 31 December 2018 had at least one recorded diagnosis (up to eight can be assigned per visit, with no hierarchy) of depression (ICD-10: F32–F39), anxiety (ICD-10: F40–F42) or stress-related disorder (ICD-10: F43). Individuals entered the study on the date of their first CMD diagnosis within this period. We then excluded individuals who were aged <19 or >64 years during the year of cohort entry and those who had any diagnosis of severe mental illness (bipolar disorder, schizophrenia or other psychotic disorder; ICD-10: F30–F31 and F20–F29) or organic mental disorder (ICD-10: F00–F09) recorded in VAL or NPR at any point prior to cohort entry. Individuals were followed until the end of 2019 (minimum follow-up of 1 year) to determine the type of treatment received.
## Treatment definitions
Individuals were categorised according to the type of treatment received during the follow-up period: no treatment versus pharmacotherapy only versus psychological therapy only versus both. Pharmacological treatments considered here were dispensations of antidepressants (ATC code: N06A), anxiolytics (ATC code: N05B), or hypnotics and sedatives (ATC code: N05C and R06AD01), as recorded in the PDR. Psychological therapies (identified using VAL) were defined as any visit with a systematic psychological therapy action code (see Supplementary Material) or any other visit where the healthcare provider was a psychologist, psychotherapist or ‘curator’ (social workers with specialist training in administering psychological therapies). Given that these pharmacological and psychological therapies can be used to treat other mental and somatic disorders, we included only those treatments that occurred proximally to a CMD diagnosis in VAL (up to 1 month before and 1 year after any CMD diagnosis recorded in VAL). Owing to concerns regarding misclassification (e.g. that CMD diagnoses may have been present but not recorded by primary care clinicians at the time when treatment was received), we excluded individuals who only received these treatments outside this 13 month timeframe rather than assigning them to the no-treatment group.
## Predictor variables
Age in years (categorised as 20–25 versus 26–35 versus 36–45 versus 46–55 versus 56–65) was determined for the calendar year of cohort entry. The following sociodemographic predictors were measured on 31 December in the year prior to cohort entry: gender, region of birth (*Sweden versus* countries within the European Union between 2004 and 2006 (AKA EU-25 countries) versus the rest of the world), family situation (married or cohabiting without children living at home versus married or cohabiting with children living at home versus single without children living at home versus single with children living at home); type of living area (inner city versus rural area), level of education (low, 0–9 years versus medium, 10–12 years versus high, >12 years).
Work-related measures included the number of days of full-time unemployment, measured during the calendar year prior to cohort entry (none versus 1–180 days versus >180 days), and work disability (sickness absence and disability pension), which were obtained for the year (365 days) prior to cohort entry. Individuals in Sweden are entitled to income-related levels of unemployment benefit (from age 16 years) or basic levels (if aged ≥20 years and having no recent job income) when registered as a job seeker at the Swedish Public Employment Service. All individuals aged 16 years or older with an income above a certain level are eligible to receive sickness benefits; payments are covered by the employer for the first 14 days, and thus only periods exceeding 14 days are covered by the Social Insurance Agency and captured in the MiDAS database. Permanent disability pension in Sweden can be granted to individuals aged 30–64 years, whereas individuals aged 19–29 years can receive time-restricted disability pension if work capacity is reduced or compulsory education is not completed. Both sickness absence and disability pension payments can be granted at full-time or part-time level. In the present study, we calculated the net days (the length of the period × extent of the period) for both, such that 30 days of half-time sickness absence payment was converted to 15 net days. These net days were then used to derive categorical variables indexing sickness absence (none versus 1–90 days versus >90 days) and disability pension (none versus any).
For the clinical predictors, we derived a seven-level, mutually exclusive, categorical variable capturing CMD diagnoses assigned in primary care (anxiety only versus depression only versus stress-related only versus depression + anxiety versus depression + stress-related versus anxiety + stress-related versus depression + anxiety + stress-related). As our intention was to determine whether CMD diagnostic group was associated with type of treatment received, for individuals treated with pharmacotherapy and/or psychological therapy, we included only those diagnoses that were recorded between the date of cohort entry and the last observed treatment date; for individuals who received no treatment, all CMD diagnoses recorded during the study period were included. The following clinical factors were measured during the year prior to and year following cohort entry (cohort entry date ± 365 days) with binary variables (none versus any) created for each variable: (a) any other (non-CMD) mental disorder treated in primary care (visits in VAL with any ICD-10 F codes excluding F32–F39 and F40–43); (b) dispensations of any other psychotropic medications (ATC codes N05A, N03AF01, N03AG01, N03AX09, N05AN01, N06B, N07BB, N07BC and N06CA recorded in the PDR); (c) relevant somatic comorbidities treated in primary care for which psychological treatment is indicated as per Region Stockholm primary care guidelines,46 namely, endometriosis, irritable bowel syndrome, pain, tinnitus, electrohypersensitivity, obesity, fibromyalgia and post-viral fatigue syndrome/myalgic encephalomyelitis (visits in VAL with ICD-10 codes N80, K58, R52, H93.1, W90, E66, M79.7, G93.3); and (d) any treatment for suicide attempt (intentional self-harm and events of undetermined intent) in primary or secondary care (visits in VAL or NPR with any ICD-10 X60-X84 or Y10-Y34 codes). To capture prior mental disorders that reached sufficient severity to warrant treatment in secondary care, we created a binary variable (none versus any) for any mental disorder, including CMDs, recorded in the NPR in the 4 years prior to and 1 year following cohort entry.
As all individuals were required to be resident in Stockholm Country for each year between 2014 and 2018, calendar year at cohort entry corresponded to time observed. Based on the rationale that individuals with longer observation periods would have more opportunities to receive treatment, this variable was included as a covariate.
## Statistical methods
Analyses were conducted using R version 4.0.4. Descriptive statistics were derived for all predictor variables. A multinomial logistic regression model was conducted using the R ‘nnet’ package with the ‘multinom’ function to examine associations between sociodemographic, work-related and clinical factors (predictors) and type of treatment received (outcome), where the reference category for the outcome variable was the no-treatment group. All predictor variables were entered into the model simultaneously, such that each was mutually adjusted for every other variable in the model. Likelihood-ratio tests were performed for each predictor to test whether inclusion significantly improved model fit. We also derived the Bayesian information criterion (BIC) value (a measure of model fit that introduces a penalty term for the number of parameters in the model) and Nagelkerke pseudo R2 (an approximation of the total variance explained) for the overall model. For each predictor in turn, we calculated the difference between the BIC and R2 values derived from the full model and the model without the tested variable included. For predictor variables that showed the greatest contribution to the model (Nagelkerke R2 value >$1\%$), effects plots were produced using the R ‘effects’ package to show the predicted probability of outcome group membership for each predictor variable, after adjusting for all other variables.
## Ethics statement
All procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by the Ethical Review Board in Stockholm (DNR: $\frac{2007}{762}$-31).
## Study population
The procedure used to derive the study population is detailed in Fig. 1. Of the 1 525 893 individuals registered as alive and resident in Stockholm County for every calendar year between 2014 and 2018, 276 830 ($18.1\%$) had at least one primary care contact where a diagnosis of CMD was recorded. After excluding individuals based on age, death during 2019, treatment for severe mental illness or organic mental disorder, receipt of pharmacotherapy or psychological therapy that did not occur proximally to a CMD diagnosis and missing data for one or more predictor variables, we included 223 271 individuals who were followed for a median of 4.13 years (range: 1.08–6.00 years). Fig. 1Procedure for deriving study population.
## Description of treatments
Within the study population, 68 243 ($30.6\%$) individuals received pharmacotherapy only, 36 900 ($16.5\%$) received psychological therapy only, 96 313 ($43.1\%$) had both and 21 815 ($9.8\%$) received no treatment. Of those treated with pharmacotherapy alone or in combination with psychological therapy ($$n = 164$$ 556, $73.7\%$ of the study population), the majority ($76.0\%$) received antidepressant medication, just over one-half ($53.6\%$) received anxiolytics and $51.0\%$ received hypnotics or sedatives (Supplementary Table 2). Among the 133 213 CMD patients ($59.7\%$ of the study population) who received psychological therapy alone or in combination with pharmacotherapy, around half ($50.8\%$) had at least one systematic psychological therapy session (predominately CBT), with the rest receiving other therapies administered by a curator, a psychologist or (less commonly) a psychotherapist (Supplementary Table 3).
## Characteristics of the study population
The vast majority of the total study population (Table 1) were aged 26–55 years, with no peak in the age distribution, and two-thirds were female. Most individuals were born in Sweden ($75.2\%$), with Asia (excluding Afghanistan, Iraq, Iran and Syria), Europe (excluding EU15, the Nordic countries and the former Yugoslavia) and Iraq being the three most common regions of birth outside of Sweden (Supplementary Table 4). In terms of work-related factors, fewer than one in ten had been unemployed (for any number of days) in the calendar year prior to cohort entry; just over one in five had received 1–90 days of sickness absence payments in the year (365 days) prior to cohort entry, with far fewer ($6.6\%$) receiving >90 days of payments. Less than $5\%$ had been granted disability pension. Most individuals presented with a single CMD diagnosis (most commonly anxiety); just under a third had diagnoses within two CMD disorder groups, and $7.3\%$ received diagnoses in all three CMD diagnostic groups (see Supplementary Table 5 for specific diagnoses). Only $6\%$ of the sample had received a diagnosis of any other (non-CMD) mental disorder in primary care in the year during or prior to cohort entry, where behavioural syndromes associated with physiological disturbances and physical factors (ICD-10 codes F50–59) were most common (Supplementary Table 6); suicide attempts (treated in primary or secondary care) during this period were also rare ($1.5\%$). However, just under a third ($31.7\%$) had been previously treated for any mental disorder in secondary care, most commonly for CMDs (Supplementary Table 7). Table 1Characteristics of 223 271 individuals diagnosed with common mental disorders in primary care in Stockholm County by type of treatment receivedNo treatment ($$n = 21$$ 815, $9.8\%$)Pharmacotherapy only ($$n = 68$$ 243, $30.6\%$)Psychological therapy only ($$n = 36$$ 900, $16.5\%$)Both ($$n = 96$$ 313, $43.1\%$)Total sample ($$n = 223$$ 271)N (%)N (%)N (%)N (%)N (%)Sociodemographic factorsAge (years)a20–252396 (11.0)6011 (8.8)5010 (13.6)10 190 (10.6)23 607 (10.6)26–355487 (25.2)13 421 (19.7)11 818 (32.0)25 302 (26.3)56 028 (25.1)36–455512 (25.3)17 199 (25.2)9656 (26.2)26 043 (27.0)58 410 (26.2)46–555031 (23.1)17 769 (26.0)6735 (18.3)22 152 (23.0)51 687 (23.1)56–653389 (15.5)13 843 (20.3)3681 (10.0)12 626 (13.1)33 539 (15.0)Gender, male7775 (35.6)25 397 (37.2)11 215 (30.4)28 486 (29.6)72 873 (32.6)Region of birthSweden15 599 (71.5)50 769 (74.4)28 614 (77.5)72 885 (75.7)167 867 (75.2)EU25 (except Sweden)1378 (6.3)4674 (6.8)2005 (5.4)5573 (5.8)13 630 (6.1)Rest of the world4838 (22.2)12 800 (18.8)6281 (17.0)17 855 (18.5)41 774 (18.7)Family situationbMarried or cohabiting without children3176 (14.6)10 529 (15.4)3731 (10.1)11 341 (11.8)28 777 (12.9)Married or cohabiting with children7288 (33.4)18 746 (27.5)12 365 (33.5)29 864 (31.0)68 263 (30.6)Single without children9876 (45.3)33 726 (49.4)17 894 (48.5)45 980 (47.7)107 476 (48.1)Single with children1475 (6.8)5242 (7.7)2910 (7.9)9128 (9.5)18 755 (8.4)Type of living area, ruralb549 (2.5)1712 (2.5)726 (2.0)2562 (2.7)5549 (2.5)Level of educationbLow (0–9 years)2628 (12.0)10 406 (15.2)3199 (8.7)11 243 (11.7)27 476 (12.3)Medium (10–12 years)9207 (42.2)28 069 (41.1)14 669 (39.8)40 961 (42.5)92 906 (41.6)High (>12 years)9980 (45.7)29 768 (43.6)19 032 (51.6)44 109 (45.8)102 889 (46.1)Work-related factorsUnemployment (days)cNone19 832 (90.9)60 822 (89.1)33 394 (90.5)85 324 (88.6)199 372 (89.3)1–180 days1481 (6.8)5554 (8.1)2724 (7.4)8316 (8.6)18 075 (8.1)>180 days502 (2.3)1867 (2.7)782 (2.1)2673 (2.8)5824 (2.6)Sickness absence (days)d015 413 (70.7)49 773 (72.9)28 142 (76.3)66 751 (69.3)160 079 (71.7)1–905667 (26.0)12 996 (19.0)7370 (20.0)22 488 (23.3)48 521 (21.7)>90735 (3.4)5474 (8.0)1388 (3.8)7074 (7.3)14 671 (6.6)Received disability pensiond505 (2.3)5980 (8.8)632 (1.7)3926 (4.1)11 043 (4.9)Clinical factorsCMD diagnosiseAnxiety4784 (21.9)21 571 (31.6)10 378 (28.1)18 942 (19.7)55 675 (24.9)Depression2203 (10.1)16 861 (24.7)4685 (12.7)12 076 (12.5)35 825 (16.0)Stress-related13 039 (59.8)12 497 (18.3)13 506 (36.6)11 720 (12.2)50 762 (22.7)Depression and anxiety242 (1.1)6802 (10.0)1528 (4.1)14 593 (15.2)23 165 (10.4)Depression and stress-related575 (2.6)4042 (5.9)2026 (5.5)11 210 (11.6)17 853 (8.0)Anxiety and stress-related887 (4.1)4380 (6.4)3834 (10.4)14 528 (15.1)23 629 (10.6)Depression, anxiety and stress-related85 (0.4)2090 (3.1)943 (2.6)13 244 (13.8)16 362 (7.3)Comorbid mental disorder (any)f501 (2.3)5342 (7.8)1358 (3.7)6298 (6.5)13 499 (6.0)Comorbid somatic disorder (relevant only)f2161 (9.9)9971 (14.6)4330 (11.7)15 202 (15.8)31 664 (14.2)Suicide attemptf194 (0.9)1287 (1.9)387 (1.0)1545 (1.6)3413 (1.5)Other psychotropic dispensationsf371 (1.7)8336 (12.2)723 (2.0)6878 (7.1)16 308 (7.3)Treatment in secondary psychiatric careg2890 (13.2)27 409 (40.2)5929 (16.1)34 551 (35.9)70 779 (31.7)Calendar year at cohort entry20144423 (20.3)22 508 (33.0)7067 (19.2)32 754 (34.0)66 752 (29.9)20154356 (20.0)14 399 (21.1)6645 (18.0)21 022 (21.8)46 422 (20.8)20164399 (20.2)12 010 (17.6)6761 (18.3)16 765 (17.4)39 935 (17.9)20174414 (20.2)10 523 (15.4)7251 (19.7)13 957 (14.5)36 145 (16.2)20184223 (19.4)8803 (12.9)9176 (24.9)11 815 (12.3)34 017 (15.2)CMD, common mental disorder.a. Measured during year of cohort entry.b. Measured on 31 December in the year prior to cohort entry.c. Measured during the calendar year prior to cohort entry.d. Measured in the year (365 days) prior to cohort entry.e. Measured during the entire study period (2014–2019) or up to last treatment received.f. Measured during the year prior to, and year following, cohort entry (cohort entry date ± 365 days).g. Measured in the four years prior to, and one year following, cohort entry (cohort entry date –1460 days to + 365 days).
## Patterns of association between patient characteristics and treatment received
Multinomial regression analyses were performed to identify patient factors associated with treatment receipt, where each treatment outcome (pharmacotherapy only, psychological therapy only or both) was compared with receiving no treatment (Table 2). Several factors consistently distinguished individuals who received no treatment from those who received any form of treatment; that is, odds ratios were in the same direction, and statistically significant, for all three pairwise comparisons (pharmacotherapy only versus no treatment; psychological therapy only versus no treatment; both versus no treatment). Specifically, when each treatment was compared with no treatment, males were less likely than females to receive pharmacotherapy (odds ratio (OR) = 0.92, $95\%$ CI 0.89–0.95), psychological therapy (OR = 0.76, $95\%$ CI 0.74–0.79) or both (OR = 0.77, $95\%$ CI 0.74–0.80). Similarly, compared with Swedish-born individuals, the odds of receiving pharmacotherapy and/or psychological therapy were lower among those born in Europe (OR range = 0.86 to 0.93, $95\%$ CI[minimum, maximum] 0.80 to 0.99) and even further reduced among those born outside Europe (OR range = 0.67 to 0.70, $95\%$ CI[minimum, maximum] 0.65 to 0.73). By contrast, individuals who were single with children living at home (compared with married or cohabiting individuals without children living at home) were more likely to receive treatment (OR range = 1.11 to 1.30, $95\%$ CI[minimum, maximum] 1.03 to 1.41), as were those with >90 days sickness absence (OR range = 1.11 to 1.29, $95\%$ CI[minimum, maximum] 1.01 to 1.41). With regards to clinical factors, individuals diagnosed with depression + anxiety disorders, depression + stress-related disorders or all three disorders were more likely than those diagnosed with anxiety disorders only to receive any form of treatment (OR range = 1.25 to 29.93, $95\%$ CI[minimum, maximum] 1.14 to 37.16), whereas patients with stress-related disorders only were markedly less likely to receive treatment (OR range = 0.21 to 0.51, $95\%$ CI[minimum, maximum] 0.20 to 0.53). Treatment for comorbid mental disorders and somatic disorders in primary care also increased the likelihood of receiving any treatment (OR range = 1.27 to 1.73, $95\%$ CI[minimum, maximum] 1.20 to 1.90). Table 2Multinomial regression models, yielding odds ratios (OR) and $95\%$ confidence intervals, examining effects of demographic, work-related and clinical factors on type of treatment received (relative to no treatment) among 223 271 individuals diagnosed with common mental disorders in primary care in Stockholm CountyPharmacotherapy only versus no treatment (Ref)Psychological only versus no treatment (Ref)*Both versus* no treatment (Ref)OR$95\%$ CIOR$95\%$ CIOR($95\%$ CI)Sociodemographic factorsAge (years)a20–25Ref–Ref–Ref–26–351.371.28–1.461.111.04–1.181.29(1.21–1.37)36–452.332.18–2.500.990.93–1.061.63(1.52–1.74)46–552.762.58–2.950.820.77–0.881.69(1.58–1.80)56–652.882.68–3.090.710.65–0.761.51(1.41–1.62)GenderFemaleRef–Ref–Ref–Male0.920.89–0.950.760.74–0.790.77(0.74–0.80)Region of birthbSwedenRef–Ref–Ref–EU25 (except Sweden)0.930.87–0.990.860.80–0.920.860.80–0.92The rest of the world0.670.65–0.700.700.67–0.730.670.65–0.70Family situationbMarried or cohabiting with no children at homeRef–Ref–Ref–Married or cohabiting with children at home0.940.89–1.001.121.05–1.201.071.01–1.14Single no children at home0.990.94–1.041.091.03–1.161.040.99–1.10Single with children at home1.111.03–1.201.301.19–1.411.281.19–1.39Type of living areabBig citiesRef–Ref–Ref–Rural areas0.970.87–1.080.810.72–0.911.020.92–1.13Level of educationbLow (0–9 years)Ref–Ref–Ref–Medium (10–12 years)0.940.89–0.991.271.20–1.351.171.10–1.23High (>12 years)1.030.97–1.081.531.44–1.631.251.18–1.32Work-related factorsUnemployment, dayscNoneRef–Ref–Ref–1–1800.910.85–0.971.081.01–1.150.960.90–1.03>1800.810.73–0.900.990.88–1.110.880.79–0.98Sickness absence, daysd0Ref–Ref–Ref–1–900.880.85–0.920.810.78–0.850.990.95–1.03>901.291.19–1.411.111.01–1.221.241.14–1.35Disability pension granteddNoneRef–Ref–Ref–Yes1.701.54–1.880.850.75–0.961.121.01–1.24Clinical factorsCMD diagnosiseAnxietyRef–Ref–Ref–Depression1.511.43–1.591.061.00–1.131.311.24–1.39Stress-related0.210.20–0.210.510.48–0.530.220.21–0.23Depression and anxiety5.094.46–5.813.052.65–3.5113.0911.48–14.93Depression and stress-related1.251.14–1.381.781.61–1.974.083.72–4.47Anxiety and stress-related0.980.90–1.062.041.88–2.213.613.35–3.90Depression, anxiety and stress-related4.073.26–5.075.454.35–6.8229.9324.11–37.16Comorbid mental disorder (any)fNoRef–Ref–Ref–Yes1.701.54–1.881.461.31–1.621.731.56–1.90Comorbid somatic disorders (relevant only)fNoRef–Ref–Ref–Yes1.271.20–1.341.271.20–1.351.461.38–1.54Suicide attemptfNoRef–Ref–Ref–Yes1.261.07–1.481.140.96–1.361.170.99–1.37Other psychotropic dispensationsfNoRef–Ref–Ref–Yes3.142.80–3.510.980.86–1.121.991.78–2.23Treatment in secondary psychiatric caregNoRef–Ref–Ref–Yes2.902.77–3.041.051.00–1.102.322.22–2.43Calendar year at cohort entry2014Ref–Ref–Ref–20150.770.74–0.811.020.96–1.070.810.77–0.8520160.670.64–0.701.071.01–1.130.710.67–0.7420170.600.57–0.631.171.10–1.230.650.62–0.6820180.530.51–0.561.571.48–1.650.610.58–0.65Multinomial logistic regression models predicting treatment group versus reference (Ref) group; bold indicates statistical significance at $P \leq 0.05$ level; all variables mutually adjusted for all other variables. CMD, common mental disorder.a. Measured during year of cohort entry.b. Measured on 31 December in the year prior to cohort entry.c. Measured during the calendar year prior to cohort entry.d. Measured in the year (365 days) prior to cohort entry.e. Measured during the entire study period (2014–2019) or up to last treatment received.f. Measured during the year prior to, and year following, cohort entry (cohort entry date ± 365 days).g. Measured in the four years prior to, and one year following, cohort entry (cohort entry date –1460 days to + 365 days).
## Relative contribution of patient characteristics to treatment group
Model diagnostics were used to determine the importance of each factor (Table 3). CMD diagnostic category explained the largest proportion of the variance in the model (R2 = $16.05\%$), with prior mental disorders treated in secondary care, age and calendar year at cohort entry each explaining 1–$2\%$. Use of other psychotropic medications, receipt of disability pension, level of education, gender and region of birth made smaller contributions to the model (R2 range: 0.14–$0.50\%$), whereas sickness absence, somatic comorbidities, comorbid mental disorders treated in primary care, family situation, unemployment, type of living area and suicide attempts each contributed less than $0.1\%$. Table 3Model diagnostics for multinomial logistic regression examining factors related to treatment receipt in 223 271 individuals diagnosed with a common mental disorder in primary care in Stockholm CountyLog-likelihood testaDifference (full − reduced)bTest statisticd.f. P-valueBICR2 (%)Age4511.8312<0.001−4364.041.61Gender418.423<0.001−381.470.15Region of birth395.686<0.001−321.780.14Family situation116.519<0.001−5.670.04Type of living area28.953<0.0018.000.01Level of education440.566<0.001−366.670.16Unemployment65.096<0.0018.800.02Sickness absence247.216<0.001−173.310.09Disability pension493.063<0.001−456.120.17CMD diagnosis41355.4318<0.001−41133.7416.05Comorbid mental disorder146.103<0.001−109.160.05Comorbid somatic disorder237.213<0.001−200.260.08Suicide attempts9.4630.02427.480.00Other psychotropic dispensations1404.703<0.001−1367.750.50Treatment in secondary psychiatric care4828.213<0.001−4791.271.72Calendar year at cohort entry3384.2412<0.001−3236.451.21CMD, common mental disorder; BIC, Bayesian information criterion; R2, Nagelkerke pseudo R2.a. Derived from the multinomial logistic regression. All variables mutually adjusted for all other variables.b. Difference between full model (all variables included) and reduced model (without tested variable included). Overall values for full model: BIC: 487231.54; overall R2: $30.06\%$.
Predicted probability plots for predictors with R2 values exceeding $1\%$ are shown in Fig. 2 to illustrate the associations between these factors and treatment receipt. Except for the CMD diagnostic category (panel A), these predictors were associated with the relative likelihood of receiving pharmacotherapy only versus psychological therapy only, rather than the likelihood of receiving none versus any treatment. Specifically, the relative probability of receiving psychological therapy only was lower among those previously treated in secondary care (panel B) and decreased with age (panel C) but was higher among those who entered the cohort in later years (panel D). For the CMD diagnostic category, groups differed in their likelihood of receiving no treatment (highest among those with stress-related disorders only) or both treatments (highest among those with all three diagnoses) as well as their relative probability of receiving pharmacotherapy only (highest among those with depression only) or psychological therapy only (highest among those with stress-related disorders only). Fig. 2Predicted probability plots showing the likelihood of treatment group membership for predictor variables with Nagelkerke R2 values >$1\%$.
## Discussion
More than $90\%$ of a large, contemporaneous cohort of individuals diagnosed with CMDs in Stockholm primary care clinics received some form of treatment proximal to their diagnosis, most commonly a combination of pharmacotherapy and psychological therapy. Several factors, including gender, region of birth, long-term sickness absence, living status, CMD diagnostic group and mental and somatic comorbidities diagnosed in primary care, were found to consistently distinguish those who received no treatment from those who received either or both. CMD diagnostic group made the largest contribution to the model; the next highest-ranking variables (prior treatment in secondary psychiatric care, age and calendar year at cohort entry) tended to be associated with relative probabilities of pharmacotherapy or psychological therapy only.
Previous studies have reported highly variable rates of treatment receipt among individuals with CMDs in primary care, ranging from 36 to $82\%$.23–28 The high treatment rate that we observed in this sample probably reflects the fact that we were able to capture psychological therapies as well as medication dispensations. Indeed, the proportion of patients in the current study who received pharmacotherapy ($73.7\%$), either alone or in combination with psychological treatments, is largely consistent with those reported by other European register-based studies.23–25 Moreover, we employed a broad time-frame for measuring treatment receipt, including treatments received up to 1 month before and 12 months after any diagnosis of CMD. Our finding that individuals with CMDs were more likely to receive pharmacotherapy than psychological therapy is consistent with community-based surveys13 but is at odds with patient preference studies, which show that $75\%$ of individuals with CMDs would prefer to receive psychological treatment.47 Notably, we found that the relative probability of receiving psychological therapy in isolation increased in parallel with calendar year at cohort entry; this trend could reflect increased availability of psychological therapies over time or indicate that patients showed a greater preference for psychological therapy in later years. Importantly, it was most common for patients in our sample to receive both treatment modalities: given that combined treatment (psychotherapy and pharmacotherapy) is estimated to be around twice as effective as pharmacotherapy alone in treating major depression, panic disorder and obsessive–compulsive disorder,48 these findings are particularly reassuring and suggest that a substantial proportion of patients treated in primary care receive the most effective treatment package.
The strongest predictors of type of treatment received were clinical factors, most notably CMD diagnostic category and treatment in secondary psychiatric care. Individuals with disorders in all three diagnostic categories had the highest probability of receiving both treatment types, probably reflecting the fact that this group had the most complex clinical presentations. One novel finding was that individuals with stress-related disorders had the lowest probability of receiving any treatment. Indeed, around $60\%$ of patients who did not receive any treatment presented with a stress-related disorder only. This may be explained by the fact that the most common diagnosis within this category was acute stress disorder, which is considered in the most recent ICD revision (ICD-11)49 to be a normal response to an extreme stressor rather than a mental disorder.50 Given these classification changes, we might not expect individuals presenting with acute stress reactions in the absence of comorbid depression to be offered psychological or pharmacological therapy. Our finding that individuals who were treated in secondary psychiatric care in the 4 years prior to and 1 year after cohort entry were more likely than those who were not to receive pharmacotherapy alone and were particularly unlikely to receive psychological therapy alone was perhaps unsurprising. Patients who have experienced psychiatric disorders that are sufficiently severe as to warrant treatment in secondary care might be managed differently by primary care clinicians, who may, for example, be less willing to risk placing the patient on a waiting list for psychological therapy and/or might be more inclined to prescribe medication that was commenced in secondary care. Consistent with the findings for clinical factors, we observed that that individuals with sickness absence >90 days were more likely to receive treatment, suggesting that functional impairment leading to long-term work disability is associated with higher treatment intensity.
Among the sociodemographic factors examined, patient age explained the largest proportion of variance in treatment received. Although we found no evidence to suggest that individuals in a particular age group were less likely to receive any treatment, the type of treatment received differed across age groups. Specifically, the relative probability of receiving pharmacotherapy alone increased with age, coinciding with a decrease in the probability of receiving psychotherapy only. Partially consistent with these findings, a recent US study of primary care patients with depression observed that the odds of receiving any treatment decreased with age and that among those who had initiated treatment, the likelihood of receiving psychological therapy (rather than pharmacotherapy) was also reduced in older age groups.27 Similarly, a UK survey found that older adults (aged 75+ years) in the general population were less likely than younger adults to receive ‘talking therapy’,21 and a study examining access to psychological therapies among individuals with anxiety disorders in Canadian primary care clinics found that those aged >60 years were less likely than those aged 25–44 to receive treatment.26 These findings could reflect barriers to accessing and utilising psychological treatments among older adult populations, although patient preference studies indicate that younger samples are more likely to prefer psychological treatment than older samples.47 Consistent with a recent UK population survey,16 we observed that males and individuals born outside Sweden (particularly those born outside Europe) were less likely to receive any form of treatment. Primary care studies, however, have reported conflicting findings with respect to gender, with some studies reporting higher27 and lower26 rates of treatment in males relative to females and other studies observing no differences.28,29 Although primary care studies conducted in the US have found that ethnic minority groups are less likely to receive treatment than their non-Hispanic White peers,27,29 studies examining migrant status are lacking. Register-based studies have shown that migrants are less likely to use mental health services and use psychotropic medications, irrespective of diagnosis, despite being more likely to develop psychiatric disorders.51 Cultural factors and (particularly for psychological therapy) language differences may contribute to under-utilisation of treatments among migrants with CMDs.
## Limitations
Owing to our use of the extensive national registers in Sweden, our study had the advantages of a large sample size and access to a wide range of sociodemographic, work-related and clinical factors. Moreover, the comprehensive coverage of Region Stockholm's VAL database meant that we were able to identify patients with CMDs treated in public primary care clinics and the majority of private clinics. However, some limitations should be noted. First, although we can be confident that the PDR captures all prescribed and dispensed medications, we cannot be certain that every session of psychological therapy is recorded in VAL. However, given that ~$100\%$ of all primary care clinics in Sweden have electronic data capture systems52 designed to facilitate reliable, high-quality data recording, it is highly likely that all individuals treated with psychological therapy had at least one session recorded. Moreover, any misclassification can be assumed to be non-differential. Second, as we did not examine treatment dose, we cannot say that individuals with CMDs received optimal or minimally adequate doses of pharmacotherapy or psychological therapy. However, we intended to investigate the factors associated with psychological and pharmacological therapy receipt, irrespective of whether patients persisted with these treatments. Finally, our findings may have limited generalisability to non-European countries without publicly funded healthcare.32 Indeed, given that primary care clinicians in Sweden are required to undertake mental health training as part of their continuing professional development,52 detection and adequate treatment of CMDs may be better than in countries where this is not required.
## Future perspectives
We observed that the vast majority of individuals in primary care received pharmacological and/or psychological therapy proximal to a CMD diagnosis. Although our findings indicate that individuals with the highest illness burden (i.e. those with multiple CMD diagnoses, long-term sickness absence and mental/somatic comorbidities) received the most intensive treatments, even after adjusting for these factors, males and individuals born outside Sweden were less likely to receive any form of therapy. These findings, which are partially consistent with those of studies conducted in other countries, highlight important treatment disparities that exist even in a well-resourced European country. Further research investigating the complex factors that contribute to the provision and uptake of psychological and pharmacological therapies may help to ensure more equitable treatment of CMDs in primary care settings. Of note, further studies examining treatment received during the COVID-19 pandemic might generate a different picture, given that primary care has developed a range of digital solutions for providing treatments that might enable better outreach.
## Data availability
The data used in this study cannot be made publicly available owing to privacy regulations. According to the General Data Protection Regulation, the Swedish law SFS 2018:218, the Swedish Data Protection Act, the Swedish Ethical Review Act and the Public Access to Information and Secrecy Act, these types of sensitive data can only be made available for specific purposes, including research, that meet the criteria for access to this type of sensitive and confidential data as determined by a legal review. Readers may contact K.A. (kristina.alexanderson@ki.se) regarding the data.
## Author contributions
A.E.C., E.M.-R. and M.H. conceived the idea for the study. All authors contributed to the study protocol. A.E.C. planned and oversaw the statistical analyses and wrote the first draft of the manuscript. All authors contributed to manuscript revisions and content.
## Funding
This study was supported by AFA Insurance Agency (AFA Försäkring, grant number 200061). We used data from the REWHARD consortium supported by the Swedish Research Council (VR; grant number 2017-00624).
## Declaration of interest
H.T. and A.T. have participated in research projects funded by grants from Janssen Cilag and Eli Lilly to their employing institution. H.T. reports personal fees from Janssen Cilag and Otsuka. S.R. received remuneration from AstraZeneca for consulting purposes during the preparation of the manuscript, which was unrelated to the current study or any pharmacological treatment considered in this study.
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|
---
title: Effects of Chia Seeds on Growth Performance, Carcass Traits and Fatty Acid
Profile of Lamb Meat
authors:
- Selene Uribe-Martínez
- Juan Antonio Rendón-Huerta
- Verónica Guadalupe Hernández-Briones
- Alicia Grajales-Lagunes
- Juan Ángel Morales-Rueda
- Gregorio Álvarez-Fuentes
- Juan Carlos García-López
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10044021
doi: 10.3390/ani13061005
license: CC BY 4.0
---
# Effects of Chia Seeds on Growth Performance, Carcass Traits and Fatty Acid Profile of Lamb Meat
## Abstract
### Simple Summary
Growth is an important factor in animal production. Polyunsaturated fatty acids in human foods have been shown to have health benefits. Dietary manipulation strategies such as the inclusion of oilseeds in diets have been used to enhance the FA profile of sheep meat. Chia seeds are rich in polyunsaturated fatty acids and fiber. This study investigated the effect of increasing the amount of dietary chia seeds on lamb growth performance and changes in the fatty acid profile of the meat. The inclusion of chia seeds in lambs’ diets to feed lambs increased the bodyweight of neither the meat carcasses nor the non-meat components. However, it tended to increase the oleic acid and decrease stearic acid in the meat (Longissimus thoracis).
### Abstract
The aim of this work was to supplement a diet with chia seeds (*Salvia hispanica* L.) based on the requirements of finishing lambs for meat, and to analyze biometric parameters and fatty acid profiles in meat. Eighteen male Rambouillet lambs with a bodyweight of 25 kg were used. Animals were kept in individual pens with water and feed provided ad libitum. Three finishing diets were designed with the inclusion of 0, 50 and 100 g dry matter chia seeds and divided among the animals ($$n = 6$$). The experimental period lasted 60 days. The weights of the individual lambs were recorded every 14 days. At the end of the experiment, the animals were slaughtered and the weights of the hot carcasses and non-meat components were registered. In addition, an analysis of the fatty acid composition was carried out in the muscles (Longissimus thoracis). The total weight gain and average daily gain displayed significant differences ($p \leq 0.05$). Initial and final bodyweights, such as the dry matter intake, did not display differences. The fatty acid profile of the meat tended to decrease the SFA (stearic acid) and increase MUFA (oleic acid) ($p \leq 0.0001$) when chia seeds were added to the lamb diets. In conclusion, chia seed supplementation did not increase meat production or other biometric parameters; however, it modified the fatty acid profile in L. thoracis.
## 1. Introduction
In Mexico, the demand for sheep is high, due mainly to the use of its meat in the preparation of barbacoa [1]. Mostly creole sheep are produced, although various pure breeds such as Suffolk, Hampshire, Rambouillet and Corriedale are also produced [2]. French et al. [ 3] pointed out that it is important to note that red meat contains high levels of saturated fatty acids (SFAs) and low levels of polyunsaturated fatty acids (PUFAs), and that this is also related to the increase in concentrate in diets. Saturated fatty acid synthesis is linked to the extensive biohydrogenation of dietary unsaturated FAs that takes place in the rumen by rumen microbiota; these SFAs are then absorbed and deposited as fat in meat [4].
Currently, red meat is an important dietary component in Western countries and is typically low in n-3 fatty acids and high in SFA and n-6 fatty acids [5]. Although dairy and meat contain high levels of SFAs, they also provide a source of monounsaturated fatty acids (MUFAs) and PUFAs that have beneficial properties for human health [6]. However, it has been evidenced that some SFAs and trans-MUFAs negatively affect blood lipid profiles and are associated with an increased risk of coronary events [7]. To avoid this, the modulation of fatty acid profiles in meat in order to reduce the SFA content can be approached via high stearoyl-CoA desaturase (SCD) activity to increase the synthesis of n-3 PUFAs, which could help to prevent cardiovascular diseases in humans [8]. Consequently, dietary manipulation strategies such as the inclusion of oilseeds in diets have been used to enhance the FA profile of sheep meat for human consumption. Several studies have been conducted with vegetable oils such as linseed, canola, rapeseed and flaxseed rich in n-3 PUFAs to modify the fatty acid profiles of lamb meat [4,7,9,10]. Furthermore, chia seeds also provide a rich source of n-3 PUFAs [11].
Chia seeds (*Salvia hispanica* L.) are native to the central valleys of Mexico and Northern Guatemala; since the Mayan and Aztec ages, chia seeds have been considered an important staple crop used as a foodstuff. These seeds are rich in omega-3 fatty acids (α-linolenic acid), contain soluble and insoluble fibers and proteins, as well as containing minerals and antioxidants [12]. The seeds are rich in lipids ($35\%$) and omega-3 fatty acids, protein ($18\%$) and fiber ($23\%$) [13]. Peiretti and Gai [14] pointed out that chia seed oil is rich in n-3 PUFAs (64.1 g/100 g of total fatty acids) and also contains phenolic compounds, mainly quercetin and kaempferol, which act as strong antioxidants [15]. In previous studies, chia seeds have been supplemented in diets for livestock to increase the omega-3 content and to reduce the risk of cardiovascular diseases. The majority of work in this regard has been performed on monogastric animals such as poultry and rabbits; only a few works have focused on ruminants [16]. Some studies that have used chia seeds in diets for domesticated animals have focused on the modulation of the fatty acid profile. More specifically, chia seeds modify the fatty acid composition of the fat of finishing pigs, where lower proportions of palmitic, stearic and arachidic acids are found with chia seed treatments [5]. On the other hand, Tres et al. [ 17] mention that not only the fatty acid composition of ingredients but also their oxidative quality (antioxidants) affect the final nutritional and stability properties of meat, liver and plasma in rabbits and chickens. Studies of chia seed supplementation in the diets of ruminants are scarce.
On this basis, under the hypothesis that the inclusion of chia seeds (S. hispanica) in the diets of growing lambs modifies the growth performance and fatty acid profile of lamb meat, the objective of this trial was to evaluate the inclusion of chia seeds in the diets of lambs and to analyze its effect on growth performance and the fatty acid profile of lamb meat (Longissimus thoracis).
## 2. Materials and Methods
The experiment was carried out in March and April 2021 in a private production unit in Salinas de Hidalgo, San Luis Potosí, Mexico (22°3639″ N and 101°42′45″ W) at 2100 m above sea level; the climate is semiarid with an annual mean temperature of 18.7 °C and receives 391 mm of rainfall. All animal management and care procedures were carried out in accordance with the Mexican Official Standard NOM 062 [18] and in compliance with the regulations established by the Animal Protection Law enacted by the State of San Luis Potosi, Mexico, and they were approved on 28 October 2020 by the Academic Committee CA0CA04CAO-CAI-FMR-05.
## 2.1. Animals
This study used eighteen male Rambouillet lambs of similar live weight (25 ± 2.4 kg; 3 months of age) that were housed in individual cages (1.5 m × 1.0 m) in a ventilated barn. Three TMR diets were designed with 0, 50 and 100 g chia seeds/g DM and were randomly assigned to the animals. At the beginning of the trial, lambs had a 14-day TMR adaptation period with free access to water and feed, which was offered twice a day at 8:00 and 16:00 h. The experiment lasted for 60 days. The feed intake was measured daily, allowing for up to $10\%$ orts. Every 15 days, lambs were weighed before being fed in the morning. Weight gain was calculated by subtracting the initial weight from the final weight. The average daily gain (ADG) was calculated from the changes in bodyweight. The feed conversion ratio was calculated by dividing the dry matter intake (DMI) by ADG.
## 2.2. Feeds and Chemical Composition of Diets
Three finishing total mixed rations (TMRs) for lambs with moderate growing potential were formulated and supplemented with 0, 50 or 100 g of chia seeds per kg of feed (dry matter), with similar contents of crude protein according to the nutritional requirements recommended by the NRC [19] for small ruminants (Table 1) to meet the requirements for finishing lambs between four and seven months of age, weighing 40 kg, with a diet comprising $16\%$ crude protein. The forage to concentrate ratio was 50:50.
Feed samples (500 g) were collected weekly and dried (60 °C for 24 h) for subsequent chemical analysis; the crude protein, ash and acid detergent fiber contents were determined following the AOAC directions [20], and the neutral detergent fiber analysis was modified by adding α-amylase to feed samples [21].
## 2.3. Carcass and Non-Meat Component Evaluation
Once the 60 d feeding period ended, the feed and water were removed for 12 h. Lambs were desensitized by electrocution and slaughtered via exsanguination by slitting their throats in the municipal slaughterhouse of Salinas de Hidalgo, San Luis Potosí, México, under veterinary inspection according to the Mexican Official Standard NOM 033 [22].
Immediately, the weight of the hot carcass and non-meat components (skin, head, heart, liver, lungs, kidney, rumen, trachea, intestines, penis and testicles) were registered on a digital balance. Next, the hot carcass was refrigerated at 4 °C for 24 h and the weight of the cold carcass was recorded [23].
## 2.4. Acid Profiles in Meat
Samples of *Longissimus thoracis* muscle were taken from the 10th to 13th rib [4]. Muscle samples were packed into polyethylene bags and stored at −30 °C until analysis. The fatty acid (FA) profile of the fat extracted from the internal part of the muscle was analyzed by gas chromatography (GC) of the methyl-esters derived from the fatty acids of the samples. The fat methylation process was performed by mixing 10 mg of sample with 0.25 mL of a sodium methoxide solution $5\%$ (w/v) in methanol and heating it at 60 °C for 30 min [24]. The methyl-esters were analyzed and quantified by GC using a Stabilwax (Restek Corp., Bellefonte, PA, USA) capillary column (60 m × 0.25 mm × 0.25 µm; Varian 3400, Palo Alto, CA, USA). The FA peaks were identified through comparison with known reference methyl-esters (Supelco 37 Component FAME Mix, 47885-U, Sigma-Aldrich Co., Bellefonte, PA, USA).
## 2.5. Statistical Analysis
The data were analyzed through a completely randomized design. Data for the weight of carcasses and non-meat components and fatty acid profiles of meat fat were subjected to a one-way ANOVA with diet as a fixed effect (0, 50 and 100 g dry matter). Six lambs were used per treatment. The data growth performance was analyzed using the ‘mixed’ SAS procedure. The model included the labels lamb (random effect) and residual (lamb within treatment). The average daily weight gain, feed intake and feed conversion were analyzed using the same model. The covariance structure that resulted in the lowest Akaike’s information was heterogeneous autoregressive (ARH) [1]. Significant differences were accepted when $p \leq 0.05$ and were obtained using the statistical software SAS v.9.2 [25].
## 3. Results
Experimental diets had similar nutrient compositions (Table 1). Adding 50 and 100 g DM of chia seeds to diets reduced the steam-flaked corn content by $22\%$ and sorghum content by $50\%$.
## 3.1. Growth Performance
Lambs fed diets with 0, 50 and 100 g of chia seeds had similar growth performances (Table 2). The initial and final bodyweights did not display differences between treatments ($p \leq 0.05$). The total weight gain displayed statistical differences in contrast with $0\%$ chia seed inclusion ($p \leq 0.05$); however, the total weight gain was similar across treatments with chia seed supplementation. The ADG also displayed differences: it was similar in treatments containing 50 and 100 g of chia seeds. The inclusion of chia seeds in the diets did not affect feed intake, but rather, feed intake was similar across the three experimental diets. Although no statistical differences were found in performance measurements, the lambs fed 100 g of chia seeds had the best feed conversion ratio (FCR).
## 3.2. Non-Meat Components of Lambs Supplemented with Chia Seeds
Table 3 shows the weights of hot carcasses and non-meat components. The statistical analysis did not display differences between treatments in the weight of hot carcasses and some of the non-meat components. The major components of the animals’ weight were the carcass, rumen and skin: 18.6, 7.6 and 6.4 kg, respectively.
## 3.3. Fatty Acid Profile in Meat
Table 4 shows the fatty acid profiles (saturated, mono-unsaturated and poly-unsaturated acids (SFAs, MUFAs and PUFAs)) of samples of lamb (Longissimus dorsi) meat. Differences were observed in the highest representative proportions of the SFAs (palmitic and stearic acid); stearic acid had a tendency to decrease when the chia seed level increased in the diet ($p \leq 0.05$). On the other hand, statistical differences were detected within the MUFA group; the biosynthesis of oleic acid represents the highest proportion of MUFAs. Oleic acid had a tendency to increase when the diets were supplemented with chia seeds. *In* general, as the SFA groups represented the highest proportion of total fatty acids followed by MUFAs and PUFAs, and as the SFA and MUFA groups displayed differences, the SFA content tended to decrease and the MUFA content tended to increase when the diets were supplemented with chia seeds (50 and 100 g). PUFAs were not biosynthesized, as we expected.
## 4. Discussion
Our results are in accordance with those reported by Urrutia et al. [ 26], who fed lambs with diets supplemented with linseed or chia seeds, both of which are rich in α-linolenic acid (ALA, C18:3n-3); both diets had no effect on the growth parameters. the effects of the inclusion of chia (*Salvia hispanica* L.) seeds in diets on the growth performance of rabbits did not display significant differences among the groups for parameters such as live weight, live weight gain, feed consumption, feed conversion ratio, carcass yield or percentages of edible organs [27].
In our results, ADG differences were observed between diets; the energy intake could have been partially responsible for the ADG differences in favor of a higher chia seed content, given that chia seeds are a good source of energy and protein, and partially replaced sorghum. Although they did not fully comprise energy intake, the chia seeds’ protein contribution was higher when they were in the same proportion as sorghum and even more so when chia seeds were in a higher proportion. This greater amount of chia seed protein may have contributed to the growth of the rumen microbial population and the production of true protein. On the other hand, the tannins contained in sorghum could also inhibit the growth of rumen microorganisms.
Lambs fed with $10\%$ chia seeds or linseed showed a high average daily gain (ADG ≈ 300 g) [28]. These results coincide with those obtained in our trial. Chia seed inclusion did not affect the growth parameters negatively.
Marino et al. [ 29] reported that the final bodyweight and average daily gain (ADG ≈ 230 g) were not affected in Merino lambs fed a diet containing linseed, quinoa seed or a combination of both ($11\%$). The results of this study agreed with the literature. Regarding rapeseed and linseed oil supplementation in diets for lambs, the bodyweight and average daily gain (ADG ≈ 295 g) were not affected by the type of dietary lipid supplement [5]. Our ADG results for lambs were close to 300 g, which coincide with those obtained in the previously mentioned studies.
An important point for the use of chia seeds in animal nutrition is the absence of off-flavors associated with other popular omega-3-rich oilseeds such as linseed, false flax or fish oils that frequently reduce consumer acceptability [15].
The meat from lambs (Navarra breed) that were fed a diet supplemented with 100 g of chia seeds had increased levels of MUFAs and PUFAs, along with decreased SFA biosynthesis. This response might have been mediated by the regulation of several genes involved in lipogenesis; such regulation seems to be tissue-specific [26]. Jiménez et al. [ 11] mention that chia seeds have a high proportion of PUFAs ($74\%$) in contrast with other ordinary ingredients used in ruminants’ diets, such as corn, soybean, silages, etc. In our trial, the inclusion of 100 g of DM of chia seeds in the diet is the reason that the animals displayed the highest proportion of MUFAs.
A $10\%$ inclusion of oilseeds (chia or linseed) did not alter the growth rate of Andorra lambs or their carcass quality [28]. In another study, chia seeds greatly improved the fatty acid profile of lamb meat compared with a standard soybean-based control and increased meat α-linolenic acid (ALA) content, raising it from the 0.52 of the control group to 1.73 g/100 g total FA in the chia seed-fed lambs [30]. In our study, ALA was not increased, although the MUFA content increased with the increased chia seed supplementation.
In a trial where lambs were abomasally infused with linseed oil or echium oil through a polyethylene catheter over 4 weeks, the authors concluded that both the linseed oil and echium oil supplementations were similarly effective in enriching lamb meat with long-chain n-3 PUFAs [10].
A study conducted by Schettino et al. [ 31] in which the diets for dairy goats were supplemented with 2.7 or $5.5\%$ chia seeds, pointed out that the milk yield did not improve, but that the fatty acid profile of the milk was modified and the proportion of short- and medium-chain fatty acids decreased (C10:0, C12:0, C14:0 and C16:0). In the same study, mono- and polyunsaturated fatty acids such as C18:1n-9 cis and C18:2 cis-9 trans-11 increased with respect to the control diet.
The inclusion of chia seeds in the diets of rabbits was effective in improving the n-3 polyunsaturated fatty acid contents of meat and increasing the lipid oxidation in hind-leg meat [32]. The inclusion of chia seed in diets for rabbits increased the PUFA content and decreased the SFA biosynthesis in L. thoracis [27], which is similar to the results found in this study. The opposite takes place when diets are supplemented with animal fat, i.e., when supplemented with beef tallow: the higher proportion of SFAs (palmitic and stearic acids mainly) than UFAs (MUFAs and PUFAs) are found in lamb meat (L. dorsi) [33].
It should be noted that although chia seeds are high in omega-3 fatty acids, these were not increased in the $5\%$ and $10\%$ treatments. This low level of PUFA biosynthesis in meat could be due to the biohydrogenation process that takes place in the rumen due to gastric isomerase bacteria (Butyrivibrio fibrisolvens and Propionibacterium acnes), which changes the cis configuration of the unsaturated fatty acids, especially α-linolenic acid, to the trans position, resulting in stearic acid and various intermediates such as rumenic acid (RA, cis-9 trans-11CLA) and vaccenic acid (VA, trans-11C18:1) [34,35,36,37,38]. On the other hand, Manso et al. [ 38] pointed out that one of the most widely used strategies to increase the levels of VA and RA in meat and milk has been to increase their levels in the rumen using linoleic acid- and α-linolenic acid-rich fats, such as vegetable oils and fats.
Silva et al. [ 39] conducted an in vitro trial to investigate the effects of chia seeds (CS, $5.5\%$) compared with flaxseeds (FLAX, $5.0\%$) and calcium soaps with palm oil fatty acid (MEG, $3.8\%$) supplementation on the ruminal metabolism in an alfalfa hay-based diet. The authors reported that the dietary treatments had similar amounts of total fat and that the apparent ruminal digestibility, microbial efficiency and N metabolism were similar. However, higher rumen concentrations of C18:0 were observed in FLAX and CS diets, indicating that both the CS and FS fatty acids were highly biohydrogenated. At the same time, both oilseeds increased the concentrations of C18:3 n-3, C20:4 n-6 and total PUFAs.
Other oilseeds used to modify the fatty acid profile in animal production are linseed and sunflower seeds. For instance, when both were supplemented at $6\%$ in grass silage-based diets for dairy goats, they did not affect the nutrient intake, digestibility, milk yield or milk composition. Moreover, both can reduce the palmitic acid and increase the oleic acid [40]. Additionally, in a study where Navarra lambs were fed $14\%$ flaxseed in their diets, the final bodyweight, carcass characteristics and SFA content were not affected compared to a control diet. Nonetheless, despite this, the fact that the PUFA content, mainly C18:3 n-3 (ALA), was significantly increased in intramuscular fat (Longissimus dorsi) could be explained by the downregulated gene expression of ACACA, SCD, FADS1 and FADS2 [41].
Finally, perhaps increasing the chia seed supplementation in the diets used in this trial could significantly increase the omega-3 concentration in intramuscular fat.
## 5. Conclusions
Increases in the chia seed inclusion in the diets of growing lambs does not depreciate their growth performance (weight gain, carcass weight). On the contrary, the inclusion of 100 g/kg DM of chia seeds in lamb diets displayed the best results in the fatty acid (FA) profile. Slight changes in the *Longissimus thoracis* FA profile were detected here, which can benefit consumers’ health.
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|
---
title: Asian Best Practices for Care of Diabetes in Elderly (ABCDE)
authors:
- Sanjay Kalra
- Minakshi Dhar
- Faria Afsana
- Pankaj Aggarwal
- Than Than Aye
- Ganapathy Bantwal
- Manash Barua
- Saptarshi Bhattacharya
- Ashok Kumar Das
- Sambit Das
- Arundhati Dasgupta
- Guruprasad Dhakal
- Atul Dhingra
- Fatemeh Esfahanian
- Sharvil Gadve
- Jubbin Jacob
- Nitin Kapoor
- Ali Latheef
- Yovan Mahadeb
- Robin Maskey
- Wali Naseri
- Jeya Ratnasingam
- Abbas Raza
- Banshi Saboo
- Rakesh Sahay
- Mona Shah
- Shehla Shaikh
- SK Sharma
- Dina Shrestha
- Noel Somasundaram
- Mangesh Tiwaskar
- Apurva Jawdekar
journal: 'The Review of Diabetic Studies : RDS'
year: 2022
pmcid: PMC10044048
doi: 10.1900/RDS.2022.18.100
license: CC BY 4.0
---
# Asian Best Practices for Care of Diabetes in Elderly (ABCDE)
## Abstract
The elderly population with diabetes is diverse with the majority experiencing a decline in physical and mental capabilities, impacting the entire diabetes management process. Therefore, a need for geriatric-specific guidelines, especially for the Asian population, was identified and subsequently developed by an expert panel across government and private institutions from several Asian countries. The panel considered clinical evidence (landmark trials, position papers, expert opinions), recommendations from several important societies along with their decades of clinical experience and expertise, while meticulously devising thorough geriatric-specific tailored management strategies. The creation of the ABCDE best practices document underscores and explores the gaps and challenges and determines optimal methods for diabetes management of the elderly population in the Asian region.
## Introduction
The elderly population with diabetes is diverse. Some have physical and mental capacities like many younger ones, whereas others experience considerable declines in these capabilities. This impacts the entire diabetes management process. Therefore, it is imperative to address the differences in managing diabetes in the elderly population in a patient-centric manner. To obtain multiple perspectives in this regard, the Asian hybrid steering committee meeting on geriatric diabetes was conducted in Rishikesh, India. The expert panel involved key opinion leaders and representatives of the government as well as private institutions from Afghanistan, Bangladesh, Bhutan, India, Iran, Malaysia, Maldives, Mauritius, Myanmar, Nepal, Pakistan, and Sri Lanka.
The participants deliberated on a range of aspects of diabetes, clinical evidence (landmark trials, position papers, expert opinions), and recommendations of the American Diabetes Association (ADA), American Association of Clinical Endocrinology (AACE), European Association for the Study of Diabetes (EASD), International Diabetes Federation (IDF), Diabetes Canada, European Society of Endocrinology (ESE), the Gerontological Society of America (GSA), and The Obesity Society (TOS) in the management of the elderly population. They exchanged their peculiar understandings, experiences, and insights regarding the management of elderly persons with diabetes to develop tailored care for elderly diabetics.
The integration of technological aids, and environmental modulation to facilitate the management of diabetes in the presence of problems unique to the elderly was elaborately discussed and best practices were agreed upon. The consensus was drafted on these principles and circulated among expert panel members for their critical appraisal; it was subsequently amended with appropriate changes. Thus, the aim of the ABCDE to underscore and explore the gaps and challenges and determine optimal methods in diabetes management of the elderly population in the Asian region was achieved.
## 1.1.1 Definition of ‘Geriatric
The term “geriatric” has had different definitions over the past decades. A few decades ago, those ≥ 85 years of age were classified as oldest-old. Later, older adults were classified into three groups young-old (65-74 years of age), middle-old (75-85 years of age), and the oldest-old (> 85 years of age). Whereas World Health Organization (WHO) defines older persons as ≥ 60 years of age, most countries usually consider 60-65 years of age as geriatric [1,2]. However, for clinical purposes, rather than classifying older adults based on chronological age, a classification based on functional capabilities is more pragmatic. The functional aspects are influenced by genetics, environmental features, and co-morbidities such as metabolic disorders, hypertension, arthritis, obesity, renal function, and most importantly, cognitive abilities, which indeed reflect the physiological and vascular age. Thus, based on the International Diabetes Federation (IDF) classification the ABCDE experts have appropriately categorized older adults into four groups/categories based on functional capacities, level of medical comorbidities, and degree of frailty, which should guide diabetes management (Table 1) [3].
**Table 1.**
| Category | Functional group | Disease conditions | Degree of frailty |
| --- | --- | --- | --- |
| I | Functionally independent and self-reliant | Only Diabetes or non-life-threatening disease conditions | Not frail |
| II | Frail patients without cognitive impairment | Suffer from fatigue, weight loss, and extremely restricted | Frail |
| | Partially dependent on others | mobility and/or strength; highly susceptible to falls and institutionalization | |
| III | Frail patients with cognitive impairment/ | Increased risk of poor control leading to glycemic extremes | Frail |
| | dementia, completely dependent on caregivers | (hypoglycemia and hyperglycemia) | |
| IV | Terminally ill patients | Significant co-morbidities or malignancy, short lifespan | Extremely Frail |
## 1.2 Prevalence of Type 2 Diabetes Mellitus in Asia
Globally, there were 704 million older adults (65-99 years) in 2019, a number that is projected to increase to 995 million in 2030 and 1.4 billion by 2045. The estimated prevalence of diabetes in this population seems constant ranging from $19.3\%$ in 2019 to $19.6\%$ in 2045. Nevertheless, the estimated number of elderly with diabetes will show a considerable spike by 2045 (276.2 million) compared to 2019 (135.6 million), which is more than twice that in 2019, if the trend continues. Thus, diabetes prevalence increases with age so the highest estimated prevalence is in adults > 65 years of age.
The past few decades have seen a rise in the number of persons with type 2 diabetes, with more than $60\%$ of these living in Asia (approximately 230 million with an estimated rise above 350 million by 2040) [4]. The South Asian countries will see a $150\%$ rise from 2000 to 2035. According to the Diabetes Atlas 2021, Southeast Asia has 90 million diabetics [5]. Among older adults (≥ 65 years) it is estimated that diabetes will double from 35.5 million in 2019 to more than 78 million in 2045 [5,6]. India and Pakistan are ranked among the top ten countries with elderly persons living with diabetes and would jump from third to second place and tenth to seventh place by 2045 diabetic patients globally [5,7]. India is projected to have 27.5 million elderly persons living with diabetes in 2045 compared to 12.1 million in 2019, and thus, would replace the United States (23.2 million) for the second position globally. Pakistan is estimated to have 6 million compared to 2.6 million for the same years, also scaling up from tenth to the seventh position, globally. Among the SEA countries, India and Pakistan would retain their top two positions in 2030, followed by Bangladesh with more than 2 million elderlies living with diabetes. In Singapore, a local survey found that $29.1\%$ between 60 and 69 years old have diabetes. It also predicted that one in two Singaporeans will experience diabetes by the age of 70 [8]. The prevalence of elderly persons with diabetes in Thailand was $17.2\%$ as per the InterASIA 2003 study [9]. The prevalence of diabetes was ~$11\%$-$13\%$ in the Vietnamese population over the age of 60 years [10]. According to the World Diabetes Federation atlas, the number of elderly with diabetes would increase by more than $200\%$ in two decades in the majority of Asian countries. Thus, these data indicate an enormous surge in the aging population with diabetes in the subsequent decades.
The South Asian region is unique as it still faces the dual pandemic of managing undernourishment and infective disorders at one end and has a rapidly increasing burden of non-communicable disorders like diabetes on the other end of the spectrum. This was further compounded by the COVID-19 pandemic [11,12]. Moreover, the unique thin-fat phenotype which is seen in the South Asian region makes the elderly more prone to develop diabetes with a lower body mass index [13,14]. Furthermore, the dietary pattern in the South Asian region, is a carbohydrate predominant non-diabetic friendly eating pattern, which is difficult to change in an older person [15]. This, along with lower per capita income in the elderly and increasing costs of treatment, in a genetically prone ethnicity, makes diabetes in the elderly even more challenging in this region [16,17]. Thus, the rapidly increasing diabetes burden coupled with the unique features of a thin-fat phenotype, high carbohydrate staple diet, poor affordability, and high-risk ethnicity makes the South Asian population a challenging one to manage. This foreseeable health threat prompted the experts across these countries to develop a consensus addressing the unique aspects of diabetes in older adults, particularly tailoring intervention according to functional status and co-morbidities.
## 1.3 Physiology
Chronological age itself is an independent risk factor for diabetes as well as other chronic diseases [18,19]. Diabetes mellitus is a precipitant of aging. Thus, common pathways partake in the pathophysiology of diabetes and aging. Numerous factors contribute to the pathophysiology of diabetes in older age (Figure 1).
**Figure 1.:** *Aging and type 2 diabetes: Aging worsens systemic inflammation, increases oxidative stress, and poor mitochondrial function resulting in metabolic abnormalities. It also alters body composition predisposing older adults to develop visceral adiposity further inducing pro-inflammatory mediators and amplifying systemic inflammation. It also impacts the beta-cell physiology culminating in impaired responses to endogenous incretins, thus accelerating the development of clinical diabetes manifestations [18].*
Regulation of insulin secretion in response to blood glucose levels permits maintenance of normal range. Unlike young adults, older adults become progressively incapable of regulating glucose metabolism. This is especially true in the post-absorptive phase during which normally half of the hepatic glucose is used by the brain and approximately one-seventh by the skeletal muscle [19].
The Baltimore Longitudinal Study of Aging conducted twice in a span of 30 years found that the impaired response to the two-hour OGTT had a positive association with age; though the glucose tolerance deteriorated from the fourth decade up to the ninth decade of life, the alteration from the sixth decade onwards remained significant even after adjusting for BMI, unlike up to the fifth decade. These findings indicate that up to the fifth decade, blood glucose abnormalities are influenced by body fat and physical activity. With advancing age, plasma glucose levels in response to OGTT were progressively higher for every decade of age until they peaked at the seventh decade, more so in males than females. Although the FPG was attenuated after adjusting for BMI, the correlation of 2hG with age remained significant [19].
On the other hand, studies also confirmed that body composition rather than age (across 30-90 years) affects hepatic glucose production [19]. The association between age and insulin resistance is ambiguous. The insulin-glucose dose-response curve shifts to the right with age both in single- and multiple-insulin dose studies. These data mean that compared to the young, for the same amount of glucose, twice the amount of insulin is required in the old. This association remained significant after adjusting for BMI [19]. When adjusted for body composition, physical activity, and peripheral insulin sensitivity, the effect of age on the non-insulin mediated peripheral glucose uptake is obscure [19]. Aging may reduce the amplitude and mass of rapid insulin pulses during fasted and fed state; although a reduction in the frequency of ultradian pulses occurs during the fasted state and it is regularized in the fed state in the old, its clinical relevance remains unknown [19].
Studies that evaluated the effect of age on β-cell function using hyperglycemic clamps found that the second phase of insulin responses reaches a plateau in nearly half the duration in the elderly than in the young (120-150 minutes vs. 300 minutes) [19]. Incretins are enteroendocrine hormones (glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) that enhance insulin response from β-cells beyond the amount ascribed to glucose alone [19].
Using a hyperglycemic clamp, in combination with OGTT studies that examined the effect of age on the sensitivity of β-cells to endogenous GIP, researchers found that β-cell sensitivity to GIP is reduced in advancing age. The insulinotropic effect of GIP diminishes with age but is unaffected at very high plasma glucose levels. These suggest that the age-related impairment in response to GIP is a significant cause of glucose intolerance [19].
Insulin resistance is primarily attributed to visceral obesity. Overconsumption of food leads to accelerated weight gain in older compared to younger persons because aging is associated with gradual loss of muscle mass and gain in fat mass from the fourth decade. Menopause in women and decline in testosterone with age in men aggravates the loss of lean muscle mass and increase in adipose tissue with suppressed catecholamine-induced lipolysis [19].
## 1.4 Alzheimer’s disease and diabetes
Alzheimer’s disease (AD) and type 2 diabetes mellitus are two of the most widespread diseases in the elderly population globally. Pathologically, AD is characterized by extracellular plaques of amyloid-β and intracellular neurofibrillary tangles of hyperphosphorylated tau [20]. Type 2 diabetes is a metabolic disorder marked by hyperglycemia, insulin resistance, and the formation of human islet amyloid polypeptide that causes pancreatic β-cell dysfunction [20]. The term “type 3 diabetes” may echo the plausibility that Alzheimer’s disease denotes a form of diabetes that only comprises the brain. Both tend to have certain common molecular and biochemical features including, but not limited to, insulin deficiency, insulin resistance, impaired energy metabolism, mitochondrial dysfunction, and oxidative stress [21].
## 1.5 Screening and diagnosis
The recent ADA, AACE, and United States Preventive Services Task Force (USPSTF) 2021 recommend widespread screening for prediabetes and diabetes, using FPG, 2h-OGTT, or A1C level, for all adults ≥ 45 years, regardless of risk factors, and screening adults who are overweight or obese (BMI ≥ 25 or ≥ 23 in Asian Americans) with one or more risk factors, regardless of age. If the glycemic indices are normal, repeat screening is recommended at a minimum of three-year intervals. However, the endocrine societies, namely the ESE, the GSA, and TOS differ from the recommendations of the diabetes associations. For patients aged ≥ 65 years without known diabetes, only the measurement of A1c may be erroneous. This is attributed due to potential comorbidities that can affect the lifecycle of red blood cells in elderly persons. Hence, these guidelines recommend that older persons with normal glycemic indices undergo repeat screening every two years; subject to the prospective benefit, terminally ill patients with malignancy or organ system failure, therefore, could be exceptions. In patients ≥ 65 years of age with abnormal FPG or A1c, a 2h-OGTT could be useful in high-risk patients (overweight or obese, first-degree relative with diabetes, Asians, history of hypertension, dyslipidemia, sleep apnea, or physical inactivity). In this high-risk group, shared decision-making is suggested if a patient is frail or finds the investigation procedure excessively onerous [22]. Nevertheless, 2h-OGTT rises by 5-10mg/ dL and FPG 1–2 mg/dl every subsequent decade and may be prone to overdiagnosis.
‘The experts in the current consensus agree with the Endocrinology Societies’ recommendations in that A1c alone may be inadequate. However, routine screening with FPG alone may also lead to underdiagnosis, hence the ABCDE suggests 2h-OGTT as a better diagnostic marker in the elderly as substantiated by the data from BLSA.’ The BLSA found that if only FPG and A1c are used for diagnosis, more than one-third of those with diabetes mellitus would be missed, particularly Asians with low A1c (because it is influenced by ethnicity) [23]. Therefore, it may be agreed that postprandial hyperglycemia is an important feature of type 2 diabetes in the older population and an OGTT is essential to avoid misdiagnosis based on normal FPG or A1C alone [18]. Hence, the ABCDE recommends OGTT for ruling out diabetes in at least high-risk groups.
‘The ABCDE recommends screening at the available timepoints along with other routine investigations in the elderly and routine annual screening in older adults with risk factors, particularly high BMI (overweight/ obese), hypertension, dyslipidemia, and heart disease.’ Screening for diabetes not only reduces morbidity but also considerably helps to diminish the risk or severity of associated complications. Among more than 150,000 postmenopausal women, the Women’s Health Initiative study found that severity of vasomotor symptoms (night sweats and hot flashes) was proportionally associated with increased risk of diabetes (mild VMS $13\%$, moderate VMS $29\%$, severe VMS $48\%$) irrespective of obesity [24].
‘The ABCDE experts believe that menopause transition may be an ideal window for clinicians to screen for diabetes in women.’ In the Asian geriatric population, a meta-analysis revealed that the prevalence of sarcopenia is higher in persons with diabetes than without ($15.9\%$ vs. $10.8\%$); therefore, screening for diabetes is relevant [25].
A study from India reported diabetes as a risk factor for dementia ($33.3\%$ of those with diabetes vs. $9\%$ of those without) [26]. Studies from India also have suggested that the ApoE4 allele is significantly associated with AD. Other reports from Asia confirm the association between type 2 diabetes and AD in ApoE4 carriers. A meta-analysis of 28 prospective observational studies found that diabetes increased the risk of all types of dementia by $73\%$ and AD by $56\%$ compared to people without diabetes [27,28]. Therefore, persons diagnosed with dementia should be screened for diabetes.
‘The ABCDE experts opine those persons with sarcopenia and dementia should be screened for diabetes.’
## 1.6.1 Unique Symptoms
Elderly persons may present with atypical symptoms, and thus, may delay the recognition of diabetes (Table 2). The subtleness, atypical or delayed manifestation of the classical symptoms are misleading. The elevated renal threshold for glucose excretion and diminished thirst perception alters the presentation of classical symptoms [29-32].
**Table 2.**
| Subtle | Predominant |
| --- | --- |
| Classical/specific symptoms | Non-classical/Non-specific symptoms |
| Polyuria/Nocturia Hyperphagia Polydipsia | Dry mouth Incontinence DehydrationConfusion Unexplained weight lossFatigueLethargyFrequent fallsSkin Infections Genitourinar y Infections |
## 1.6.2 Traditional Vascular Complications
Older persons with diabetes have poorer end-organ function due to aging and co-morbidities. One or more co morbidities are present in $60\%$ and ≥ 3 co-morbidities are present in $40\%$ of the elderly persons with diabetes [18,33].
## 1.6.3 Macrovascular (Cardiovascular, Cerebrovascular, Peripheral vascular Disease) Complications
More than $40\%$ of elderly adults with diabetes experience some type of coronary heart disease (CHD) [34]. More than $20\%$ of older adults with diabetes have silent myocardial ischemia [35]. Among patients aged 65-84 years, the prevalence of MI was higher in those with than without diabetes ($11.3\%$ vs. $8\%$; $$p \leq .032$$). The CV health study in adults aged ≥ 65 years showed that the CHD-associated mortality in persons with diabetes either treated with oral agents or insulin was more than twice as high as those without [36]. Thus, among older patients with diabetes, CHD is a leading cause of fatal outcomes. Like CHD, the prevalence of CVD in older adults aged between 65 to 84 years is significantly higher in those with than without diabetes ($10.6\%$ vs. $7\%$; $$p \leq .003$$) [37]. Advancing older age intensifies the risk of developing PVD. This was corroborated by an Indonesian study in which persons with type 2 diabetes in the seventh decade were sevenfold more likely to develop PVD compared to those in the sixth decade of life [38].
## 1.6.4 Microvascular (Retinopathy, nephropathy, neuropathy) Complications
Unlike the macrovascular, the prevalence of ocular complications of diabetes in the elderly (≥ 65 years) is similar to that in middle-aged (40-64 years) patients ($29.5\%$ vs. $28\%$; $$p \leq .64$$) [39]. Moreover, retinopathy is less common among adults diagnosed with diabetes in older age compared to middle age owing to the lesser duration of disease [39].
In older adults (> 60 years), the most common basis for CKD and end-stage renal disease (ESRD) is diabetic nephropathy [40]. The prevalence of CKD was significantly higher in individuals > 65 years diagnosed with diabetes compared to those without, with an absolute difference of $8\%$-$17\%$ (Kidney Early Evaluation Program $48.2\%$ vs. $40.4\%$, NHANES $58.3\%$ vs. $41.4\%$, Medicare $14.2\%$ vs. $4.4\%$; $p \leq .001$) [41].
Similar to CV complications, long-standing (≥ 25-year) diabetes increases the risk of neuropathy with more than $50\%$ being diagnosed with diabetic peripheral neuropathy (DPN) [42-44]. The presence of DPN may impair their balance due to malfunctioning of the three major elements: sensory (lack of motion), motor (impaired movement coordination), and autonomic (the existence of postural hypotension) [45]. Diabetic peripheral neuropathy increases the risk of falls almost five-fold.
Further autonomic neuropathy is suspected to be a modifier of the extent of QT-prolongation which is a known risk factor for ventricular arrhythmias [46]. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial found a higher incidence of hypoglycemia and mortality in intensively treated type 2 diabetes patients. Thus, it is plausible that hypoglycemia induces cardiac arrhythmias in patients with type 2 diabetes, causing the ‘dead-in-bed syndrome’ [46].
## 1.7 Geriatric syndromes and Diabetes: Geriatric-specific Complications
In addition to the traditional vascular complications which affect the quality of life, the incidence of a set of conditions termed geriatric syndromes, prevails at a greater frequency in older adults with diabetes which leads to diminished self-care abilities and clinical outcomes.
## Prevalence
The proportion of older adults/elderly and those diagnosed with diabetes is progressively increasing, and therefore, menopausal women may form a substantial proportion of the diabetic population ($19.4\%$ post-menopausal vs. $12.1\%$ pre-menopausal in Asian women) [47].
Late-onset hypogonadism (LOH) or age-associated testosterone deficiency syndrome occurs with advancing age [60]. Hypogonadism is reported in $33\%$-$64\%$ of men with diabetes and men with low testosterone levels are at risk of impaired glucose tolerance [61-63,65].
According to the AWGS criteria, the prevalence of sarcopenia in Asian adults (aged ≥ 65 and > 60 years, respectively) with type 2 diabetes is $15\%$ using the AWGS definition [72,73]. Older adults with sarcopenia may have an increased risk of developing type 2 diabetes as confirmed in large epidemiological studies which showed the double risk of developing diabetes in individuals in the lowest skeletal muscle mass quartile compared to those in the highest quartile [74-76]. Lower handgrip strength was also prognostic of higher fasting glucose in subjects over 40 years [77]. The Korean Sarcopenic Obesity Study, Health ABC Study, and the English Longitudinal Study of Ageing demonstrated that patients with type 2 diabetes have three times the risk of low skeletal muscle mass, diminishing muscle mass over five years, and $43\%$ greater odds of low handgrip strength at the end of eight years compared to non-diabetic controls [78-80].
Advancing age and lifestyle, both affect the incidence of type 2 diabetes and osteoporosis and both frequently co-occur. Type 2 diabetes mellitus is a significant risk factor for fractures. Though the Bone mineral density (BMD) may be normal in these persons with type 2 diabetes, the fracture risk is increased, indicating deteriorating bone health.
A pooled analysis of 14 studies across Asia, Europe, and the USA found that diabetes was significantly correlated with ~ $60\%$ increased risk of dementia, and there was a $40\%$ risk of nonvascular dementia [97]. More than 66,000 elderly (≥ 65 years) patients with diabetes were found to have a $28\%$ greater risk of cognitive impairment compared to those without in a cross-sectional study using the Abbreviated Mental Test [98]. The prevalence progressively increased with increasing age; from $13.1\%$ for the age group 65-74 years to $24.1\%$ in those > 75 years [99]. A cross-sectional study from India found nearly $33\%$ of older adults with diabetes at risk of cognitive impairment [100]. The Edinburgh Type 2 Diabetes Study showed that memory worsened progressively with the severity of hypoglycemia and diabetic retinopathy after adjustment for confounders. Similarly, stroke significantly accelerated the cognitive deterioration in a study over fifteen years. After adjusting for this macrovascular complication, the association between accelerated decline in cognition and diabetes was further reinforced compared to nondiabetics [101-103].
There has been a steady rise in the occurrence of hypoglycemic episodes due to the increasing trends in achieving tighter glycemic control and prevalent co-morbidities. Hypoglycemia is the second most common adverse drug reaction. Insulin was the second most common medication associated with ED visits in older people ≥ 65 years, due to hypoglycemia [115,116]. The exact incidence of hypoglycemia is not known but it is higher in older than younger persons with diabetes. A one-year, prospective, observational study found significantly higher episodes in persons aged > 70 years than < 60 years old ($12.8\%$ vs. $9.0\%$, $p \leq .01$) and more persons in this group needed medical assistance ($0.7\%$ vs. $0.1\%$) [117]. The incidence is even higher in the community setting up to $41.9\%$ over one year [118].
In the data derived from the NHANES I population-based sample aged 55–74 years, diabetes was associated with an almost double risk of having a serum albumin concentration < 38 g/L. Also, reduced albumin is associated with an unfavourable metabolic profile [131].
Among the various electrolyte abnormalities commonly found in the elderly, hypernatremia and hyponatremia ($11\%$-$18\%$) are the most frequent [137]. Both are associated with a high fatality rate. Based on the settings and the definition, hyponatremia ranges from $2.5\%$ to as high as $50\%$. Electrolyte disturbances are frequent in persons with diabetes, particularly the elderly.
It has been suggested that the prevalence of malnutrition or its risk in elderly patients with diabetes is greater than $50\%$ [141]. According to the Mini Nutritional Assessment (MNA), malnutrition is higher in the elderly with diabetes than in those without diabetes [141].
## Pathophysiology
Menopause has deleterious effects on body weight and adipose tissue distribution. Further reduced energy expenditure along with diminished insulin secretion and decreased insulin sensitivity increase the risk of diabetes in addition to aging. The surge in visceral obesity-associated pro-inflammatory state and androgen excess creates an insulin-resistant environment which increases the incidence of metabolic syndrome compared to reproductive age women ($30\%$-$70\%$ vs. $14\%$-$45\%$). However, data obtained from the Women’s Health Study have shown that glucose abnormalities observed during menopause are associated with age and not with the waning ovarian function [48]. On the contrary, several studies have shown that premature menopause, whether spontaneous or induced or lesser number of reproductive years, is associated with a $20\%$-$57\%$ increased risk of diabetes, with some studies reporting a risk of $18\%$ after adjusting for obesity [24,49-52]. Diabetes per se may hasten the arrival of menopause [53].
The association between LOH and diabetes is bidirectional/two-way; insulin resistance is a shared feature [64]. Insulin resistance is both the horse and cart in hypogonadism [65]. This association may be true because high testosterone levels have shown a $42\%$ reduction in the risk of development of diabetes [65-69]. The concern in patients with diabetes and LOH is that both may additively increase the risk of fractures [64]. Male patients with both conditions have high or normal BMD; however, the bone dimensions are lower, with a significantly decreased bone turnover, which is primarily the predominant effect of diabetes on bone. Thus, reduced osteoblastogenesis and osteoblast activity could be decisive elements affecting bone health in males with LOH and diabetes [65].
Sarcopenia similar to LOH is the cause and result of diabetes (bidirectional), predisposing older adults to the development and worsening of diabetes through abnormal glucose clearance due to low muscle mass (normally responsible for $80\%$ glucose disposal), and also increased inflammation due to cytokines produced by the excess adipose tissue [81]. Insulin resistance, inflammation, AGE buildup, augmented oxidative stress, and vascular complications impact muscle health; and weakened muscle health also enhances the risk of developing and worsening type 2 diabetes [82].
Type 2 diabetes directly affects bone metabolism and strength, certain anti-diabetic medications affect bone metabolism, and there is an association between diabetic complications and risk for falls and subsequent fractures [56]. Long-standing diabetes, its poor control, and associated complications have been shown to increase the risk of fractures [85-89]. Although high FPG is associated with an increased risk of fracture, elevated PPG has shown the reverse which could be due to high BMI [56]. Furthermore, unlike type 1, type 2 diabetes is not a listed secondary cause of osteoporosis in the FRAX online tool which calculates the ten-year fracture risk probability since BMD is usually higher in those with than without diabetes [56]. An increased risk of hip fracture is observed even in persons with higher BMD. Clinical evidence in older adults suggests that femoral neck BMD T-score and the WHO FRAX score were associated with hip and nonvertebral fractures. Compared to those without diabetes, the fracture risk was higher in persons with diabetes for a given T-score, FRAX score, and age [90]. Bone mineral density assessment as a standalone tool may not sufficiently represent bone health in persons with diabetes [91]. Therefore, instead of BMD, the trabecular bone score related to bone microarchitecture and fracture risk which provides information independent of BMD should be preferred in elderly with diabetes and post-menopausal women [91,92].
Hyperglycemia, oxidative stress, and the formation of AGEs, negatively affect bone remodeling. Vitamin D deficiency, which is found in diabetics due to various interrelated factors such as obesity, less physical activity, and reduced sun exposure, aggravates calcium deficiency and may increase the risk of osteoporosis.
Diabetes may increase the risk of neurodegeneration [96]. Chronic exposure to hyperglycemia can deteriorate cognitive function and other aspects of mental health (Figure 2). Reports have demonstrated that hyperglycemia is closely related to the development of cognitive impairment and dementia, suggesting that there may be a cause-effect relationship between hyperglycemia and dementia. Depression is a risk factor for the development of age-related cognitive dysfunction in people with diabetes, independent of vascular complications [104].
**Figure 2.:** *Type 2 diabetes and cognitive dysfunction.*
## Treatment
The basis of managing menopausal diabetic women is lifestyle intervention involving a diet with an energy deficit of 500-750 kcal/day and ≥ 150 minutes/week of moderate exercise or ≥ 75 minutes/week of vigorous exercise [54,55]. Gradual weight loss (NMT $5\%$–$7\%$ of initial body weight per annum) is recommended; since declining, bone mineral density and sarcopenia are important concerns in postmenopausal women [56,57]. Smoking cessation is recommended because it is a known risk factor for CVD and osteoporosis similar to diabetes and aging [57].
Compensatory estrogen supplementation (hormone replacement therapy-HRT) may improve glucose metabolism regardless of diabetes status. In women with pre-existing diabetes and negligible CVD risk, oral oestrogens may be chosen, whereas, in postmenopausal obese and diabetic women, transdermal 17 β-oestradiol should be preferred. Notwithstanding, a progestogen such as progesterone or transdermal norethisterone should be chosen to prevent effects on glucose metabolism.
In women on HRT, the incidence of diabetes reduced from $21\%$ to $12\%$ owing to the improvement in adiposity-associated changes and pro-inflammatory state. But HRT is not a chronic therapy and evidence substantiating its use for diabetes prevention is weak [58]. However, in diabetic women who attain menopause, HRT enhances glycemic control and thereby lowers the dosages of OHA; however, evidence confirming the effect on CVD is missing. Possibly, HRT may be useful in diabetic women during the early menopausal period unlike in older women in whom destabilization of atheroma may occur resulting in acute thrombotic episodes [57].
Studies also have found a higher incidence of urinary tract infections (UTIs) in postmenopausal women with type 2 diabetes [59]. Use of estrogen, either oral or vaginal, maintenance of healthy vaginal flora using vaginal formulations, and antimicrobial therapy may be useful in the prevention and treatment of UTIs in this patient population [59].
‘The ABCDE experts recommend an evaluation of preexisting CVD to determine the benefit of HRT regardless of a diabetes diagnosis. Lifestyle intervention involves diet and moderate/vigorous exercise [54,55]. Gradual weight loss (NMT 5–$7\%$ of initial body weight per annum) [56,57]. Smoking cessation is recommended [57].’ Although few patients may desire relief from hot flashes, increased sweating, loss of libido, low energy levels, and erectile dysfunction, others may be keen on improving bone health and muscle mass. It is necessary to contemplate those symptoms such as fatiguability, sarcopenia, loss of bone mass, central obesity, and sexual dysfunction that are common to LOH and diabetes. Hence lifestyle intervention is essential along with HRT to enhance its effectiveness. Sometime, nonpharmacological means may even preclude the use of hormone replacement.
Observational studies and systematic reviews have demonstrated the benefits of testosterone replacement therapy (TRT) in hypogonadal men with diabetes. The TIMES2 study showed beneficial effects of TRT on insulin resistance, total and LDL-cholesterol, lipoprotein-a, and sexual health.
Individualized decision-making based on concurred and symptom-led outcomes enhances adherence and improved the probability of attaining endpoints [70].
‘Experts recommend, an individualized and patient-centric approach as it is pragmatic and rationale and enhances compliance since the expectation of patients are met [70]. Only TRT may not be adequate, lifestyle intervention may enhance its effectiveness and sometimes even preclude its need’ Exercise and dietary changes, especially improving the dietary protein intake are the cornerstones of therapy for patients with diabetes and sarcopenia [82]. Resistance training is the most effective strategy for improving lean muscle mass, strength, and metabolic health in sarcopenic and diabetic individuals. It also reduces the muscle loss associated with caloric restrictions by $50\%$ [83]. Aerobic exercise improves mitochondria-derived oxidative stress and may at least partially benefit persons with sarcopenia [84]. However, their efficacy in older diabetic and sarcopenic adults is yet uncharted. Furthermore, improving dietary protein is also challenging in the South Asian region.
There are several drug classes in the pipeline which may improve muscle mass, and physical function, thus improving the metabolic status of diabetes patients [73].
‘Recommendations: The experts, suggest a balance of dietary modifications, aerobic and anaerobic exercise in older diabetic adults at risk of sarcopenia. In the functional elderly individuals, resistance training should be encouraged.’ As per the Indian Society for Bone and Mineral Research (ISBMR) position statement, in elderly individuals with type 2 diabetes, the intervention threshold should be increased to T-score ≤ −2.0 at the femoral neck or total hip or lumbar spine measured by DXA, unlike non-diabetics in whom T-score ≤ -2.5 [93].
Appropriate exercise program after evaluation of the multifactorial fall risk assessment.
Selective use of orthotics could help reduce discomfort, prevent falls and fractures, and improve quality of life. Supplementation with vitamin D3 greater than 1000 to 2000 international units (IU) of daily maintenance therapy is recommended to maintain an optimal serum 25(OH)D level [93]. Adequate dietary intake of calcium with a total intake (including diet plus supplement, if needed) of ≥ 1000 mg/day is recommended. Bisphosphonates, raloxifene, denosumab, and teriparatide increase BMD in patients with diabetes, the former reduce the risk of vertebral and latter non-vertebral fractures [56,93-95].
‘The trabecular bone score related to bone microarchitecture and fracture risk should be used for evaluation of osteoporosis in elderly persons and post-menopausal women with diabetes, instead of BMD. In suspected cases of secondary osteoporosis, FPG, PPBG, and A1C should be evaluated in a known or suspected case of Type 2 diabetes.’ A cross-sectional analysis of the large PREDIMED-Plus study on the elderly suggested that once diabetes has been diagnosed, cognitive decline prevention strategies need to be implemented to improve treatment adherence and quality of life [111]. When cognitive decline is diagnosed, appropriate behavioural therapy should be initiated [112]. No disease-modifying therapy is currently available to cease or decelerate the processes that lead to dementia [113]. Only symptomatic (psychotropic) and standard (cholinesterase inhibitors (ChEIs) and a partial N -methyl-D-aspartate antagonist) AD therapies are currently available [114].
## 1.7.3 Sarcopenia
The Asian Working Group for Sarcopenia (AWGS) defines it as low muscle strength, low muscle mass, and poor physical performance measured as handgrip strength: < 26 kg (men) and < 18 kg (women), ALM/ height2: < 7.0 kg/m2 (men) < 5.4 kg/m2 (women) and gait speed ≤ 0.8 m/s (6-m course), respectively [71].
## 1.7.5 Impaired Cognition and Dementia
Aging is an established risk factor for cognitive impairment and dementia, and co-existing diabetes worsens it [96].
## Investigations
Older diabetic patients with identified risk factors should be prioritized for a screening of cognitive impairment at the primary care level [100]. Annual screening for cognitive functions using simple appropriate tools (Mini-Mental State Examination Mini-Cog, and the Montreal Cognitive Assessment) should be conducted [105-110]. It is important to note that although robust data are missing currently, recognizing cognitive decline early will have a significant effect on diabetes management in these older adults. The cognitive assessment also should be conducted when glycemic control is impaired. Frequent episodes of hypoglycemia also should prompt cognitive assessment, because it could be due to the inability to identify hypoglycemic signals and consequently incapability of preventing it. Elderly with diabetes who are found to be positive on screening should be referred for prescribed cognitive/neuropsychological assessment.
## Definition
Consistent with a Consensus Report from the ADA, AACE, and other Diabetes Associations three levels of hypoglycemia have been defined- level 1 or hypoglycemia alert (≤ 70 mg/dL); level 2 or clinically significant hypoglycemia (< 54 mg/dL) and level 3 or severe hypoglycemia, associated with severe cognitive impairment necessitating hospitalization for recovery (no specific glucose level).
Hypoalbuminemia defined as serum albumin concentration < 1.5 g/dL [130].
## Causes
The counter-regulatory responses to hypoglycemia in persons with diabetes are defective [119]. The risk of hypoglycemia is increased in older adults due to several co-morbidities, including diminished hormonal regulation and counter-regulation, insufficient intake of water and/or food, decreased intestinal absorption, and cognitive impairment [120]. Polypharmacy, frequent in the elderly may increase the risk of severe hypoglycemia [121]. This patient population also demonstrates age-related changes (increased fat: muscle mass ratio, decreased renal function, decreased drug clearance) in pharmacokinetics and pharmacodynamics thus potentially increasing the risk of adverse events [121,122].
Various studies demonstrate a progressive reduction of albumin serum concentration between 0.08 and 0.17 g/L/year, associated with aging [132]. Diabetes may be associated with impaired liver production of albumin because insulin is an important regulator of its synthesis [133]. Cross-sectional studies show that hypoalbuminemia is an independent risk factor for frailty in elderly with diabetes, suggesting relative malnutrition in these frail patients [133].
Dyselectrolytemia may occur due to altered dispersal of electrolytes corresponding to hyperglycemia-induced osmotic fluid shifts or total-body deficits owing to osmotic diuresis [138]. Elderly persons are at particular risk due to a decline in cognitive ability and may experience a syndrome of inappropriate antidiuretic hormone (SIADH) secretion [138]. Pharmacotherapies used for the treatment of diabetes also may result in electrolyte disturbances, specifically dysnatremia. Additionally, higher water intake in combination with a low salt diet adopted by many elderly patients and hypoproteinaemia due to diabetes and malnutrition may increase the risk of ‘Tea and toast’ hyponatremia [139].
## Presentation
In older adults, the counter-regulatory response depends on the frequency of hypoglycemic episodes they experience, with recurrent episodes reducing the glycemic level at which these mechanisms are activated [115]. Thus, the elderly with diabetes not only manifest fewer pronounced symptoms but also do so at progressively lower glucose levels compared to their younger counterparts. They tend to present with neuroglycopenic symptoms (i.e., dizziness, visual disturbances, increased agitation, and/or confusion) rather than classical adrenergic symptoms (i.e., palpitations, sweating, tremors), leading to loss of self-correction window [123]. Also, lack of cerebral glucose causes non-specific symptoms like nausea, falls, and unsteadiness. The neuroglycopenic symptoms are also non-specific because these may occur due to several other conditions, especially in the elderly [122,123]. Furthermore, such symptoms may occur even below the level associated with neuroglycopenia, a condition termed hypoglycemia unawareness. Thus, in older adults, hypoglycemia-associated autonomic failure can occur [115]. Therefore, due to the non-specific nature, multiple potential causes, and co-morbidities, recognizing hypoglycemia in the elderly is challenging.
Modest to very low serum albumin concentrations are associated with muscle wasting, disability, and higher morbidity and all-cause mortality in older persons [134]. Because albumin functions as a low-affinity, high-capacity carrier of several different endogenous and exogenous compounds acting as a depot and a carrier for these compounds, a drop in its serum levels may lead to increased free drug concentration in the plasma, more rapid hepatic metabolism, or both, which needs to be taken into consideration when treating diabetes and co-morbid diseases [135].
## Management
Periodic feedback to determine awareness of hypoglycemia, any associated symptoms, adherence to treatment, meal frequency, and unintentional repeat doses should be obtained from patients and their caregivers, (e.g., selected questions from the Diabetes Care Profile) [124,125]. Also, any future risk should be evaluated using validated risk calculators, and these older patients should be categorized based on the severity of anticipated hypoglycemia [126].
Consideration should be given to cognitive abilities and co-morbidities in older adults when determining the treatment strategy. The most obvious method is to select anti-diabetic regimens with a lower risk of hypoglycemia. Glycemic targets should be adjusted to avoid any risk of hypoglycemia, and at the same time, not enhance hyperglycemia [147].
Studies suggest serum albumin concentration of elderly people to be maintained at ≥ 4.0 g/dL to prevent frailty [136]. Such elderly with diabetes should be supported with sufficient high biological value protein and energy intake for anabolism.
## 2.2 Severe Hyperglycemia
Hyperglycemic hyperosmolar syndrome (HHS) is a particularly severe complication of unidentified or sub-optimally treated hyperglycemia in older adults. Dehydration, electrolyte abnormalities, urinary incontinence, dizziness, and falls should prompt appropriate glucovigilance to avoid HHS because it has a poorer prognosis in older than younger adults [127-129]. Providing a detailed approach to its management is beyond the scope of the current document.
## Caution/Careful considerations
Because advancing age and diabetes (irrespective of hyperglycemia) per se increase the risk of dysnatremia, careful consideration of therapies that affect the electrolyte balance is necessary [140]. It is noteworthy that unlike TZDs and first-generation SU, second-generation SU rarely causes dysnatremia. SGLT-2i and GLP-1 agonists have not been found to cause electrolyte abnormalities [139,140]. Thiazide diuretics, and to a lesser, extent loop diuretics, induce hyponatremia [139]. Antidepressants associated with hyponatremia are predominantly attributed to SSRIs, SNRIs, and mirtazapine in persons ≥ 60 years of age [139]. Elderly persons treated with anti-epileptic drugs (AEDs) such as valproic acid, phenytoin, or topiramate have a higher risk of being hospitalized with hyponatremia. Other AEDs such as carbamazepine, oxcarbazepine, eslicarbazepine, lamotrigine, levetiracetam and gabapentin also induce hyponatremia [139].
## Effects on medication
Malnutrition is associated with functional impairment (decreases in activities of daily life, grip strength, physical performance of the lower limbs) and consequently poor quality of life, longer hospital stays, and increased rates of institution and mortality [141]. Malnutrition also plays a key role in developing frailty and sarcopenia. Muscle hypertrophy and atrophy may occur due to poor protein intake [141]. It can hinder normal brain function and encourage cognitive loss [141]. Thus, in elderly patients’ diabetes and its complications are compounded by malnutrition [142].
Besides the disease prolonged use of metformin causes vitamin B12 deficiency in $10\%$-$30\%$ of patients with diabetes. It may worsen cognitive performance in elderly patients [143]. However, vitamin B12 deficiency also has been reported in diabetic patients not taking metformin [144,145]. These factors should be specially considered in patients who develop paresthesia and neuropathy [146].
## Goals for glycemic control in elderly
Considering the multiple co-morbidities and syndromic factors associated with advancing age, the usual glycemic targets recommended in younger adults are not justified in older adults with diabetes. In older adults with diabetes, the aim is to maintain an optimal quality of life, which in turn, depends on individual patient risk factors concurrently avoiding acute glycemic extremes.
Hypoglycemic events are a frequent occurrence in older adults due to diminished sympathetic counter-regulatory responses. Moreover, hypoglycemia predisposes this patient population to increased risk of diminished cognition, dementia, all-cause hospitalization, and all-cause mortality [147-149]. Hyperglycemia due to undertreatment increases the risk of dehydration, dizziness, falls, and long-term mortality [18,150].
Inadequacy of quality RCTs focussing on older diabetic adults impedes the establishment of suitable glycemic goals. Pivotal trials namely ACCORD, VADT, and ADVANCE trials in older adults have failed to demonstrate any additional benefit in cardiovascular outcomes with tight glycemic control as per AACE (A1c < $6\%$ or < $6.5\%$), rather ACCORD was prematurely discontinued due to higher mortality ($$p \leq .04$$) and higher frequency of hypoglycemic events ($p \leq .001$) in the intensively treated group [151-153]. However, even an A1c level > $8\%$ is associated with increased morbidity and mortality [154]. Based on this clinical evidence in older diabetic adults, rather than focussing on contentious glycemic targets, attuning/ personalizing the glycemic goals as a function of the patient’s duration of diabetes, life expectancy, functional status, concomitant disease conditions should be considered. Management should aim at a realistic and modest glycemic goal of A1c between $7\%$ and $8\%$ rather than tight control in old diabetic patients (Table 3) [155]. It is worth noting that although there are small differences in the A1c targets among guidelines, all of them recommend a personalized/individualized approach to diabetes management (Table 4).
‘The ABCDE experts agree that there is no ‘single best’ – the best varies for each patient, therefore instead of arguing on the glycemic goals, a tailored approach would be vital in maintaining an asymptomatic and quality life for the older adult with diabetes.’
## Prevention of falls
A major cause of morbidity and mortality in older adults with diabetes is falling; the risk in the elderly with diabetes is 17-fold higher compared to those without diabetes [156]. The three-year Longitudinal Ageing Study in Amsterdam found a $67\%$ greater risk of recurrent falls (≥ 2 falls/6 months) in the elderly (> 65 years) with diabetes compared to those without which persisted after adjusting for other risk factors [157]. Overall, $18\%$ of the cohort from the Study of Osteoporotic Fractures followed for more than seven years experienced recurrent falls. Compared to nondiabetics, diabetics treated with OHAs and insulin are at $68\%$ and $178\%$ higher risk of recurrent falls (> 1/year) [158]. These findings for insulin were substantiated again by the five-year Kaiser Permanente Registry in the elderly (> 60 years) [159]. Diabetes is also known to increase the risk of psoriatic, gout, and rheumatoid arthritis, causing movement-limiting pain and mobility weakness. Persons with diabetes are also at twice the risk for osteoarthritis compared to the general population.
It is essential to screen and modify the risk factors for falls in elderly persons with diabetes and formulate interventions tailored to address the detected issue (balance, walking dysfunction, reactions, muscle weakness, pain) (Table 5) [156,160,161].
**Table 5.**
| Causes/Risk factors for Falls/Fractures | Multifactorial Preventive Interventions |
| --- | --- |
| Hypoglycemia, hypotension, postural hypotension | • Reduce the intensity of glycemic control• Adjust antihypertensive and psychoactive medication• Treat postural hypotension |
| Diabetes-related loss of strength, arthritis, pain | • Structured exercise• Balance training (yoga, tai chi)• Aerobic exercise• Resistance training• Co-ordination training |
| Loss of sensor y perception, balance secondar y to peripheral neuropathy, ataxia | • Agents to relieve neuropathy signs and symptoms• Medical nutrition therapy, vitamin B12, vitamin D supplementation |
| Secondar y hyperparathyroidism and renal osteodystrophy related to DPN | • Treat underlying pathology |
| Polypharmacy | • Reduce polypharmacy to improve cognitive function |
| Cognitive impairment | • Cognitive training/therapy |
| Dyselectrolytemia | • Correction of electrolyte levels |
| Psoriatic, gout, and rheumatoid arthritis pain | • Immune therapy |
| Reduced visual acuity due to cataracts/ macular degeneration/ glaucoma | • Treat accordingly |
| Arrhythmia | • Treat accordingly |
| Foot problems (plantar pressures, ulcer, bony prominences) causing discomfort restricting movement | • Use orthotic devices (insoles, rocker-bottom shoes)• Orthoses compensate for age-related changes (improve groundcontact, house boney eminences, and alleviate skeletal imbalances)• Reduce pain and improve the ambulatory capability |
| Footwear | • Encourage patients to wear low-heeled shoes |
## Lifestyle management
In the elderly with diabetes, lifestyle management should be tailor-made to their frailty status. Optimal nutrition with sufficient protein intake along with a structured exercise program is aimed at decreasing sedentary intervals and increasing mobility in frail elderly persons with diabetes. In the elderly who are not frail, but are overweight or obese, given its multiple benefits, an intervention aimed at weight loss is recommended [162].
## Diet and Nutrition
Nutritional status should be assessed for older patients with diabetes for the preclusion of cognitive impairment, frailty, and mortality is BMI ≥ 30 kg/ m2, weight gain (> $10\%$), metabolic syndrome (< 75 years of age), sarcopenic obesity, BMI < 18.5 kg/m2, inadvertent weight loss, and malnutrition. A dietitian’s referral should be advised in this regard because they provide advice for maintaining nutritional status and medical nutrition therapy, when necessary, bearing in mind the patient’s personal preferences (age-related deterioration in taste, dental problems, co-morbidities, dietary restrictions, poor gastrointestinal function), food availability, and individual goals which improve QoL [162,163] (Table 6).
**Table 6.**
| Protein intake | Attenuates the decline in muscle mass and insulin sensitivity that occurs with increasing age and diabetes. |
| --- | --- |
| Protein intake | Helps in an increase in protein anabolism, ‘high-quality’ weight loss (fat loss and muscle preservation/gain), enhanced glycemic control, daily appetite control, and satiety. |
| Protein intake | Protein intake should be 1.0-1.2 g/kg/day in older adults and 1.2–1.5 g/kg/day in those who are at risk of malnutrition. |
| Dairy foods | Increased consumption may be beneficial for older adults with diabetes due to the combined effect of dairy on both insulin sensitivity and lean body mass. |
| Diet rich in fruits and vegetables as well as exogenous antioxidant vitamins (such as vitamins E and C and Carotenoids) and minerals | Restores the skeletal muscle redox homeostasis and prevents oxidative stress, thereby contributing to muscle maintenance. |
## Exercise and Physical activity
Frailty prevalence is ~$10\%$ in the population over 60 years old and increases to > $25\%$ in those ages ≥ 80 years of age [164]. A cross-sectional study showed that compared to Europeans, South Asians (~50 years of age) need to undertake ~230 minutes of moderate-intensity physical activity every week [165].
In older adults, apart from improved glycemic benefit, exercise also improves body composition and arthritic pain, reduces falls and depression, increases strength and balance, enhances the quality of life, and improves survival [166-168]. It is prudent to get the patient to start an exercise or physical activity program gradually and encourage them to remain consistent, with recommended activities based on functional status (Table 7) [169-171].
**Table 7.**
| Elderly with diabetes | Intervention | Goal |
| --- | --- | --- |
| Frail- mobility, and gait issues at high risk of fall/fractures Cognitive impairment, vision impairment | Physical therapists and occupational therapistsWeight training and aerobic exercise | Improve balance and muscle strengthImprove mobility to enhance functional capacity |
| Non-frail, independent | Aerobic exercise 30 minutes for 5 days/ week | Maintain mobility and cardiovascular health |
| High risk of CVD | Walking or aerobic exercises (routine ECG/stress test) | To reduce the risk of coronary heart disease and cerebrovascular disease |
## Stress Management
Chronic diseases like diabetes and associated socioeconomic factors can be major causes of stress among the elderly. Chronic psychological stress may increase inflammatory and platelet aggregation responses and result in poor diabetes control due to activation of the hypothalamic-pituitary-adrenal axis [172]. If such stressors are not managed, the elderly are at risk of developing mental health issues like anxiety and depression. Considering that psychosocial impact affects morbidity and mortality in persons with diabetes, psychosocial facets should be included at all levels of diabetes management. Thus, elderly persons with diabetes require access to stress management along with treatment of metabolic abnormalities to improve treatment adherence to achieve good glycemic control, reduce the risk of complications and improve QoL [173]. Psychosocial stress management could include Cognitive behavioral therapy, motivational therapy, problem-solving therapy, coping skills training, and family behavior therapy (Table 8) [174-178,180,189].
**Table 8.**
| Method | Description |
| --- | --- |
| CBT | • Recognition of irrational or rational thoughts which contribute to the stress.• Using CBT these dysfunctional thoughts on diabetes and other life issues should be minimized.• The elderly should be educated to practice questioning themselves in stressful situations• [Questions such as ‘Is my handling of this concern appropriate or helpful? What would be helpful, appropriate handling (thought, behaviour)? Can the problem be pushed aside without worrying?] |
| Problem-solving strategies | • It is important to identify patients’ exaggerated fears or worries as well as extreme callousness regarding disease and treatment. These issues should be addressed using problem-solving strategies. |
| Motivational interviewing | • Motivational interviewing self-management program leads to significant improvement was found for self-efficacy, self-care behaviour, glycemic control, and quality of life (daily life satisfaction, influence of disease)• Assessment of perception of self-management level, hurdles, and coping strategies for self-management and goal setting should be performed.• Use of verbal persuasion and appreciation for sustaining self-care. Reassess barriers and offer more coping strategies if goals are not met. |
| Family behaviour therapy | • Involving family members and caregivers in interventions for the elderly population may be vital for providing them with social support related to their diabetes management.• Spouse participation in diabetes education program for elderly diabetic patients showed greater improvements in knowledge, metabolic control, and stress level than those who participated alone.• Such programs involving family social support training have also helped older patients to maintain dietary restrictions. |
| Social Interaction | • Communication between social groups for the elderly at sports centres, local parks, religious centres may improve selfmanagement skills and the elderly may also find motivation from patients of the same age-group |
## Sleep Hygiene
Diabetes can cause direct sleep disturbances due to nocturia, polyuria, diabetic neuropathy and neuropathy pain, and other several chronic illnesses that can impair sleep and quality of life [181]. It is important to address their sleep issues and the impaired quality of life due to inadequate and fragmented sleep, as it may be severely affecting their glycemic control and quality of life. Therefore, sleep education is an essential aspect of diabetes management in the elderly (Table 9) [182-184].
## Environmental Modulation
Aging with individuality is vital for the elderly which includes living independently in one’s home and choosing on spending one’s time [185,186]. These aspects are influenced by an interaction between health and the environment. Physical environmental barriers can generate hazards in the home and community. Elderly individuals with chronic conditions and functional limitations are not only at significant risk for adverse health events (such as falls) and injuries but also face difficulties in performing ADLs. Moreover, these barriers attenuate the effectiveness of caregivers, assistive technologies, and health care devices sometimes necessitating early institutionalization (Table 10) [187,188].
‘Experts suggest that given the less consultation time (~3.5 minutes) and large patient pool, physicians should recruit dietitians and patient care educators for patient benefit since lifestyle changes form the cornerstone of diabetes management.’
## Pharmacological
The pharmacotherapy for the elderly should be directed at achieving good glucose control, without causing hypoglycemia, and preventing symptomatic hyperglycemia. Intensive glycemic control should be avoided and so should polypharmacy. Polypharmacy increases the risk of drug-drug and drug-disease interactions, particularly in elderly persons with diabetes with sensory and cognitive deficits due to age [189]. Such deficits may impede the prompt conveying of initial symptoms of adverse drug events. Therefore, pharmacotherapy for the elderly person with diabetes should be chosen based on individuals’ functional capabilities. These include but are not limited to visual, motor, or cognitive abilities, pre-existing comorbidities, and risk factors (Table 11 and Table 12).
## Therapeutic parsimony
Geriatric physiology and psychology are different from those of younger and healthy adults [234]. Due to age-related changes in pharmacokinetics and pharmacodynamics, elderly persons respond to lower doses of several drugs [235]. Furthermore, they also occasionally forget to take their medicines at the right dose and at the right time. Unfortunately, however, polypharmacy becomes more common with advancing age. This is attributed, at least in part, to the presence of multiple co-morbidities and complications in this population.
The law of therapeutic parsimony provides us with a framework in which to plan therapy for geriatric persons living with diabetes. It states the following: ‘minimal therapeutic interventions should be used, in place of multiple ones, as long as this can achieve equivalent therapeutic outcomes’ [236]. Being cognizant of this law, an attempt should be made to minimize the number of tablets/capsules or pill burden, and the frequency of their administration. A few means of achieving this end include prescribing rational fixed-dose combinations, which in turn, reduce the dose of individual agents. Agents with proven pleiotropic effects also could be preferred, provided adequate efficacy is assured. It should be suggested to the patient that all medications can be taken at the same time, perhaps five minutes before a meal or immediately after a meal. It also should be ensured that while prescribing brand names, strips or tablets that are similar in appearance are not prescribed. It is common for the patient to get confused between two strengths of the same medication; e.g., 1 mg and 2 mg of glimepiride tablets due to similarity in packaging.
## Glycemic Monitoring
Optimal glycemic control in elderly persons with diabetes can be achieved through certain lifestyle adaptations, adjustment, and individualization of the therapy, which invariably depends on careful self-monitoring and control. Blood glucose monitoring can be done by different methods (Table 13).
**Table 13.**
| Method | Need | Benefits (in elderly*) | Limitations (for elderly*) |
| --- | --- | --- | --- |
| Glycatedhemoglobin(A1C) | Laboratory-basedtest | • Represents the average the exposure• Represents risk of complications | • No information on daily glycaemic values• No information on daily glycaemic variability• Unable to differentiate between pre-prandial and postprandial glycemia, possible hypo and hyperglycemia.• No immediate feedback to the patient regarding the therapeutic or nutritional decision- less useful in preventing hypoglycemia*• Multiple comorbidities interfere with the values*1 |
| | Rapid A1C assay (POC testing) | • Able to differentiate be^een pre-prandial and postprandial glycemia, possible hypo and hyperglycemia• Provides immediate feedback and allows timely therapy changes, more useful in preventing hypoglycemia* | • No information on daily glycaemic values• No information on daily glycaemic variability• Medical conditions could interfere with the values |
| Self-monitoring of blood glucose (before and after meals) (SMBG) | Finger-stick Glucose monitoring Glucometer with advanced features connected to smart devices (smartphone- enhanced glucometers) | • Provides real-time data and offers information on possible glycemic excursions• Allow patients to program their insulin to-carbohydrate ratio, correction factor, and target blood glucose level into the meter.• Offers information about the glycemic fluctuations• Take immediate therapeutic2 decisions/modifications | • Patient education/training is necessary• The elderly from Group II-IV may find difficulty independently using• SMBG• Need caregiver assistance*• The overall effect of SMBG on glyce- mic control is small• Cochrane Review 2012- Did not affect health-related quality of life or patient satisfaction [239] |
| Continuous Glucose Monitoring System (CGMS) | CGMS device | • Comprehensive image of the development of the disease and the treatment offers the ability to appreciate the glycemic tendency• Take immediate therapeutic decisions/modifications• Identify hypoglycemia, particularly the nocturnal and unawareness phenomena, and therefore may prevent associated falls and CV complications*• Useful in elderly persons using insulin*• Useful in elderly persons with physical or cognitive limitations who require monitoring of blood glucose by a surrogate* | • Not entirely identical to the glycemic level, certain lag• Patient education/training is necessary |
| Time in range | CGM Metric:Time spent in an individual's target glucose range (70-180 mg/dL) | • Simple and intuitive• Gives information regarding the qualify of glucose control• Extremely useful in elderly at risk of hypoglycemia unaware ness and complications*• Strongly correlated with A1C• Linked to the risk of developing vascular complications• Each incremental 5% increase in TIR is associated with clinically significant benefits [242] | |
| Recommended level of Blood glucose | Recommended level of Blood glucose | Required time | |
| 70-180 mg/dL<70 mg/dL>250 mg/dL | 70-180 mg/dL<70 mg/dL>250 mg/dL | >50% (>12 h)<1% (<15 min)<10% (<2 h, 24 min) | |
A1C Recommendation: It should be performed twice annually for stable glycemic control and four times for unstable/need change in treatment [240].
Self-monitoring of blood glucose (SMBG): International guidelines recommend ≥ three times daily pre-prandial capillary blood glucose measurements for patients with intensive insulin therapy or insulin pump carriers; in other cases, the frequency of SMBG should be individualized depending on the treatment scheme and glycemic level control. Postprandial monitoring is recommended for cardiovascular prevention.
Continuous glucose monitoring-time in range (CGM-TIR): For older patients including high-risk ones (those with complications or co-morbidities, such as cardiovascular or kidney disease, cognitive deficits, osteoporosis, fractures, or joint disease), the TIR target is > $50\%$ [241]. For the elderly TIR, the target was lowered from > $70\%$ to > $50\%$ and reduced TBR to < $1\%$ at < 70 mg/dL (< 3.9 mmol/L) to place greater importance on reducing hypoglycemia than maintaining target glucose levels [242].
## Methods to improve adherence and reduce dosing errors
Advances in technology are being used to improve elderly care and quality of life aiming at both disease-specific and elderly-specific parameters. Table 14 and Table 15 The application of technological advances in elderly care may be broadly categorized.
Elderly persons have different barriers to adherence, the most common ones being remembering to inject themselves or injecting the correct dose or maintaining supplies. Therefore, in this patient population with diabetes, insulin pumps and continuous delivery devices certainly can facilitate improving adherence, and consequently, glycemic control [245]. Moreover, the use of an insulin delivery system is more accurate and precise than insulin administration via injections [249]. Use of insulin delivery systems also helps to reduce hypoglycemia and consequently its complications. ( Table 16)
**Table 16.**
| Technolog | Method | Application/Benefit |
| --- | --- | --- |
| Insulin Delivery Systems | Pump or CSII | • Reduce hypoglycemia• Improve Alc• Availability of bolus calculators• Smaller accurate doses• Keep track of active insulin• Downloadable reports |
| Insulin Delivery Systems | Wearable insulin delivery system | • A 24-hour patch delivers continuous stipulated basal insulin and on-demand bolus dosing by pushing a button at mealtimes• No alarm |
| Insulin Delivery Systems | Inhaled insulin for prandial use | • Helpful for improving adherence, especially in patients who need lower doses of insulin at mealtimes. |
| Insulin Delivery Systems | Bluetooth-enabled insulin pen | • Bolus calculator• For patients using multiple daily injections• Keep track of active insulin• Useful to assess adherence• Downloadable reports |
| Monitoring systems | CGM | • Alarm/alerts are available in most• Connected to smart devices like mobile phones, sharing data with caregivers is feasible• Reduce hypoglycemia• Reduce glucose variability• Improve glucose control• Reduce the need for fingersticks measurement• Downloadable reports |
| Hybrid closed-loop system | Combined glucose monitoring systems and insulin delivery systems | • A special feature that gets activated adjusts, suspends, and restarts insulin delivery based on the encoded glucose level.• Uses technolog to stop insulin delivery for up to 30 minutes to 2 hours if the glucose level reaches a preset low limit and the user doesn't react to a low-glucose alarm• Bolus prandial insulin needs to be initiated• Reduce hypoglycemia• Reduce glucose variability• Improve glucose control• Downloadable reports• Alarm/alert |
Self-monitoring of blood glucose (SMBG) is a key component of diabetes management; however, it is limited by its staticity. Unlike SMBG, Continuous glucose monitoring (CGM) provides a trend of glycemic status, and therefore, is used for pattern management and assessment of glucose excursions. Hybrid systems combine insulin delivery and monitoring systems, enhancing the convenience of use [246,249]. The patients or the caregiver must be educated on interpreting and utilizing the information obtained from monitoring devices so that it affects adherence positively.
Self-monitoring is a basis of behavioral lifestyle interventions. The incorporation of personal fitness technologies into behavioral interventions enhances physical activity, decreases sedentary behaviours, and improves glycemic control in older adults. Activity trackers may boost physical activity through the amalgamation of analytically-tested behavioral change techniques such as goal-setting, self-monitoring, social support, social comparison, feedback, and rewards. Studies have observed weight loss, social connection, and increased activity awareness in the elderly using activity trackers (Table 17 and Table 18) [247,248].
## Religious/Cultural Fasting
Many patients with diabetes may fast for religious or cultural reasons. Elderly persons often are more religious and may insist on fasting. Fasting could be either regular or intermittent for a few days in a week or continuously for an entire month. Management of intermittent fasting is more challenging because the pharmacotherapy schedule becomes more complex compared to when it is continuous. Maintaining euglycemic status is even more complex in patients using insulin therapy. The rate of complications is significantly greater in older adults than in younger ones with diabetes. Therefore, management is even more challenging for the elderly. Besides, this population is excluded in most trials, and guidance is based on expert opinions rather than clinical evidence.
The experts suggest the management of diabetes should be attuned to the patient’s lifestyle, not the opposite as a reassurance of living a normal life even with diabetes. For the elderly who wish to fast, a careful risk-benefit analysis must be performed. The results should be discussed with the person and his/her family to arrive at a shared decision.
The other aspect of fasting is dehydration in case liquids are also restricted or the patient is on diuretics or SGLT-2i or those with brittle diabetes or renally impaired. Most liquid intake depends on the type of food consumed, particularly in the elderly who depend on a liquid diet (juices and soups). These patients also should avoid fasting or can observe fasting provided extreme caution is followed along with recommended dietary and medication modifications. If the patient still insists on fasting, the use of SGLT2i must be avoided.
## Sick Day Rule
Most common illnesses in the general population can assume serious complications in people with diabetes, particularly in the elderly who have a compromised immune system. Certain illnesses like common cold or flu, upper and lower respiratory infections, urinary tract infections gastrointestinal tract infections, and skin infections are accompanied by high body temperature. These illnesses can dysregulate the plasma glucose resulting in serious complications like DKA or HHS.
Our experts agree with the ADA and IDF-Europe recommendations for the management of sick patients with diabetes. The anti-diabetic medication should be continued; in particular, insulin should not be discontinued but plasma glucose should be more frequently monitored (at least every four hours). Adequate hydration and oral caloric intake should be maintained [250,251]. Glucose lowering therapy should be adjusted according to oral caloric intake.
Fatigue, weight loss, and polyuria are indicators of hyperglycemia. Plasma glucose above 270 mg/dL, ketones in the urine, and excessive thirst are signs of DKA. Vomiting, abdominal pain, tachycardia, and reduced level of consciousness constitute medical emergencies.
## Inpatient management
In critically ill patients in the ICU as well as for most patients admitted for general illnesses or surgery in the non-ICU setting, the various guidelines recommend target glucose between 140-180 mg/dl (Table 19).
**Table 19.**
| Unnamed: 0 | ICU | Non-ICU |
| --- | --- | --- |
| ADA/AACE | Initiate insulin therapy for persistent hyperglycemia (>180 mg/dl)Treatment goal: 140 - 180 mg/dl if achievable without significant risk for hypoglycemia. | No specific guidelines.If treated with insulin, pre-meal glucose targets should generally be <140 mg/dl, with RBG levels <180 mg/dl Less stringent targets may be appropriate for people with severe comorbidities. |
| ACP | Recommends against intensive insulin therapy in surgical/medical ICUsTreatment goal 140 - 200 mg/dl, in surgical / medical ICUs | |
| Critical Care Society | >150 mg/dl should trigger insulin therapyTreatment goal: <150 mg/dl in ICU.Maintain glucose levels <180 mg/dl while avoiding hypoglycemia. | |
| Endocrine Society | | Pre-meal glucose target <140 mg/dl and RBG <180 mg/ dl. |
| Endocrine Society | | Terminal illness and/or with limited life expectancy or at high risk for hypoglycemia <180 - 200 mg/dlAdjust antidiabetic therapy when the glucose falls <100 mg/dl to avoid hypoglycemia. |
| Society of Thoracic Surgeons (Guidelines specific to adult cardiac surgery) | Continuous insulin infusion preferred over SC or intermittent intravenous boluses Treatment goal: <180 mg/dl during surgery (≤110 mg/dl in fasting and pre-meal states) | |
| Joint British Diabetes Society for Inpatient Care | | 108 - 180 mg/dl with an acceptable range of between 72 - 216 mg/dl |
Insulin, preferably delivered intravenously, remains the best alternative for maintaining glycemic status in the inpatient setting, specifically in the ICU. However, evolving data suggest that OHAs from the DPP-4i class either as monotherapy or in combination with basal insulin may be used in patients with mild to moderate hyperglycemia. In a non-ICU setting (general medicine or surgery) the recommended total daily insulin dose for most people should start between 0.15-0.3 units/kg body weight, with lower doses of 0.1-0.15 units/kg recommended for older patients with renal failure (eGFR < 60 mL/min/1.73 m2), history of hypoglycemia, or poor oral intake [252]. In addition, patients with enteral or parenteral nutrition should be monitored for glucose at 4-6-hour intervals to avoid hypo-hyperglycaemia [171].
## Summary
The panel opined that the current and expected geriatric population with type 2 diabetes in *Asia is* enormous along with a huge spectrum of dietary and disease (communicable and non-communicable) disparities unique to the region. Hence, management of diabetes in the Asian geriatric population is indeed challenging. The Best Practice Document has considered the conventional as well as unique factors when proposing recommendations for glycemic targets. Particular attention has been paid to the geriatric syndrome and management of type 2 diabetes with pharmacological and non-pharmacological interventions aligned to its existence. The use of technological aids to monitor the disease status and avoid several risks specifically in this vulnerable population has been emphasized. Modifications to diabetes management in special situations like religious fasting or hospitalization or days when they feel unwell have been addressed. The panel has been thorough in addressing aspects specific to geriatric diabetes.
## Abbreviations
2hG: 2-hour glucose AACE American Association of Clinical Endocrinology ACCORD: Action to Control Cardiovascular Risk in Diabetes AD: Alzheimer’s disease ADA: American Diabetes Association
ADVANCE: Action in diabetes and vascular disease: preterax and diamicron mr controlled evaluation AEDs: Anti-epileptic drugs AGA: American Geriatrics Association AGE: Advanced glycation end products
AWGS: Asian Working Group for Sarcopenia BBB: Blood Brain Barrier BLSA: Baltimore Longitudinal Study of Aging BMD: Bone mineral density
BMI: Body mass index CBT: Cognitive Behavioral Therapy CGM-TIR: Continuous glucose monitoring-time in range CHD: Coronary heart disease
CKD: Chronic kidney disease CVD: Cardiovascular disease DPN: Diabetic peripheral neuropathy EASD: European Association for the Study of Diabetes
ED: Emergency department ESE: European Society of Endocrinology FPG: *Fasting plasma* glucose GIP: Glucose-dependent insulinotropic polypeptide
GLP-1: Glucagon-like peptide-1 GSA: Gerontological Society of America HHS: Hyperglycemic hyperosmolar syndrome HRT: Hormone replacement therapy
ICU: Intensive care unit IDF: International Diabetes Federation ISBMR: Indian Society for Bone and Mineral Research IU: international units
LDL: Low-density lipoprotein LOH Late-onset hypogonadism MCI: Mild cognitive impairment MNA Mini Nutritional Assessment NHANES: National Health and Nutrition Examination Survey NMT: Not more than
NPI: Non-pharmacological Interventions OGTT: Oral glucose tolerance test PPBG: Post prandial blood glucose PVD: Peripheral vascular disease
QoL: Quality of life SD: Standard Deviation SGLT-2i: Sodium-glucose Cotransporter-2 Inhibitors SIADH: Syndrome of inappropriate antidiuretic hormone
SMBG: Self-monitoring of blood glucose SNRI: Serotonin and norepinephrine reuptake inhibitors SSRI: Serotonin and norepinephrine reuptake inhibitors TBR: Time below range TIMES2 Testosterone replacement In hypogonadal men with either metabolic Syndrome or type 2 diabetes
TOS: The Obesity Society TRT: Testosterone replacement therapy TZD: Thiazolidinediones USA: United States of America
USPSTF: United States Preventive Services Task Force UTI: Urinary tract infections VADT: Glycemic Control and Complications in Diabetes Mellitus Type 2 VMS: Vasomotor symptoms
WHO: World Health Organization
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---
title: Development of Diagnostic Capabilities for Complications of Bacterial Infection
in Diabetic Patients
authors:
- Samiah Hamad S Al-Mijalli
- Ashwag Y Shami
- Rasha A Al-Salem
- Nawaf M Alnafisi
journal: 'The Review of Diabetic Studies : RDS'
year: 2022
pmcid: PMC10044050
doi: 10.1900/RDS.2022.18.135
license: CC BY 4.0
---
# Development of Diagnostic Capabilities for Complications of Bacterial Infection in Diabetic Patients
## Abstract
### OBJECTIVE
Our objective was to assess the pattern of urine infections, the most common pathogen, and their susceptibility pattern to antibiotics among Saudi diabetic patients.
### METHODS
We performed a year-long cross-sectional study from January 2018 to January 2019 at KAAU Hospital in Riyadh, KSA. We cultured the urine specimens obtained from diabetic patients based on optimal aerobic and anaerobic microbiological methods. By adopting standard microbiological methods, we identified the bacterial isolates. We also followed the guidelines of the Clinical and Laborator y Standards Institute (CLSI) to do antibiotic susceptibility testing.
### RESULTS
A total of 100 isolates were evaluated, and a total of 22 organisms were isolated. The majority were multidrug-resistant organisms. Streptococcus haemolyticus was the most frequent organism and rated ($15\%$). It was followed by *Staphylococcus hominis* ($11\%$), *Pseudomonas aeruginosa* ($9\%$), *Enterococcus faecalis* ($9\%$), *Enterococcus fiseum* ($7\%$), *Escherichia coli* ($7\%$), *Staphylococcus aureus* ($7\%$), *Staphylococcus lantus* ($5\%$) and *Klebsiella pneumoniae* ($5\%$). We also found multi-microbial infections. Most of the organisms were susceptible to tigecycline, gentamycin, and nitrofurantoin, rating ($88\%$), ($84\%$) and ($78\%$), respectively.
### CONCLUSIONS
Our study revealed that a wide range of pathogens affects the diabetes patients. Staphylococcus haemolyticus is the most prevalent pathogen. We observed considerable antimicrobial resistance. Tigecycline had a wide sensitivity spectrum and was effective against most of the bacteria. Thus, it can be used as an empirical antibiotic.
## Introduction
Diabetes mellitus (DM) is a universal and pervasive health problem. In some countries, including Saudi Arabia, DM achieves epidemic proportions [1]. In 2000, it affected about 171 million people worldwide. Eleven years later, this number grew to 366 million. Furthermore, some authorities estimate that DM may affect more than 552 million by 2030 [2].
According to the World Health Organization (WHO) – Diabetes Country Profiles, 2016, the prevalence of DM is $14.7\%$ in males and $13.8\%$ in females [3]. The International Diabetes Federation (IDF) argues that there are 451 million persons worldwide (aged 18-99) with DM. There were 415 million people in 2017. It is estimated that there may be 693 million with DM by 2045.
DM affects multiple aspects of patients’ lives, including quality of life and employment. It may result in premature death. It is ranked as the fifth cause of death in developed countries [4]. Therefore, patients with DM are increasingly at risk to asymptomatic bacteriuria and pyuria, cystitis, as well as serious upper urinary tract infections (UTIs) [5,6].
Once the urine glucose level is elevated and the host immune factors are impaired, a person is predisposed to infection. A patient may encounter neutrophil dysfunction due to hyperglycemia that elevates intracellular calcium levels, interfering with actin. As a result, a patient has diapedesis and phagocytosis.
Although variations occur, most common relevant UTI-related bacteria in DM are *Escherichia coli* (E. coli), Proteus (multiple species), Klebsiella (multiple species), Pseudomonas aeruginosa, Enterococcus faecalis, Staphylococcus aureus, and coagulase-negative staphylococci [7-9]. Patients with DM are prone to mortality more than those without DM [10]. They are also at a greater risk of UTIs and complications, especially those with type 2 DM. One survey concluded that UTIs are the most common microbial infections worldwide [11] and the most prevalent among DM patients [12]. Moreover, UTIs explain a massive proportion of antibacterial drug consumption and have considerable fiscal impact [13].
UTIs indicate the presence of microbial pathogens inside the urinary tract. A UTI is commonly categorized according to the location of infection – bladder (cystitis), kidney (pyelonephritis), or urine (bacteriuria). UTIs may be either asymptomatic or symptomatic. As an infection, a UTI prevails among patients, regardless of the age group [14].
E. coli often is ranked first in terms of causing UTIs. Klebsiella and Proteus species are ranked second and are often related to stone disease. Moreover, Gram-positive bacteria, eg, Enterococcus and Staphylococcus, increase [15,16].
Despite abundant research, alterations in the phagocyte function, as well as augmented, attenuated, or unchanged cytokine responses to infection in relation to DM are not well defined. Thus, the immunological foundation of DM-intracellular bacterial infections requires further examination [17-21].
Previous studies have shown reduced functioning of diabetic polymorphonuclear cells and diabetic monocytes/macrophages, including chemotaxis, phagocytosis, killing of bacteria, as compared to cells of control individuals. Moreover, some microorganisms pose severe threats in an environment with increased glucose content. Evidence suggests that the more microorganisms adhere to DM cells, the more prevalent infections among DM patients become. In addition, serum TNF-α level in type 1 DM patients has increased considerably, regardless of age, disease duration, and ethnicity groups [22,23].
Bacteremia is a serious health concern. It indicates that viable bacteria are in the bloodstream, as shown by blood cultures. It causes mortality in up to $20\%$ of cases. Therefore, in-depth research-based knowledge is an important objective for improving patient prognosis.
Hyperglycemia is damaging. It causes dysfunction and failure of various organs. It mainly affects the eyes, kidneys, nerves, heart, and blood vessels. Hyperglycemia leads to defective phagocytosis and reduced immunity [24].
## Methods and materials
We conducted a year-long prospective study on Saudi type 2 DM patients from January 2018 to January 2019 at KAAU Hospital in Riyadh, Kingdom of Saudi Arabia. We also studied the media for bacterial growth. In short, we identified the bacteria isolates from urine by conventional biochemical testing. A total of 100 isolates were analyzed for c/s.
All colonies were grown on agar of blood culture bottles, subjected to direct Gram staining, and subcultured onto nutrient agar, blood agar, mannitol salt agar, MacConkey agar and Sabouraud’s dextrose agar. They were incubated at 37°C under aerobic and anaerobic conditions for 24-hours.
The identification of aerobic and facultative anaerobes was done according to standard microbiological methods and antibiotic sensitivity of isolated aerobic and facultative anaerobes [25,26]. The antimicrobial susceptibility was defined based on the disk diffusion method. It was interpreted using the CLSI.
We collected 100 samples during the study period. To isolate the microbial agents of UTIs, we cultured urine specimens using blood agar and MacConky agar media. Then, we incubated the media at 37°C aerobically for 24-hours. We also studied the media for bacterial growth. In short, we identified bacteria isolates from urine by conventional biochemical testing.
## 2.1 Antibiotic susceptibility testing
We conducted antimicrobial susceptibility tests on Mueller-Hinton agar utilizing disk diffusion (Kirby Bauer’s) method following the CLSI guidelines and using the following antimicrobial agents – Amikacin, Gentamicin, Ciprofloxacin, Ertapenem, Nitrofurantoin, Imipenem, Meropenem, Trimethoprim/ Sulfamethoxazole, Tigecycline, Piperacillin/ Tazobactam, Levofloxacin, Colistin, Cephalothin, Cefuroxime, Ceftriaxone, Ceftazidime, Cefoxitin, Cefepime, Aztreonam, Ampicillin and Amoxicillin rating (30 μg), (10 μg), (5 μg), (30 μg), (300 μg), (30 μg), (30 μg), (25 μg), (30 μg), (30 μg), (30 μg), (10 μg), (30 μg), (30 μg), (30 μg), (30 μg), (30 μg), (10 μg), (30 μg), respectively for all bacterial isolates.
## 2.2 Statistical analysis
We used the Statistical Package for Social Sciences (SPSS), version 21.0, for data analysis. We presented data in the form of frequencies and percentages.
## 3.1 Demographic characteristics
Table 1 illustrates the demographic characteristics of the population. A total of 100 patients with type 2 DM participated. There were $47\%$ males 53 % females, with a mean age of 37+14 years, and duration of DM 7.2+3.5 years duration of UTIs. Comorbidities among patients included hypertension ($22\%$), cardiovascular disease ($14\%$), and nephropathy ($9\%$).
**Table 1.**
| Variables | NO % |
| --- | --- |
| Age (Mean + SD) | 37+14 years |
| Gender: | 47% |
| Male | 53 % |
| Female | |
| Duration of Diabetes | |
| Duration of UTI | |
| Duration of diabetes mellitus (DM) | 7.2+3.5 years |
| Hypertension | 22% |
| Cardiovascular disease | 14% |
## 3.2 Pathogens isolated from type 2 diabetes mellitus patients
A total of 100 isolates were evaluated, and 22 organisms were isolated. The majority were multidrug-resistant organisms. Streptococcus haemolyticus was the most frequent organism in $15\%$ of cases. It was followed by *Staphylococcus hominis* ($11\%$), *Pseudomonas aeruginosa* ($9\%$), *Enterococcus faecalis* ($9\%$), *Enterococcus fiseum* ($7\%$), E.coli ($7\%$), *Staphylococcus aureus* ($7\%$), *Staphylococcus lantus* ($5\%$), and *Klebsiella pneumoniae* ($5\%$). We also found multi-microbial infections, as Table 1 shows.
## 3.3 Microbial susceptibility
The salient antibiotics tested were Roxithromycin, Clindamycin, Aztreonam, Imipenem, Meropenem, Amikacin, Gentamicin, Ciprofloxacin, Norfloxacin, Ofloxacin, Doxycycline, Minocycline, Tigecycline, Nitrofurantoin, Trimethoprim, Vancomycin, Tetracycline, Erythro, Azithromycin, Levofloxacin, Clarithromycin, Amoxicillin, Flomoxef, Benzylpenicillin, Ampicillin, Cefixime, Cefetoxime, Ceftriaxone and Methicillin.
With respect to the pathogens we found, the most effective antibiotic was Tigecycline, because $86.25\%$ of total isolates were sensitive to it, whereas just $12\%$ resisted it. It was followed by Gentamycin, because $84\%$ of isolates showed sensitivity to it. Nitrofurantoin was ranked third, to which $78\%$ of isolates were sensitive, as Figure 1 illustrates.
**Figure 1.:** *Antimicrobial sensitivity of bacterial isolates.*
## 3.4 Resistance to salient antibiotics
Antimicrobial sensitivity tests were performed on 100 bacterial isolates from Saudi patients with DM. Tigecycline was the most active agent on the 100 isolates. About $61\%$ of isolates were resistant to Clindamycin, $34.2\%$ to Emipem, $33.3\%$ to Meropenem, $32.2\%$ to Amikacin, $16.04\%$ to Gentamicin, $12\%$ to Tigecycline and $21.9\%$ to Nitrofurantoin.
## 3.5.1 Staphylococcus haemolyticus
Staphylococcus haemolyticus was the most frequent pathogen in our study. It was most sensitive to Tigecycline and Trimethoprim followed by Doxycycline and Gentamicin. Streptococcus aeruginosa was resistant to Trimethoprim, Tigecycline and Doxycycline, but it was sensitive to Gentamicin and Imipenem (Figure 2).
**Figure 2.:** *Antimicrobial resistance of Staphylococcus haemolyticus.*
## 3.5.2 Pseudomonas aeruginosa
Pseudomonas aeruginosa was sensitive to Imipenem and Gentamicin, and resistant to Tigecycline, Trimethoprim tigecycline and Doxycycline.
## 3.5.3 Klebsiella pneumoniae
Klebsiella pneumoniae was sensitive to Tigecycline, Trimethoprim, Nitrofurantoin, Doxycycline and Gentamicin.
## 3.5.4 Enterococcus faecalis
Enterococcus faecalis was sensitive to Ciprofloxacin, Nitrofurantoin and Vancomycin. It was resistant to Tetracycline, Trimethoprim and Erythromycin.
## Discussion
Identifying the local or regional causes of UTIs and antimicrobial resistance can help define empirical therapy. We observed that the frequency and features of pathogens vary according to time and region.
Because of the prevalence of these infections, proper treatment plays a considerable role in a patient’s health, development of antibiotic resistance and healthcare costs.
Resistance to commonly advised antibiotics is a global problem. Our study showed that *Streptococcus haemolyticus* was the most frequent organism, present in $15\%$ of UTIs associated with DM. However, other studies have reported that E. coli is the most frequent uro-pathogen [27,28].
The Gram-positive, β-hemolytic, chain-forming bacterium *Streptococcus haemolyticus* causes serious infection among neonates, pregnant women, and the elderly with chronic medical illness [29]. However, it was most sensitive to Tigecycline and Trimethoprim, followed by Doxycycline and Gentamicin.
The high prevalence of antimicrobial drug resistance in bacteria has become a major health concern. Therefore, new antimicrobial agents, including Tigecycline, should be tested against the widest possible range of these resistant organisms globally [30]. We illustrated in our study that *Tigecycline is* the best antimicrobial agent to which $86.25\%$ of bacterial isolates are sensitive. Our findings approach those of another study [31] where the tested Gram-positive organisms were $100\%$ susceptible to the therapeutic effects of Tigecycline.
Staphylococcus hominis was the second most frequent resistant bacterium and rated ($11\%$), followed by *Pseudomonas aeruginosa* ($9\%$), *Enterococcus faecalis* ($9\%$), *Enterococcus fiseum* ($7\%$), E.coli ($7\%$), *Staphylococcus aureus* ($7\%$), *Staphylococcus lantus* ($5\%$) and *Klebsiella pneumoniae* ($5\%$). We also found multi-microbial infections.
Regarding the pathogens we found in our study, the most effective antibiotic was Tigecycline because $86.25\%$ of total isolates were sensitive to it, and just $12\%$ resisted it. It was followed by Gentamycin because $84\%$ isolates showed sensitivity to it. Nitrofurantoin was ranked third to which $78\%$ isolates were sensitive, as Table 2 shows.
**Table 2.**
| Pathogens | Number % |
| --- | --- |
| Streptococcus haemolyticus | 15 (15%) |
| Pseudomomasaerugimosa | 9 (9%) |
| Enterococcus faecalis | 9 (9%) |
| Staphylococcushoiinis | 11 (11%) |
| Klelsiella pneumoniae | 5 (5%) |
| Staphylococcus epidermidis | 4 (4%) |
| E.coli | 7 (7%) |
| Staphylococcus aureus | 7 (7%) |
| Aeromomas hydrophila | 2 (2%) |
| Enterococcus faecium | 7 (7%) |
| Citrokcter feoseri | 3 (3%) |
| Staphylococcus auricularis | 1 (1%) |
| Pseudomonas putida | 1 (1%) |
| Staphylococcus lentus | 5 (5%) |
| Staphylococcus lugdunensis | 1 (1%) |
| Acinetokcterliffii | 3 (3%) |
## Conclusion
We found that a wide range of pathogens affect DM patients, *Staphylococcus haemolyticus* is the most prevalent. Considerable antimicrobial resistance was observed. Tigecycline had a wide sensitivity spectrum and was effective against most of the bacteria. It could be used as an empirical antibiotic. However, choosing antibiotics to treat UTIs should be controlled by the local resistance pattern.
## Abbreviations
DM: *Diabetes mellitus* WHO: World Health Organization IDF: International Diabetes Federation UTIs: Urinary tract infections
SPSS: Statistical Package for Social Sciences
## Conflicts of interest statement
The authors declare that they have no conflicts of interest.
## Author contributions
SA wrote the topic and practical. AS collected the samples. RA helped in the practical. NA wrote the paper.
## Ethical approval
The study protocol was pursuant to the declaration of Helsinki. The local ethics committee at the study center approved study procedures.
## Data availability
The data used to support the findings of this study are included in the article.
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---
title: Maslinic Acid Supplementation during the In Vitro Culture Period Ameliorates
Early Embryonic Development of Porcine Embryos by Regulating Oxidative Stress
authors:
- Ting-Ting Yang
- Jia-Jia Qi
- Bo-Xing Sun
- He-Xuan Qu
- Hua-Kai Wei
- Hao Sun
- Hao Jiang
- Jia-Bao Zhang
- Shuang Liang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10044061
doi: 10.3390/ani13061041
license: CC BY 4.0
---
# Maslinic Acid Supplementation during the In Vitro Culture Period Ameliorates Early Embryonic Development of Porcine Embryos by Regulating Oxidative Stress
## Abstract
### Simple Summary
High-quality early embryos are essential for accelerating animal reproduction and genetic modification of mammals, and oxidative stress is strongly associated with a decline in in vitro embryo developmental potential. During in vitro culture, the most direct and effective approach to alleviate oxidative stress is to add antioxidants to the in vitro culture medium. In this study, maslinic acid (MA), a pentacyclic triterpenoid acid in olive plants possessing antioxidant capacities due to its ability to scavenge free radicals, ameliorated the in vitro developmental capability of porcine embryos from parthenogenetic activation and somatic cell nuclear transfer. MA also enhanced oxidation resistance, maintained mitochondrial function, and inhibited apoptosis in porcine early-stage embryos.
### Abstract
As a pentacyclic triterpene, MA exhibits effective free radical scavenging capabilities. The purpose of this study was to explore the effects of MA on porcine early-stage embryonic development, oxidation resistance and mitochondrial function. Our results showed that 1 μM was the optimal concentration of MA, which resulted in dramatically increased blastocyst formation rates and improvement of blastocyst quality of in vitro-derived embryos from parthenogenetic activation (PA) and somatic cell nuclear transfer (SCNT). Further analysis indicated that MA supplementation not only significantly decreased the abundance of intracellular reactive oxygen species (ROS) and dramatically increased the abundance of intracellular reductive glutathione (GSH) in porcine early-stage embryos, but also clearly attenuated mitochondrial dysfunction and inhibited apoptosis. Moreover, Western blotting showed that MA supplementation upregulated OCT4 ($p \leq 0.01$), SOD1 ($p \leq 0.0001$) and CAT ($p \leq 0.05$) protein expression in porcine early-stage embryos. Collectively, our data reveal that MA supplementation exerts helpful effects on porcine early embryo development competence via regulation of oxidative stress (OS) and amelioration of mitochondrial function and that MA may be useful for increasing the in vitro production (IVP) efficiency of porcine early-stage embryos.
## 1. Introduction
Compared with in vivo-derived embryo collection, IVP of porcine early-stage embryos is a highly essential technique that supplies sufficient quantities of embryos for biotechnology applications such as cloning [1] and genetic modification [2]. Thus, the IVP of high-implantation-potential embryos is an essential step in the process of creating porcine models for biomedical research.
Embryo in vitro culture (IVC), a crucial part of the IVP procedure, is a complex system that mimics in vivo conditions [3,4]. During IVC, embryos are cultured in controlled laboratory settings with a synthetic medium. Despite substantial progress in the field of IVC optimization, in vitro-derived embryos have a lower ratio of early-stage development than in vivo-cultured embryos [5]. Long-term accumulation of ROS within embryos during the process of IVC has been established as a significant factor altering in vitro embryo quality [6]. Importantly, OS caused by an imbalance between antioxidants and ROS can negatively affect the efficiency of IVC [7]. Excessive ROS exert their pathological effects through damage to cellular organelles [8] and alterations in enzymatic function [9]. Compared with embryos of other species, porcine embryos contain large amounts of stored lipids; therefore, they are more sensitive to environmental conditions in IVC [10]. At present, antioxidant supplementation in the embryo IVC medium is an effective approach to overcome embryonic OS. Various antioxidants, such as resveratrol [11], melatonin [12,13], vitamin C [14], laminarin [15] and asiatic acid [16], have been widely used to alleviate OS by interrupting free-radical chain reactions. However, an optimized system for the effective mitigation of OS-induced embryo damage still needs to be developed.
MA (2α,3β-2,3-dihydroxylolean-12-en-28-oic acid), a plant secondary metabolite, is a triterpenoid compound principally obtained from olive tree (*Olea europaea* L.) [17]; however, it can also be extracted from Lamprothyrsus hieronymi Schum. [ 18], Tetracentron sinense Oliv. [ 19] and *Geum japonicum* [20]. MA and its derivatives exert many beneficial pharmacological effects on cells, such as anticancer [21,22], antidiabetic [23], antiviral [24], antimicrobial [25], anti-inflammatory [26,27,28] and antiplatelet aggregation [29] activities. Extensive studies have suggested that MA can scavenge free radicals as a natural antioxidant [30,31,32,33,34]. Previously, it has been shown that the Akt/Nrf2/HO-1 pathway is involved in the antioxidative activity of MA to vascular smooth muscle cells (VSMCs) [35]. Furthermore, MA-treated diabetic animals have been shown to have reduced malondialdehyde levels, which increases the activity of glutathione peroxidase (GPX) and superoxide dismutase (SOD) in renal, cardiac, and hepatic tissues [36].
Although extensive experiments have shown that MA has a strong ability to scavenge free radicals and relieves oxidative damage to cells or tissues in vivo and in vitro, the influence of MA on porcine early-stage embryos is rarely reported. Thus, we hypothesized that supplementation with the natural antioxidant MA would be able to relieve systemic OS in porcine early-stage embryos in vitro, thereby helping to improve developmental competence. This study first investigated the effects of MA supplementation on the developmental competence of porcine in vitro-cultured embryos derived from PA and SCNT. Subsequently, we further analysed the mechanism underlying the promotion of MA on developmental competence of porcine early-stage embryos.
## 2. Materials and Methods
All chemical reagents involved in the study were purchased from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise specified.
## 2.1. Porcine Oocyte Collection and In Vitro Maturation (IVM)
Porcine ovary collection was carried out at a local abattoir within 2 h of obtaining the prepubertal gilts, then delivered to the laboratory in $0.9\%$ saline at 35~37 °C. Cumulus–oocyte complexes (COCs) were collected using a 10 mL syringe aspirating method. According to the IETS grading system, COCs of grade B and above were selected under a stereomicroscope (S22-LGB, Nikon, Shanghai, China), transferred to maturation medium [37] and incubated for 42–44 h at 38.5 °C under $5\%$ CO2 in $100\%$ humidified air with mineral oil in 4-well plates (144444, Thermo Fisher, Shanghai, China). Fifty to sixty COCs were cultured in 500 μL of maturation medium per well.
After IVM, the COCs were digested with $0.1\%$ hyaluronidase to remove the surrounding expanded cumulus corona cell. Only the oocytes with homogeneous ooplasm and a polar body were subjected to subsequent experiments.
## 2.2. PA, SCNT and IVC
PA and SCNT procedures were performed according to our previous study [38]. After completing PA and SCNT, approximately 50 activated or reconstructed embryos were transferred to 500 μL porcine IVC medium with mineral oil and continuously cultured in 4-well plates at 38.5 °C, $5\%$ CO2 and saturated humidity without changing the medium for 7 days. The day on which the activated or reconstructed embryos were transferred to the IVC medium was denoted as day 0. In this experiment, the percentages of 2-cell and 4-cell embryos, formed blastocysts and hatched blastocysts to the total number of embryos observed on day 2, day 6 and day 7 were counted as cleavage rate, blastocyst formation rate, and hatching rate, respectively.
MA was added to the IVC medium at a final concentration of 1 μM, 2 μM or 5 μM.
## 2.3. Total Cell Number Assay in Blastocysts
To count the total cell numbers in blastocysts, day 6 porcine blastocysts were fixed in $4\%$ paraformaldehyde (w/v) at room temperature for 30 min. Subsequently, the blastocysts were stained with 10 μg/mL Hoechst 33342 for 10 min. Then, the stained embryos were mounted on glass slides, covered with cover slips and observed under a fluorescence microscope (Axio Vert. A1, Zeiss, Germany). The fluorescence signal intensities in each group of embryos were analysed with ImageJ software (National Institutes of Health, Bethesda, MD, USA).
## 2.4. 5-Ethynyl-2′-Deoxyuridine (EDU) Analysis
The cell proliferation of porcine blastocysts was detected using BeyoClick™ EDU with an Alexa Fluor 555 cell proliferation kit (C0075S, Beyotime, Shanghai, China) as specified in the manufacturer’s protocols. Briefly, blastocysts were incubated with 10 μM EDU at 38.5 °C and $5\%$ CO2 in air with saturated humidity for 2 h in the dark. At the end of incubation, the blastocysts were washed with PBS-PVA three times and fixed with $4\%$ paraformaldehyde for 15 min. Then, the blastocysts were permeabilized by treatment with $0.1\%$ Triton X-100 for 10 min and then washed with PBS-PVA three times. Next, the blastocysts were stained with Azide 555 solution for 30 min and 10 μg/mL Hoechst 33342 for 8 min in the dark. After washing with PBS-PVA three times, the blastocysts were mounted on glass slides and observed under a fluorescence microscope. The EDU-positive cells were analysed with NIH ImageJ software.
## 2.5. TUNEL Assay in Blastocysts
In brief, porcine blastocysts were fixed in $4\%$ paraformaldehyde for 1 h. Next, the blastocysts were permeabilized with $0.1\%$ Triton X-100 for 10 min. The blastocysts were washed with PBS-PVA three times and incubated in the dark for 1 h at 37 °C with TUNEL detection solution (MA0223, Dalian Meilun Biotechnology, Dalian, China). Subsequently, the blastocysts were stained with 10 μg/mL Hoechst 33342 for 15 min in the dark. The blastocysts were mounted on glass slides after washing three times and detected under a fluorescence microscope. The apoptotic nuclei in blastocysts were analysed with NIH ImageJ software.
## 2.6. Intracellular ROS and GSH Abundance Analysis
To determine intracellular ROS abundance, porcine 4-cell- or blastocyst-stage embryos were incubated with 10 µM 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA; S0033S, Beyotime, Shanghai, China) for 15 min. Furthermore, porcine embryos were cultured in IVC medium with or without 1 µM MA for 2 or 6 days under oxidative damage conditions (200 µM H2O2 preincubation for 30 min) to define whether MA reversed the oxidative damage in porcine embryos. To determine intracellular GSH levels, porcine 4-cell- or blastocyst-stage embryos were incubated with 10 μM 4-chloromethyl-6,8-difluoro-7-hydroxycoumarin (CMF2HC; C12881, Thermo Fisher, Shanghai, China) for 30 min. The fluorescence signals of both ROS and GSH were captured in tagged image file format (TIFF) using a digital camera connected to the fluorescence microscope, and fluorescence intensities were analysed using NIH ImageJ software.
## 2.7. Western Blot Assay
For Western blotting, embryos in each group ($$n = 80$$/per replicate) were collected and roundly lysed in lysis buffer comprising $40\%$ ddH2O, 0.5 mM Tris-HCl, $50\%$ glycerol, $10\%$ SDS, bromophenol blue and β-mercaptoethanol at 95 °C. Next, the protein samples were resolved using $12\%$ SDS-polyacrylamide gel electrophoresis (SDS–PAGE), and then transferred to polyvinylidene fluoride (PVDF) membranes. The PVDF membranes were sealed using $5\%$ BSA at room temperature for 2 h; incubated overnight at 4 °C with primary antibodies against OCT4 (1:800, WL03686, Wanleibio, Shenyang, China); CAT (1:4000, 66765-1-Ig, Proteintech, Wuhan, China), SOD1 (1:2000, 10269-1-AP, Proteintech) and GAPDH (1:10,000, 60004-1-Ig, Proteintech); and then incubated with an HRP-conjugated anti-rabbit secondary antibody (1:10,000, SA00001-2, Proteintech) or anti-mouse secondary antibody (1:8000, SA00001-1, Proteintech)for 1.5 h. After washing with 1x TBST three times, the immunoblots were visualized with ECL solution (SQ201, Epizyme, Shanghai, China) by using a Tanon 5200 Image Analyser (Tanon, Shanghai, China) and analysed with NIH ImageJ software.
## 2.8. Reverse Transcription Quantitative Polymerase Chain Reaction (RT–qPCR) Analysis
Day-6 embryos in each group ($$n = 60$$/per replicate) were extracted for total RNA using TRIzol Reagent (Takara, Japan). The total RNA was reverse transcribed into cDNA (200 ng) with a Prime Script™ RT Reagent Kit (Takara, Japan). RT–qPCR was performed using SYBR Green Real-Time PCR Master Mix (Roche, Basel, Switzerland) in a PCRmax (Eco, Staffordshire, UK). RT–qPCR was performed in a 10 μL reaction with 5 μL of SYBR Green Real-Time PCR Master Mix, 2 μL of ddH2O, 10 μmol in 0.5 μL of forward or reverse primer, and 2 μL of cDNA using the following procedure: 95 °C for 30 s, 95 °C for 5 s and 60 °C for 30 s for 45 cycles. *Target* gene expression was quantified relative to housekeeping gene (GAPDH) expression. Each RT–qPCR primer involved in the process is listed in Table S1 (Supplementary Materials).
## 2.9. Mitochondrial Membrane Potential (ΔΨm) Assay
A ΔΨm assay was carried out using the ΔΨm-sensitive fluorescent probe 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethyl-imidacarbocyanine iodide (JC-1; C2003S, Beyotime, Shanghai, China). Briefly, embryos were incubated with 2 μM JC-1 for 30 min. After washing with PBS-PVA three times, the fluorescence signals were captured using a fluorescence microscope connected to a digital camera. The ΔΨm was calculated as the ratio of J-aggregate red fluorescence (red) to J-monomer green fluorescence (green). The fluorescence intensities of each embryo were analysed using NIH ImageJ software.
## 2.10. Intracellular Adenosine 5′-Triphosphate (ATP) Level Analysis
Briefly, embryos in each group ($$n = 40$$/per replicate) were lysed with 200 μL of ATP-releasing reagent from an assay kit (S0027, Beyotime, Shanghai, China), sonicated on ice for 10 min and centrifuged at 12,000× g at 4 °C for 5 min. Next, ATP assay working solution was prepared by diluting ATP assay reagent with ATP assay diluent at a ratio of 1:4, and then 30 μL of the supernatant was pipetted into a 96-well plate with a pipette together with 100 μL of ATP working solution. Subsequently, chemiluminescence was detected using a microplate reader (Infinite M200 Pro, Tecan, Shanghai, China).
## 2.11. Statistical Analysis
For each experiment, at least three independent biological replicates were required. The data were evaluated using GraphPad 8.0.1 software (GraphPad, San Diego, CA, USA), and statistical comparisons of experiments were made using independent-sample t tests. The data are presented as the mean ± standard deviation (SD), and $p \leq 0.05$ was considered to indicate statistical significance.
## 3.1. Effect of MA Supplementation at Various Concentrations on Porcine PA Embryo Development
To screen the optimum concentration of MA, parthenogenetically activated embryos were supplemented with different concentrations of MA (control, 1, 2 and 5 μM), and the developmental performance of these embryos was analysed in vitro. The results indicated that none of the supplementation experimental groups showed any differences in cleavage rate (Figure 1B; $p \leq 0.05$). At a concentration of 1 μM, MA prominently increased the blastocyst formation rate on day 6 (Figure 1A,C; 44.97 ± $4.63\%$ vs. 36.70 ± $2.65\%$; $p \leq 0.01$) and increased the rate of blastocyst hatching on day 7 (Figure 1D; 12.31 ± $2.38\%$ vs. 8.03 ± $2.09\%$; $p \leq 0.05$) for the PA embryos compared with the embryos cultured in the non-supplemented group. Further analyses showed that 1 μM MA not only increased the total cell numbers (Figure 1E,F; $p \leq 0.05$) and cell proliferation (Figure 2A,B; $p \leq 0.05$), but also decreased apoptosis occurrence (Figure 2C,D; $p \leq 0.01$) in these embryos. Given these findings, 1 μM MA was used in all subsequent experiments.
## 3.2. Effect of MA Supplementation on the In Vitro Developmental Potential of SCNT Embryos
Further analyses of the developmental potential of SCNT embryos in vitro demonstrated that MA supplementation in IVC prominently improved porcine SCNT embryo developmental competence. Compared with the control, the addition of 1 μM MA to the culture medium did not affect the cleavage rate of SCNT embryos. ( Figure 3B; $p \leq 0.05$). Blastocyst formation rate was clearly higher in the MA-supplemented group than in the control group (Figure 3A,C; 23.15 ± $8.95\%$ vs. 15.12 ± $7.60\%$; $p \leq 0.05$). Compared with the non-supplemented group, MA supplementation obviously increased the total cell numbers of blastocysts derived from SCNT embryos (Figure 3D,E; $p \leq 0.05$).
## 3.3. Effects of MA Supplementation on the Oxidation Resistance of Porcine Early-Stage Embryos
Because MA possesses free radical-scavenging properties, we hypothesized that MA supplementation would increase the resistance of porcine early-stage embryos to OS during the IVC period. The data showed that intracellular ROS abundance in porcine PA embryos was significantly lower at the 4-cell stage (Figure 4A,C; $p \leq 0.01$) and blastocyst stage (Figure 4B,D; $p \leq 0.0001$) in the MA supplementation group than in the non-supplemented group. In addition, under oxidative damage conditions, MA supplementation significantly attenuated intracellular ROS levels in porcine parthenogenetic embryos at the 4-cell stage during IVC (Figure S1A,C; $p \leq 0.0001$) and blastocyst stage (Figure S1B,D; $p \leq 0.01$) compared with those in the H2O2-exposed group. Further analysis showed that MA supplementation during IVC increased intracellular GSH abundance in 4-cell embryos (Figure 4E,G; $p \leq 0.0001$) and blastocyst stage (Figure 4F,H; $p \leq 0.01$). Western blotting showed that the pluripotency factor octamer-binding transcription factor 4 (OCT4) and antioxidant factors superoxide dismutase 1 (SOD1) and catalase (CAT) were upregulated in the MA supplementation group compared with the non-supplemented group of porcine PA embryos at day 6 (Figure 5). Subsequent RT–qPCR analyses showed that the relative mRNA expression levels of anti-apoptotic gene B-cell lymphoma 2 (BCL2), antioxidant-related gene haem oxygenase-1 (HO-1) and cell proliferation-related gene dihydroorotate dehydrogenase (DHODH) were significantly upregulated, while that of pro-apoptotic gene BCL2-associated X protein (BAX) was significantly downregulated, in the MA supplementation group compared with the non-supplemented group of porcine PA embryos at day 6 (Figure S2). These results indicated that MA supplementation could increase the OS resistance of porcine early-stage embryos during the IVC period.
## 3.4. Effects of MA Supplementation during IVC on the Mitochondrial Function of Porcine Early-Stage Embryos
*Excess* generation of ROS is associated with mitochondrial dysfunction. Thus, the ΔΨm and intracellular ATP levels in porcine parthenogenetic embryos were analysed. The ΔΨm was assayed using the JC-1 fluorescence reaction. Quantitative analysis demonstrated that MA significantly increased the relative ratio of JC-1 fluorescence intensity (red/green) in the MA supplementation group compared with the non-supplemented group of porcine blastocysts derived from PA embryos (Figure 6A,B; $p \leq 0.01$). Furthermore, analysis indicated that MA supplementation during IVC also led to dramatic increases in intracellular ATP levels (Figure 6C; $p \leq 0.0001$). These results suggested that MA supplementation could effectively improve the mitochondrial function of porcine early-stage embryos.
## 4. Discussion
During IVC of early-stage embryos, OS often impairs embryo development rate and quality [39]. This paper has indicated evidence to show that MA, a natural antioxidant, promotes the developmental performance of PA and SCNT embryos to blastocysts during the IVC period. This beneficial effect occurred because MA effectively alleviated OS, promoted cell proliferation, reduced apoptosis levels and stabilized mitochondrial function, suggesting that MA can ameliorate the developmental competence of early-stage embryos by enhancing oxidation resistance.
Previous studies have already confirmed that MA has antioxidative effects in several other cell types, including human umbilical vein endothelial cells [27], vascular smooth muscle cells [35], human healthy peripheral blood mononuclear cells [40] and pheochromocytoma cells [31]; however, the present study is the first to evaluate the effect of MA on the in vitro development of porcine PA and SCNT embryos during the early stage in IVC with prolonged ROS accumulation. Our data showed that supplementation with 1 µM MA markedly increased the development rate and quality of porcine early-stage embryos, as demonstrated by improved blastocyst formation, hatching and total cell numbers; promoted proliferation; reduced apoptosis; and increased ΔΨm and ATP levels. Importantly, the abundance of intracellular ROS was dramatically decreased and the abundance of intracellular GSH was greatly increased in the MA group. These beneficial effects of MA on the development of early-stage embryos may involve the antioxidant and antiapoptotic properties of MA.
To further explore why MA promotes porcine early-stage embryo development, we used EDU labelling of blastocyst cells, and the results revealed that MA supplementation obviously increased the cell proliferation rate of blastocysts. MA is known to effectively exert pro-proliferative and antiapoptotic influences on somatic cells at a relatively low dose [34]. Similarly, the apoptotic cell numbers of the MA group blastocysts were clearly fewer than the numbers of the non-supplemented group in the present study. BCL2-family genes, especially BAX and BCL2, are key regulators of apoptosis, and their main sites of action are mitochondria [41]. The downregulation of the proapoptotic gene BAX and upregulation of the antiapoptotic gene BCL2 in the MA group also supported our hypothesis [42]. In addition, OCT4 plays a vital role in both cell fate decisions and cell proliferation in porcine early-stage embryos [43], while knockout of OCT4 inhibits blastocyst development [44]. Therefore, the OCT4 expression level is positively related to blastocyst pluripotency. The study also showed that OCT4 was notably upregulated in the MA addition group compared with the non-supplemented group.
Overaccumulation of ROS has adverse effects on cells through damage to cellular lipids [45], DNA and organelles [46], as well as alterations in enzymatic function [47], proliferation and apoptosis [48]. Accordi4ngly, maintaining a dynamic balance between antioxidant and ROS levels is pivotal for high embryo development. GSH, a tripeptide of γ-glutamylcysteinylglycine, is an endogenous antioxidant that contributes to both enzyme-dependent and non-enzymatic dependent against OS [49], and is an ineffective substrate for SOD1-catalysed H2O2 formation in cells and tissues [50]. A lack of GSH can cause an increase in intracellular ROS generation [51], embryonic apoptosis sensitivity [52] and expended mitochondrial damage [53], and high intracellular GSH levels can improve the developmental competence of embryos by reducing intracellular ROS levels and changing the apoptosis coefficient [54]. In this paper, MA supplementation clearly decreased ROS levels, even under oxidative damage conditions, and evidently increased GSH levels in porcine early-stage embryos, which is consistent with the results of other studies [32]. SOD1 and CAT are crucial enzymatic antioxidants for ROS scavenging [55]; SOD can resolve into a superoxide to oxygen and hydrogen peroxide, which is then converted to water and oxygen by CAT. A prior study reported that MA increases the activity of these enzymes in healthy A10 cells following treatment with H2O2 [56]. In the present study, SOD1 and CAT levels were markedly higher in the MA group than in the non-supplemented group when porcine zygotes developed to the blastocyst stage. SOD1 and CAT expression were upregulated. Additionally, it has previously been reported that MA protects VSMCs against OS through the Akt/Nrf2/HO-1 pathway activation [35], and HO-1 is one of the most critical genes regulated by Nrf2 [57]. Moreover, it is generally acknowledged that HO-1 deficiency is attributed to embryonic death, and previous studies have confirmed the role of HO-1 in embryonic survival [58]. Consequently, the mRNA expression level of HO-1 was significantly upregulated, and these results indicated that the antioxidant capacity of blastocysts was greatly improved in the MA group. Hence, MA played a positive role in protecting porcine early-stage embryos from OS by enhancing oxidation resistance.
In addition to scavenging of ROS directly, MA could also maintain mitochondrial stability [59]. Mitochondria are essential organelles during early embryonic development, and mitochondrial dysfunction is associated with failure of embryonic development [60]. ΔΨm is commonly used as an indicator of mitochondrial function and cellular viability in embryos [61]. A normal ΔΨm is necessary for mitochondrial ATP production [62] and oxidative phosphorylation [63]. When ΔΨm decreases, mitochondrial permeability increases [64], ATP synthesis decreases [65], cytochrome c is released [66], intracellular oxidative reduction is altered and BCL2 gene family members intervene to accelerate apoptosis [67]. Importantly, the release of mitochondrial membrane proteins and the dissipation of ΔΨm occur frequently in disease states with increased cell death [68]. In our study, MA-tre6ated embryos exhibited dramatically increased ΔΨm and ATP levels. In both prokaryotes and eukaryotes, DHODH is the only enzyme in pyrimidine biosynthesis located in the mitochondria rather than the cell membranes [69]; it acts on cell proliferation and has a physical connection with respiratory complexes. Loss of DHODH leads to mitochondrial dysfunction. DHODH deficiency partially inhibited mitochondrial respiratory chain complex III, decreased ΔΨm, and increased ROS production [70]. The upregulation of DHODH expression further proved that MA could improve mitochondrial function and cell proliferation in blastocysts.
## 5. Conclusions
The present context suggests that MA can enhance the developmental capacity in vitro of porcine early-stage embryos by eliminating OS, enhancing mitochondrial function, promoting proliferation and inhibiting apoptosis, thereby improving the developmental efficiency of early embryos during the IVC period. Therefore, MA is a potential candidate natural antioxidant that can be used to improve the potential of porcine early-stage embryo IVC. In the future, these findings may be verified in in vitro-produced porcine embryos and, ultimately, in in vivo studies.
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|
---
title: Effect of Antibiotic Eye Drops on the Nasal Microbiome in Healthy Subjects—A
Pilot Study
authors:
- Clemens Nadvornik
- Martin Kallab
- Nikolaus Hommer
- Andreas Schlatter
- Theresa Stengel
- Gerhard Garhöfer
- Markus Zeitlinger
- Sabine Eberl
- Ingeborg Klymiuk
- Slave Trajanoski
- Marion Nehr
- Athanasios Makristathis
- Doreen Schmidl
- Alina Nussbaumer-Proell
journal: Antibiotics
year: 2023
pmcid: PMC10044076
doi: 10.3390/antibiotics12030517
license: CC BY 4.0
---
# Effect of Antibiotic Eye Drops on the Nasal Microbiome in Healthy Subjects—A Pilot Study
## Abstract
Background: Antibiotic eye drops are frequently used in clinical practice. Due to the anatomical connection via the nasolacrimal duct, it seems possible that they have an influence on the nasal/pharyngeal microbiome. This was investigated by using two different commonly used antibiotic eye drops. Methods: 20 subjects were randomized to four groups of five subjects receiving eye drops containing gentamicin, ciprofloxacin, or, as controls, unpreserved povidone or benzalkonium chloride-preserved povidone. Nasal and pharyngeal swabs were performed before and after the instillation period. Swabs were analyzed by Illumina next-generation sequencing (NGS)-based 16S rRNA analysis. Bacterial culture was performed on solid media, and bacterial isolates were identified to the species level by MALDI-TOF MS. Species-dependent antimicrobial susceptibility testing was performed using single isolates and pools of isolates. Results: Bacterial richness in the nose increased numerically from 163 ± 30 to 243 ± 100 OTUs (gentamicin) and from 114 ± 17 to 144 ± 45 OTUs (ciprofloxacin). Phylogenetic diversity index (pd) of different bacterial strains in the nasal microbiome increased from 12.4 ± 1.0 to 16.9 ± 5.6 pd (gentamicin) and from 10.2 ± 1.4 to 11.8 ± 3.1 pd (ciprofloxacin). Unpreserved povidone eye drops resulted in minimal changes in bacterial counts. Preservative-containing povidone eye drops resulted in no change. A minor increase (1–2-fold) in the minimal inhibitory concentration (MIC) was observed in single streptococcal isolates. Conclusions: Antibiotic eye drops could affect the nasal microbiome. After an instillation period of seven days, an increase in the diversity and richness of bacterial strains in the nasal microbiome was observed.
## 1. Introduction
Bacteria, viruses, fungi, and other eukaryotic organisms are all part of the human microbiome, and as an ecosystem it is essential for maintaining homeostasis in tissues and organs including the skin, stomach, urogenital, oropharyngeal, and respiratory tracts [1,2,3,4,5,6].
Generally, the human or especially the nasopharyngeal microbiome received increasing attention over the last years, due to a variety of hypotheses linking dysbiosis to health and disease, as will be discussed in more detail in the following paragraphs. Furthermore, the nasopharyngeal area is colonized by commensal and pathogenic bacteria as well. Most dominating representatives of genera of the nasal microbiome of humans are Staphylococcus, Streptococcus, Bifidobacterium, Corynebacterium, Dolosigranulum, and Moraxella. Many pathogenic species, including, for example, Streptococcus pneumonia, Haemophilus influenza, and Staphylococcus aureus, also occur in healthy individuals, but mainly asymptomatically [7,8,9,10].
It has been found that the nasal microbiome is involved in several diseases of the upper respiratory tract, and it also seems to play a role in conditions of organs in anatomical proximity such as age-related macular degeneration or neurodegenerative diseases [11,12,13].
Studies have shown that patients with chronic rhinosinusitis with nasal polyps have significantly lower bacterial richness compared with patients without polyps [14,15]. Especially at a young age, dysbiosis of the microbiome may contribute to development of allergies, and patients with asthma have been found to have a different composition of the bacterial microbiome compared to healthy subjects [16,17]. Whether the existing imbalance of the microbiome is the consequence or the cause of diseases is still debatable. Nevertheless, the microbiome might play an important role in our health.
In clinical practice, ophthalmic topical antibiotics are often prescribed to treat bacterial infections and for prophylaxis after injuries and surgeries. Gentamicin and ciprofloxacin are the most commonly used substances [18]. Due to the anatomical proximity and direct connection via the nasolacrimal duct and the fact that up to $80\%$ of ocular-applied medications diffuse into the systemic circulation via the highly vascularized nasopharyngeal mucosa, it seems possible that ophthalmic antibiotic therapy affects the nasopharyngeal microbiome. However, currently only few data on the effect of antibiotic eye drops on the nasal microbiome can be found in the literature [19].
In the present pilot study, we investigated the effect of one-week treatment with two different antibiotic eye drops on the nasal and the pharyngeal bacterial microbiome in healthy subjects. Even though the microbiome involves more species than only bacteria (e.g., fungi), we focused especially on the bacterial microbiome, as antibiotic eyedrops have been used in this study.
Here, the antibiotic formulations contained benzalkonium chloride (BAC) as a preservative (with potential additional antimicrobial effect). Thus, we decided to use two different topical lubricants as control, povidone eye drops with and without BAC, since also BAC could have an effect or possible influence on the microbiome as shown in other studies [20]. The purpose of this pilot study, therefore, was also to investigate whether BAC alone has an effect on the nasal or pharyngeal microbiome.
The primary objective of this work was to provide a first insight into this topic and gain preliminary results by conducting a pilot study, which should further serve as an outlook for subsequent studies including a higher number of healthy volunteers.
In this study, next-generation sequencing was applied. This method has the advantage to provide information on a broad variety of the bacterial microbiome deep down to the phylogenetic level, rather than merely on pre-selected species as is the case for culture-based technologies [21,22,23,24].
## 2. Materials and Methods
The study protocol was reviewed by the Ethics Committee of the Medical University of Vienna and approved by the relevant authorities. It was conducted in compliance with the guidelines of the International Council for Harmonization–Good Clinical Practice (ICH-GCP) and the Declaration of Helsinki. Written informed consent was obtained from all study participants prior to participation.
Subjects: Only healthy subjects with normal ophthalmic findings were included into the randomized, single-masked, controlled parallel group pilot study. The exact inclusion parameters for the study were: men and women aged between 18 and 45 years, normal results of the ophthalmic examination, and no use of topical eye or nasal drops in the last three months. All subjects had to pass a screening examination four weeks up to one day before the first study day. Screening included medical history, an ophthalmic examination, and a pregnancy test in women with childbearing potential.
Subjects were excluded if they fulfilled one or more of the following criteria: regular use of medication, abuse of alcoholic beverages or drugs, participation in a clinical trial in the three weeks preceding the study, treatment with any drug (except intake of hormonal contraceptives) in the previous three weeks, treatment with topical or systemic antibiotics within eight weeks before inclusion, symptoms of a clinically relevant illness in the three weeks before the first study day, history or presence of gastrointestinal, liver, or kidney disease or other conditions known to interfere with distribution, metabolism or excretion of the study drugs, known hypersensitivity to any of the components of the study drugs, pregnancy or breast-feeding, and no effective contraception in women of childbearing potential. Care was taken to ensure that no medications or vaccinations were taken by the subjects during study participation.
Study groups and Eye drops: The subjects were randomized to receive either Gentamicin (Gentax eye drops, Agepha Pharma, Senec, Slowakia), Ciprofloxacin (Ciloxan eye drops, ALCON Ophthalmika GmbH, Wien, Austria), Povidone-unpreserved (Protagent SDU eye drops, ALCON Ophthalmika GmbH, Wien, Austria), or Povidone-preserved (Oculotect fluid eye drops, Thea Pharma GmbH, Annonay, France) in a 1:1:1:1 ratio.
Every healthy subject in each group had to administer four drops daily for 7 ± 1 days with at least two hours between instillations.
First a pilot study with 20 subjects was performed, which is described within this manuscript. Based on the results of this pilot study, the control for the main part of the study will be chosen, depending on the effect on the bacterial microbiome. The results of the pilot study should provide useful data to adjust the sample size for the main study part.
Description of Study Protocol: For the included healthy subjects, two study days (Visit 2 and 3) were scheduled. On the first study day (Visit 2), the following measurements were performed: assessment of adverse events, pregnancy test in women with childbearing potential, nasal swabs, pharyngeal swabs, and an ophthalmic examination. On study day 2 (Visit 3), 7(±1) days later, all the measurements and assessments were performed as described for study day 1.
Sample collection techniques: Nasal and pharyngeal swabs: At each study visit, three nasal swabs were taken from the right nostril and three from the pharynx by using the “BD™ culture swab” (BD Diagnostics, Heidelberg, Germany), and samples that were used for microbiome analysis were stored at −80 °C until further processing. Samples for microbiological analysis and antibiotic susceptibility testing were immediately transferred to the microbiological laboratory and were processed promptly as described below.
Nasal and pharyngeal microbiome analysis: Samples stored at −80 °C were sent to the Core Facility Molecular Biology of the Medical University of Graz. There, further analysis took place according to published protocols [25]. Briefly, total DNA isolation was performed with the MagNA Pure LC DNA III Isolation Kit (Bacteria, Fungi) (Roche, Mannheim, Germany) in a MagNA Pure LC 2.0 instrument according to the manufacturer’s instructions. Then, 500 µL MagNA Pure Bacteria Lysis Buffer (Roche, Mannheim, Germany) was added to the samples and incubated according to the manufacturer’s instructions. For mechanical lysis, samples were bead beaten in MagNA Lyser Green Bead tubes (Roche, Mannheim, Germany) at 6500 rpm for 30 s two times in a MagNA Lyser instrument (Roche, Mannheim, Germany). Nucleic acids were eluted in 50 µL according to the manufacturer’s instructions, and samples were stored at −20 °C until PCR analysis [26,27,28].
16S targeted PCR amplification was performed in triplicate using FastStart High Fidelity PCR system (Roche, Mannheim, Germany), amplifying the hypervariable region V4 with the primers 515F (GTGYCAGCMGCCGCGGTAA) and 926R (CCGYCAATTYMTTTRAGTTT). PCR amplification was performed for 30 cycles according to published procedures (Klymiuk et al. 2022), and triplicates were pooled, indexed, and purified. Then, the library was sequenced on an Illumina MiSeq desktop sequencer (Illumina, Eindhoven, Netherlands) with v3 chemistry for 2× 300 cycles. FastQ files were used for data analysis.
Microbiological MALDI-TOF analysis and antibiotic susceptibility testing: Swabs were inoculated and incubated on blood agar plates at 37 degrees Celsius under aerobic conditions, and further bacterial strains were determined by MALDI-TOF. Matching isolates were grouped to pairs (before and after treatment) if present. Minimal Inhibitory Concentration (MIC) testing with broth-microdilution against gentamicin and ciprofloxacin was performed for each isolate of *Staphylococcus epidermidis* before and after treatment. Susceptibility testing of Streptococcus spp. for both above-mentioned antibiotics was performed by E-Tests for each paired isolate and in a pool (all isolates within one visit from each the nose and the pharynx) have been conducted. Additionally, to screen for other potential antibiotic resistances, antibiograms were evaluated. The obtained bacterial isolates were identified by microbiological analysis (MALDI-TOF), and antibiotic susceptibility testing was performed regarding the EUCAST guidelines (https://www.eucast.org/ast_of_bacteria/, accessed on 11 January 2022).
NGS data analysis: FastQ raw data from Illumina Sequencing were used for data analysis. Quantitative Insights into Microbial Ecology (QIIME2 Version 2018.4), a bioinformatic pipeline integrated in the open-source web-based platform Galaxy (https://galaxyproject.org/, accessed on 11 January 2022) hosted on the MedBioNode HPC cluster of the Medical University Graz, Austria (https://galaxy.medunigraz.at/, accessed on 11 January 2022), was used to analyze the final sequence files. Shortly, paired-end raw forward and reverse reads were quality-filtered, de-noised, de-replicated, merged, and checked for chimeras using DADA2 denoise pipeline with optimized parameters: p-trunc-len-f: 260, p-trunc-len-r: 220, p-trim-left-f: 19, p-trim-left-r: 20 and p-max-ee: 2.0. The outcome of DADA2 was further used for alpha and beta diversity analysis in QIIME2. Downstream statistical analysis was performed in R version 4.0.2 (R Core Team, 2020) accompanied with the packages vegan, ggplot2, and psych. Paired-end FASTQ raw sequence reads were uploaded to the NCBI sequence read archive (SRA) and can be accessed under the BioProject ID PRJNA851930.5.
## 3. Results
A total of 20 healthy subjects were included into the study. Mean age was 27 ± 6 years, 16 subjects were female and 4 were male. Subjects were evenly distributed between the study groups regarding age and sex (Table 1).
From the 4,443,738 raw reads, 3,486,684 reads were used for further analysis after quality filtering and trimming. For Alpha diversity calculations, two different parameters were obtained from the data, “bacterial richness” and the “phylogenetic diversity index” (pd) in the nasal and pharyngeal microbiome. To describe the bacterial richness, bacteria were categorized based on their sequence similarity, and the number of microorganisms in the microbiome were expressed in operational taxonomic units (OTUs). The phylogenetic diversity index is described by a quantitative measurement of the number of nodes in a phylogenetic tree that indicates how many different species are present in the data set.
Sequencing results provided information about the diversity and richness of the different sample sites. In the gentamicin group (group 1), bacterial richness in the nose increased numerically from 163 ± 30 to 243 ± 100 OTUs and in the ciprofloxacin group (group 2) from 114 ± 17 to 144 ± 45 OTUs. Unpreserved povidone eye drops resulted in minimal numerical changes in bacterial counts (from 177 ± 41 to 186 ± 63 OTUs). Preservative-containing povidone eye drops resulted in no change (148 ± 50 vs. 148 ± 39 OTUs). A schematic representation of these results is shown in Figure 1.
In both antibiotic groups, the sequence analysis demonstrated that after 7 ± 1 days’ instillation, the phylogenetic diversity index (pd) of different bacterial strains in the nasal microbiome increased (12.4 ± 1.0 to 16.9 ± 5.6 pd in the gentamicin group; 10.2 ± 1.4 to 11.8 ± 3.1 pd in the ciprofloxacin group). No relevant difference in the phylogenetic diversity index could be observed in the two povidone groups. A schematic representation of these results is shown in Figure 2.
In contrast to the nasal microbiome, none of the administered eye drops had a relevant effect on the pharyngeal microbiome (data resp. figures on the pharyngeal microbiome are provided in the Supplement Figures S1–S4).
The most common species detected by MALDI-TOF were Streptococcus spp. and Staphylococcus spp., among others. In half the cases ($50\%$), the MIC values of Staphylococcus spp. were ≥0.125 mg/L for gentamycin which indicate resistant strains, only certain isolates showed elevated MICs (1–2-fold) after treatment compared to baseline (~$11\%$) within both antibiotic groups. Similar results were seen for Streptococcus spp. with E-tests of single and pooled isolates, as MICs of both antibiotics tended to be elevated after treatment (~$18\%$ of the isolates, microbiological). Nevertheless, these MIC elevations (1–2 fold) are within the uncertainty of MIC testing, indicating no antibiotic resistance development.
An excerpt of our results regarding the change in relative abundance of the major bacterial genera in the antibiotic groups in the nasal bacterium is shown in Figure 3: Staphylococcus, and Figure 4: Streptococcus. Here, the change in relative abundance is germ-specific—for example, decreasing for staphylococci after gentamicin treatment and increasing for ciprofloxacin. For streptococci, the results showed an increase in relative abundance after antibiotic administration. Figures about the change in relative abundance (in %) of Staphylococcus (Figure 3) and Streptococcus (Figure 4) in the pharyngeal bacterium are provided in the supplement.
## 4. Discussion
In healthy subjects, we discovered that use of antibiotic eye drops for one week had an influence on the nasal microbiome. This is of relevance due to the fact that various illnesses, especially respiratory diseases, including allergic or chronic rhinosinusitis with nasal polyps, asthma, bronchitis, or influenza have been linked to changes in the nasal microbiome [29]. Studies detected correlations between the alteration of the nasal microbiome and the occurrence of respiratory diseases, hypothesizing that a dysbiosis of the nasal microbiome may promote the onset of these diseases. Even the pathogenesis of neovascular AMD has been linked to nasal microbiome alterations. An explanation for these findings could be the anatomical proximity of the mentioned organs, which allows proinflammatory pathogens to easily spread among them and trigger chronic inflammatory diseases [11,12,13].
After antibiotic therapy, usually a numerical decrease in bacterial counts would be expected. Remarkably, the opposite was observed in the current study. A possible explanation could be that the concentration of antibiotics reaching the nose was too low. Even though studies have shown that subtherapeutic concentration or low antibiotic levels might favor development of resistance [30], this was not the case within our study. It is known that orally administered low-dose antibiotics impair the gut microbiome, which promotes the growth of pathogenic strains [31].
Antibiotic resistance was evaluated only by repeated antibiotic susceptibility testing which relies on a phenotypically evaluation. Thus, we would like to add here that antibiotic eyedrops did not lead to an increase in phenotypic resistance development (in the obtained bacterial isolates in the nose or pharynx when administered daily for 7 ± 1 days) but would like to emphasize that we did not check antibiotic resistance on molecular basis. Therefore, to evaluate if antibiotic resistance has occurred in form of resistance genes analysis on molecular basis has to be done.
In contrast to the nasal microbiome, the nearly non-existent effect of antibiotic eye drops on the pharyngeal microbiome can be caused by the greater anatomical distance. Probably the eye drops no longer or hardly reach the pharynx.
Previous studies showed that BAC has an antibacterial effect on the nasal microbiome [32]. We, therefore, expected a similar effect in this study and included two control groups that either received eye drops with or without BAC. Contrary to previous data, we observed no difference in bacterial counts in these two groups.
Since both antibiotic eye drops contained BAC as a preservative and we selected one control with BAC and one without, we can assume that this effect probably originated from the antibiotic.
Other studies have shown that antibiotic eye drops (fluoroquinolones) have an effect on the distribution of bacteria in the microbiome of the nose and conjunctiva [33]. In addition, the occurrence of resistance to the applied antibiotics’ used has already been addressed in some studies [34,35]. These studies also used a culture-based method to analyze the microorganisms. Nevertheless, with these analyzing methods, it is possible to detect only a few specific species of bacteria, highlighting the importance of the subsequent microbiome analysis in our study.
In the present study, a relatively new sequencing method, “next generation sequencing” (NGS) was used, which can obtain more accurate information about the bacteria, the composition, and structure of the nasal/pharyngeal microbiome by identifying the entire bacterial microbiome at once. In culture-based systems, the intended species has to be determined in advance. NGS, therefore, saves several analysis steps, while providing all relevant information about the present microorganisms [24,33,36,37,38,39].
Several other groups used the advantage of NGS technology to analyze microbiota, e.g., to analyze changes of the ocular surface microbiome after contact lens wear, diabetes, or dry eye [39]. Furthermore, NGS was also used to investigate changes in the oral microbiome after periodontal interventions [40].
However, it has to be kept in mind that the present study only included five subjects per group and only had a pilot character. Moreover, it must be said (despite very similar microbiota) that interindividual variations of microbiota are not decisive here; rather, the total value of change has significance. Another limitation that should be taken into consideration is the fact that no measurements of the concentration of the antimicrobial agent have been performed at the target sites, due to technical feasibility.
Another important point is the aspect of pneumococcal vaccination and the influence on possible change in the composition of the nasopharyngeal microbiome. These vaccines, but also other external interventions, such as the use of antibiotics, can lead to dysbiosis of the nasopharyngeal microbiome and are associated with a disbalance in the ratio between commensal and potentially pathogenic bacteria. The impact of vaccination on the microbiome is still poorly understood [41,42].
According to a study examining the effect of 10-valent pneumococcal conjugate (PCV10) vaccination on the nasopharyngeal microbiome, analysis found that bacterial composition was similar between the unvaccinated and vaccinated subjects. In addition, bacterial diversity did not differ between the vaccinated and non-vaccinated subjects. The study showed that the PCV10 vaccine works without significantly altering the nasopharyngeal microbiome. However, a higher abundance was observed in patients after PCV-10 vaccination. *The* genera *Streptococcus and* Haemophilus were increased in the vaccinated group while Moraxella was decreased (not statistically significant) [42].
In principle, we can primarily disregard this aspect tangentially since we assume the prevailing microbiome of the subjects as the initial value and the potential change was of interest. The primary composition of the microbiome was not the main focus of this study; thus, theoretically, a possible childhood vaccination can be hypothetically ignored, since we explicitly looked at the change from baseline to the state after a one-week antibiotic eye drops instillation period.
In order to confirm the present results, future studies with a larger sample size and a longer follow-up period are needed. These studies may also answer whether the observed changes in the microbiome are transient, and if yes, how long they last. For this purpose, this pilot study with 20 subjects was carried out, which is described in the manuscript. The control and the follow-up period for the subsequent main part of the study will be selected on the basis of the results collected. These results also provide data to generate calculations regarding the sample size of the upcoming study.
Based on the results, we have discovered that antibiotic eye-drops might have an effect on the nasal microbiome. This is the first study demonstrating that topical treatment with an antibiotic agent can unintentionally impact bacterial microbiome in anatomical proximity places. In contrast, none of the administered eye drops had a relevant effect on the pharyngeal microbiome.
To conclude, antibiotic eye drops led to an increase in the diversity and richness of bacterial strains in the nose when administered daily for 7 ± 1 days. In order to confirm these results, larger studies with a longer observation period are needed.
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|
---
title: 'Microbiological Retention on PTFE versus Silk Suture: A Quantitative Pilot
Study in Third Molar Surgery'
authors:
- Stefano Parrini
- Alessandro Bovicelli
- Glauco Chisci
journal: Antibiotics
year: 2023
pmcid: PMC10044079
doi: 10.3390/antibiotics12030562
license: CC BY 4.0
---
# Microbiological Retention on PTFE versus Silk Suture: A Quantitative Pilot Study in Third Molar Surgery
## Abstract
Background: Mandibular third molar (M3M) removal and management of postoperative complications represent a common matter of interest in oral and maxillofacial surgery. This potential quantitative study assessed the ability of two types of surgical sutures, Silk and polytetrafluoroethylene polymer (PTFE), to carry aerobic and anaerobic bacteria on wounds after mandibular third molar surgery, with a collection of the stitches at the suture removal and study in the laboratory on the basis of colony-forming units. Methods: This prospective quantitative study sampled a total of 10 consecutive healthy patients for mandibular third molar surgery at the Oral Surgery School, Dentistry and Dental Prosthodontics, Department of Medical Biotechnologies, University of Siena, Siena, Italy. The mean age of the patients was 31 years (range 25–40 years), seven patients were male and three patients were female. Inclusion criteria were: presence of a partially impacted mandibular third molar. Exclusion criteria were: smoking and diabetes mellitus. Extraction of the mandibular third molar was performed under local anesthesia: after the third molar surgery, two sutures were applied on the surgical site distally to the second mandibular molar: one single $\frac{3}{0}$ silk stitch; one single $\frac{3}{0}$ PTFE stitch. No sutures were applied on the release incision. Sutures were removed after 7 days and were immediately conserved and sent to the laboratory to be rated on the basis of colony-forming units (CFUs). CFUs were evaluated and reported on GraphPad Prism and transformed into its base 10 logarithm. Data were analyzed with a non-parametric Wilcoxon test, and p-values < 0.05 were evaluated as statistically significant. Results: All the patients attended the suture removal date, and all the sutures were present in the site. None of the surgical sites presented dehiscence. No stitch loss was reported, and no patient reported mouth washing or tooth brushing in the surgery site. All interventions were uneventful and no major complications were reported after M3M surgery. Bacterial retention resulted as statistically greater in silk sutures rather than PTFE sutures, both in Brain Heart Infusion samples ($$p \leq 0.003$$) and Wilkins-Chalgren anaerobe samples ($$p \leq 0.002$$). Conclusions: We found the PTFE suture to be superior to the silk suture in a reduction in the bacterial biofilm in both aerobic and anaerobic evaluations after M3M surgery.
## 1. Introduction
Third molar removal is one of the most common interventions in oral and maxillofacial surgery current practice [1]. Hemostasis is vital for preventing blood clot loss and promoting healing after surgery, for both outpatients and hospitalized patients.
The extraction of mandibular third molars (M3M) leaves a decent size socket, which may be hard to heal via primary intention, due to the presence of wide space distal to the second molar, which can facilitate dehiscence and food impact. This highlights the importance of primary closure after M3M extraction. For this reason, hemostasis, represented by suture application, represents a key point following the extraction of the third molars, both for very complex extractions and easy flapless extractions.
Recently, in mandibular third molar surgery (M3M), the use of cyanoacrylate has been introduced as an alternative to the suture to perform a correct hemostasis, with interesting results [2]; however, the suture after M3M extraction, today, represents the most common procedure to achieve the hemostasis required for wound recovery, and this procedure is surely the most known and documented technique [3]. A variety of sutures have been used in dental extractions: silk has been the most common suture material in M3M surgery, and the main criticism it has received is regarding the bacterial biofilm formation over it [4]. With regards to suture technique in M3M surgery, international literature provides sutureless techniques, single suture techniques, different knotless suture techniques and the use of sutureless release incision techniques: each of these reported small advantages in terms of postoperative symptoms, time spent for suture and time saved after surgery [5,6,7,8,9]. Drainage inside the wound after M3M extraction and its retention until suture removal may have a role to reduce postoperative complications, too [10]. Among the complications that may appear after M3M surgery, alveolar osteitis and infection represent the most common and most feared adverse events [3]. Infection after M3M surgery may be due to many different causes, such as food infection and/or possible inflammation, caused by bacterial biofilm retention on the suture; all the suture materials can host or harbor bacterial biofilm formation to a variable degree.
The infection that can appear following a surgical extraction of a mandibular third molar may derive from the food impact that accumulates inside the wound or from the food that accumulates around the sutures. In fact, the type of suture can influence the amount of permitted food impact and/or bacterial plaque on the stitches. In the case of sutures that remain in the oral cavity for a prolonged time, the type of material also influences the entity of plaque that accumulates. Further, the use of the first intention suture technique or the second intention suture technique may have a role on food impact in the alveolus of the extracted third molar [11].
The oral biofilm formation on the sutures depends on the nature, type and material of the suture [12,13]. Asher et al., in their interesting randomized study, underlined this aspect, suggesting that the type of oral surgery did not significantly influence bacterial accumulation; also, periodontal diagnosis had little impact on bacterial counts. Interestingly, antibiotic administration after surgical treatment also had only minor effects on bacterial accumulation on the various sutures [14]. In fact, in this paper, the type of suture was superior to other aspects of surgery: the monofilamentous nylon sutures showed less microbial accumulation than the other tested materials that were all braided [14]. With regard to possible benefits of coated sutures versus non-coated sutures, Klaus et al. reported no advantages with the use of Triclosan-coated sutures [15]. Nadafpour et al. too underlined, in their research, regarding sutures in oral implantology, the superiority of nylon compared with others sutures [16].
Polytetrafluoroethylene (PTFE) is a material introduced in oral surgery with interesting biomechanical characteristics, such as smooth surface and no plastic memory [17]. Among the plausible characteristics of this material, there is a presumed possibility of reduced plaque formation and, therefore, reduced inflammation in the tissues around the extraction socket [17]. This benefit could be useful in operations, such as the extraction of the mandibular third molar, in which the operative site is characterized by a stagnation of saliva, food and the presence of plaque could easily evoke inflammation and infection of the pericoronal tissues during recovery.
For this reason, in this study, the authors hypothesized a possible advantage of PTFE versus a common old suture material, silk, in terms of bacterial retention to the stitches in partially impacted mandibular third molar surgery. The objective of this study is to evaluate the ability of two types of surgical sutures, silk and PTFE, to carry aerobic and anaerobic bacteria on wounds after mandibular third molar surgery with a collection of the stitches at the suture removal and a laboratory study on the basis of colony-forming units.
## 2.1. Patients
This is a prospective quantitative study of subjects scheduled for outpatient M3M surgery at the Oral Surgery School, Dentistry and Dental Prosthodontics, Department of Medical Biotechnologies, University of Siena, Siena, Italy. Inclusion criteria were: the IIB classification of Pell and Gregory, partially impacted mesio-angulated mandibular third molar, confirmed with a preoperative panoramic radiograph [18]. Exclusion criteria were: smoker patients and diagnosed diabetes mellitus.
Between September 2019 and December 2019, 10 patients with partially impacted mandibular third molar, mean age 31 years old (range 25–40 years), were included in this study: 7 patients were male and 3 patients were female. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board. Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the patients to publish this paper. For this study, the following sutures were used on each patient: $\frac{3}{0}$ $100\%$ polytetrafluoroethylene polymer (PTFE) completely non-absorbable suture, white in color (DeOre, Negrar, Italy); $\frac{3}{0}$ braided silk non-absorbable suture composed of an organic protein, black in color (Johnson & Johnson Medical N.V., Machelen, Belgium).
All patients received oral hygiene performed by a professional oral hygienist 10 days before surgery and were educated on the correct home oral hygiene. All interventions and suture placements were performed by the same surgeon with more than 20 years of experience in oral surgery (S.P.): after local anesthesia with articaine 1:100.000, an incision was performed distally to the second mandibular molar with vestibular release incision. Further, a full-thickness mucoperiosteal flap was elevated; osteotomy around the mandibular third molar crown was then performed with bur, the tooth was dislocated with a straight lever and it was then completely removed with pliers. Accurate alveolar revision was then performed, and a saline lavage was performed inside the alveolus. After the tooth removal, two sutures with different materials were applied on the surgical site in order to obtain a first intention closure: one single stitch with $\frac{3}{0}$ silk; one single stitch with $\frac{3}{0}$ PTFE (Figure 1). Three knots per single stitch were used for both sutures: the order of the sutures was the same for all the patients involved in the study, with the PTFE placed distal to mandibular second molar and silk suture placed distal to PTFE suture.
Patients were educated to not brush on the surgical site and to avoid rinses with mouthwashes for seven days. The two sutures were removed after 7 days and were immediately inserted in two different 1 mL PBS/glycerol $10\%$ solutions in sterile test tubes and sent to the laboratory for analysis. Postoperative drug administration for pain and swelling relief provided ibuprofen 600 mg every 12 h for all the patients.
## 2.2. Determination of Colony Forming Units
Laboratory procedures were performed by experienced staff, and two experienced lab technicians were involved in the calculation of CFUs. A blinding method was used for the laboratory transmission data: each sample was related with a number and no data of the nature of the suture was sent to the laboratory. For the data report from the laboratory, the information of the number was related to the nature of the suture. All the samples received sonic treatment for 10 s (Transsonic T460 Elma, Singen, Germany) to remove biofilm from the sutures and frozen at −80 °C. Adequate dilutions of every sample were prepared on plates for Brain Heart Infusion (BHI, Oxoid) agar and Wilkins-Chalgren anaerobe agar (WC, Oxoid) (Figure 2). WC plates were incubated at 36°C ± 1 °C in ambient air, while BHI plates were anaerobically incubated with Genbag (Biomerieux). Plates were evaluated every day for 72 h and colony forming units (CFUs) were calculated.
## 2.3. Statistical Analysis
CFUs were evaluated and reported on GraphPad Prism and transformed into its base 10 logarithm. Data were analyzed with a non-parametric Wilcoxon test, and p-values < 0.05 were evaluated as statistically significant.
## 3. Results
In total, 10 patients were operated on for M3M surgery. All the patients presented at the suture removal date, and all the sutures were present in the site after 7 days. None of the surgical sites presented dehiscence. No stitch loss was reported, and no patient reported mouth washing or tooth brushing on the site of the surgery. All interventions were uneventful and no major complications were reported after M3M surgery. Microbiological quantitative data are reported in Table 1.
Bacterial retention resulted as statistically greater in silk sutures rather than PTFE sutures, both in BHI samples ($$p \leq 0.003$$) and WC samples ($$p \leq 0.002$$). Logarithm graphs are reported in Figure 3.
On the basis of the laboratory results, a significantly greater accumulation of bacteria on the silk suture was reported on both samples (WC and BHI) compared to the PTFE suture, pointing out the superiority of PTFE in terms of bacteria retention 7 days after third molar surgery.
## 4. Discussion
Suture after third molar extraction is a great matter of interest in oral and maxillofacial surgery. The choice of suture technique after M3M surgery represents a topic that has great divergence among surgeons; in fact, this matter has been discussed many times in the literature: primary and secondary closure represents a challenging matter for many surgeons in order to influence the wound recovery and to obtain a better surgical outcome. While primary closure after M3M surgery appears to be more appealing for surgeons in order to protect the wound and the blood clot, to reduce the food impact in the wound and in order to reduce alveolar osteitis, secondary closure after M3M extraction appears to be more effective in reducing postoperative pain, facial swelling and trismus [19]; on the other hand, the proper role of suture and possible advantages of sutureless techniques after M3M surgery have been discussed too, reporting many controversial results [11,20]. The presence of suture after M3M surgery represents a vehicle for bacterial adhesion that elicits an inflammatory reaction [21]. This may be due to the needle injury, presence of suture around the alveolar pocket and/or the presence of bacterial plaque over the stitches. The development and presence of plaque and the possible development of inflammatory complications after M3M surgery may be annoying for both patients and clinicians [22,23]. In order to reduce postoperative infections, the use of local antibiotics inside the third molar socket at the end of the surgery has also been reported [24].
This current research in ten consecutive patients stated that the material of the suture plays a role in the bacterial retention as well as the formation of biofilm at the time of suture removal. The superior role of suture material over surgery and patient morbidity (periodontal disease) was reported in research from Asher et al. [ 14].
In this study, we used a first intention closure technique distal to the mandibular second molar in order to reduce the food impact in the surgical site; we hypothesized that this may somehow reduce the plaque accumulation variables that may occur in the case of different meals.
Many previous studies suggested a role of biofilm formation and bacterial adhesion in postoperative inflammatory complications. This eventuality is influenced by the physical, chemical and mechanical conformation of the suture materials [25]. Silk suture has been probably the most common material used in third molar surgery. Silk’s main advantages are physical resistance, maneuverability and low cost [26]. In M3M surgery, the physical advantages of silk are usually appreciated by both the surgeon and the patient; recovery after third molar surgery in the mandible is commonly influenced by chewing muscle activity, with significant stress on the suture around the alveolar pocket. In this study, we compared the bacterial growth on silk stitches versus PTFE stitches after mandibular third molar surgery. PTFE is a nonabsorbable material characterized by high biocompatibility, which, for many years, has been used for the production of vascular implants, heart valves and membranes for guided bone regeneration. Recently, expanded PTFE was introduced as a suture material in periodontal surgery and regenerative surgery [27,28,29].
In our study, PTFE showed the same physical resistance as silk suture; all the sutures were found on the surgical site after 7 days at the stitch removal stage and no stitch loss was reported. All the stitches were exposed to the same chewing muscle activity in the same patient. The “stitch loss” in third molar surgery represents an unfavorable point for the surgeon, a sign of high chewing activity, the sign of improper suture technique or the sign of an uncooperative patient. Commonly, in third molar suture studies, these data are lacking. Regarding the quantitative study on microbiological retention, PTFE showed statistically lower values than silk sutures in both aerobic and anaerobic samples after M3M extraction.
These data may appear to agree with previous studies regarding PTFE suture. In an in vitro study, Charbit et al. compared silk and PTFE, reporting the superiority of PTFE [30]. However, the main limitation of Charbit’s study was that it was an in vitro study, without the variability of the patient; our study, instead, evaluated sutures after M3M removal on ten consecutive patients. In a recent paper, Mahesh et al. studied bacterial adhesion on silk and PTFE after guided bone regeneration surgery, reporting good results for PTFE and silk, compared with vicryl and polyamide sutures; however, guided bone regeneration is rarely executed in the third molar site [31]. Further, in guided bone regeneration, the flap passivation technique is commonly executed, while this procedure is uncommon in M3M surgery. Flap passivation in guided bone regeneration is a technique that allows for complete coverage of the bone graft; this aspect cannot be compared with the common secondary or primary closure in M3M surgery [32].
Few papers in the literature have compared silk and PTFE. Scarano et al. reported an interesting study of sutures with silk versus PTFE in sinus lift surgery, reporting reduced bacteria colonization on PTFE and suggesting that the multifilament structure of silk favors a greater bacterial adherence, proliferation and persistence, so monofilament and e-PTFE suture should be preferred in oral surgery [33].
The main difference between the present study and the research of *Scarano is* the examined operation. While sinus lift is an operation with first intention suture and usually without local infection, the operation of extraction of M3Ms is commonly related to a contaminated surgical field, with bacteria from the pericoronitis surrounding the tooth and an increased stagnation of saliva due to the position in the oral cavity [33]. Regardless of the differences in operation, the results are similar and suggest better results with PTFE in oral surgery.
Pons-Vicente et al., in their study, compared silk- and teflon-coated polyester sutures (similar to PTFE) in implant surgery and reported a more pronounced plaque accumulation for silk sutures, without a statistical difference [34]; further, Pons-Vicente et al. reported that silk was reported as less comfortable for the patient than teflon-coated polyester sutures [34]. Compared to our study, this research reports results from a “clean” surgery, as well as Scarano et al., compared to the M3M extraction reported in our study. On the basis of our literature review, this is the first article that reports examinations of this suture with regard to M3M surgery with a quantitative bacterial growth evaluation. This lack of PTFE studies after M3M surgery may be due to an excessive trust on this suture or due to a higher cost, compared to the silk. The cost of suture is a parameter to take into account in oral surgery. Waite and Cherala, in their study, reported the outcome of 366 impacted third molar patients treated with a sutureless technique, opening the research to the sutureless technique in M3M surgery. However, cost-guided surgery did not show the expected relevant outcomes [35]. The use of a single filament suture was recently supported by the literature for prolonged recovery time [27,36]. However, the possibility to reduce plaque retention on a suture after M3M surgery represents a matter of interest in order to reduce postoperative complications. In our article, we reported positive outcomes for the PTFE suture after partially impacted mandibular third molar extraction. We conducted a study on bacterial growth on sutures with aerobic and anaerobic inoculation. The main limits of our study are the small number of patients, but with a complete follow-up of all patients, and the placement of the two stitches on the same side. In this study the split mouth was not performed but both the sutures were applied on the same surgical site; this reduces possible variables among two sides that the patient may have (e.g., tooth brushing, absence of teeth and a favorite chewing side). Further, we used the same order of the suture placement in all patients; a different order of the sutures among the patients could influence the results. If the stitch distal to the first stitch could be more exposed in the oral cavity and not “protected” by the second molar, the stitch near the second molar could be more influenced by the plaque arising on the distal surface of the tooth, but the results reported less bacteria on the PTFE stitch near the tooth. In the end, BHI is a nonselective agar for bacteria and the results could have been influenced by this aspect, albeit our results are overlapping the results of the previous research on PTFE sutures in oral surgery. For the limits we reported and the pilot nature of this study, we advocate further studies on this matter in order to evaluate the advantages of PTFE and comparison with nylon suture in mandibular third molar surgery. The superiority of PTFE over silk in terms of bacterial retention may be due to the structure of multifilament silk, which presents a structure with a hospitable niche for bacterial growth and proliferation. The microorganisms present in the multifilament are resistant to immune response, antimicrobial therapy and may produce a biofilm that increases microbial persistence.
## 5. Conclusions
We found the PTFE suture to be superior to the silk suture in the retention of bacterial biofilm in both aerobic and anaerobic evaluations after partially impacted mandibular third molar extractions; for this reason, on the basis of our pilot study, we suggest that the first choice of suture should be PTFE in third molar surgery.
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|
---
title: Sedeveria pink ruby Extract-Mediated Synthesis of Gold and Silver Nanoparticles
and Their Bioactivity against Livestock Pathogens and in Different Cell Lines
authors:
- Palaniselvam Kuppusamy
- Sujung Kim
- Sung-Jo Kim
- Myunghum Park
- Ki-Duk Song
journal: Antibiotics
year: 2023
pmcid: PMC10044096
doi: 10.3390/antibiotics12030507
license: CC BY 4.0
---
# Sedeveria pink ruby Extract-Mediated Synthesis of Gold and Silver Nanoparticles and Their Bioactivity against Livestock Pathogens and in Different Cell Lines
## Abstract
Biological synthesis of metal nanoparticles has a significant impact in developing sustainable technologies for human, animal, and environmental safety. In this study, we synthesized gold and silver nanoparticles (NPs) using Sedeveria pink ruby (SP) extract and characterized them using UV–visible spectrophotometry, FESEM-EDX, HR-TEM, XRD, and FT-IR spectroscopy. Furthermore, antimicrobial and antioxidant activities and cytotoxicity of the synthesized NPs were evaluated. UV–visible absorption spectra showed λmax at 531 and 410 nm, corresponding to the presence of SP gold NPs (SP-AuNPs) and SP silver NPs (SP-AgNPs). Most NPs were spherical and a few were triangular rods, measuring 5–30 and 10–40 nm, respectively. EDX elemental composition analysis revealed that SP-AuNPs and SP-AgNPs accounted for >$60\%$ and $30\%$ of NPs, respectively. Additionally, some organic moieties were present, likely derived from various metabolites in the natural plant extract, which acted as stabilizing and reducing agents. Next, the antimicrobial activity of the NPs against pathogenic microbes was tested. SP-AgNPs showed potent antibacterial activity against *Escherichia coli* and Yersinia pseudotuberculosis. Moreover, at moderate and low concentrations, both NPs exhibited weak cytotoxicity in chicken fibroblasts (DF-1) and macrophages (HD11) as well as human intestinal cancer cells (HT-29). Meanwhile, at high concentrations, the NPs exhibited strong cytotoxicity in both chicken and human cell lines. Therefore, the synthesized SP-AuNPs and SP-AgNPs may act as promising materials to treat poultry diseases.
## 1. Introduction
Nanotechnology is advanced science with broad applicability in different areas, such as chemistry, physics, mechatronics, biotechnology, microbiology, and environmental sciences [1,2]. Nanoparticles (NPs) possess attractive qualities useful for the development of nano-biosensors, probes, therapeutic agents, vaccine adjuvants, drug delivery vehicles, and toxin removal systems [3,4]. NPs can be synthesized via conventional physical and chemical methods, albeit with certain limitations, primarily the use of toxic reagents in synthesis steps to prevent aggregation and the requirement of severe reaction conditions. In addition, the conventional methods of NP synthesis show side effects, poor solubility, undesirable activity in therapeutic interventions, and environmental hazards. Moreover, the chemical procedures may produce certain unwanted by-products and/or unreacted substances, which remain in the synthesized colloidal medium, rendering it unsuitable for medical applications [5,6]. Therefore, green chemistry approaches have been developed to synthesize stable monodispersed NPs with low toxicity. Green chemistry-based synthesis approaches use a wide range of biological resources, such as plant extracts, bacteria, fungi, microalgae, and seaweeds, to obtain nanosized materials [7,8]. These materials are low-cost, eco-friendly, sustainable, and suitable for scaled-up production [9,10,11]. Nonetheless, some drawbacks of green NP production must be acknowledged; the most obvious shortcoming is that these procedures do not guarantee materials of a consistent shape and size. The size and morphology of nanomaterials significantly impact their applications [12,13,14]. NPs are classified into two major classes, namely [1] inorganic NPs, such as metal NPs (Au, Ag, Cu, and Al), magnetic NPs (Co, Fe, and Ni), and semiconductor NPs (ZnO, ZnS, and CdS), and [2] organic NPs, such as carbon-based materials, quantum dots, carbon nanotubes, and silicon oxide nanotubes. Gold NPs (AuNPs) possess many valuable functional properties, such as optical and electrochemical characteristics, and show a high affinity for molecules of various molecular weights, such as RNA, DNA, and proteins. The optical properties of AuNPs, expressed as specific λmax values, have prompted their use in various biomedical and clinical applications, such as biosensors, gene therapy, drug delivery, bioinstrumentation, and bio-imaging techniques [15,16]. Likewise, silver NPs (AgNPs) are noble materials primarily composed of silver oxide [17]. AgNPs present electrical and catalytic properties, large surface area, and chemical reactivity, allowing them to bind with various ligands. AgNPs have been widely used to fabricate an array of commercial products, including surgical instruments, food storage containers, textile coatings, surgical masks, filters, toothpaste, tissue scaffold materials, and other medical accessories [18,19].
Furthermore, plant bioactive metabolites can be employed to synthesize NPs and control agglomeration during NP synthesis [20,21,22,23]. However, biologically derived NPs have certain limitations, such as the lack of standardization synthesis procedures, fluctuating concentrations of phytochemicals in plant extracts, effects of geography and climate on plant sources, difficulties in predicting the major compounds emerging from synthesis steps, and challenges in identifying the binding of targeted phytochemicals on the surface of NPs. However, synthesis procedures can be optimized by modulating the synthesis reaction parameters, such as pH, temperature range, metal salt concentration, and reaction time. Such optimized reaction conditions may improve the properties of biosynthesized NPs and promote the formation of NPs with a desired size and shape [24,25,26]. Recently, Ferocactus echidne was shown to rapidly reduce silver ions into nano-silver without the use of any external chemical agent; moreover, the cactus extract-stimulated NPs were active against Gram-positive and Gram-negative bacteria and fungi (e.g., Candida albicans), indicating the potential use of the synthesized NPs as potent broad-spectrum antibiotic and antifungal agents [27]. In addition, these NPs may be used as novel antimicrobial agents and food additives to improve the health of poultry and other livestock animals.
Sedeveria pink ruby is an ornamental succulent plant (Figure 1a). It is a cross species of Sedum and Scheveria succulents. S. pink ruby is rosette-shaped with thick padded leaves. It can grow in conditions of low water and high temperature. However, the bioactivities of S. pink ruby and its bioactive constituents have not been reported in the literature. To the best of our knowledge, the present study is the first to report the biological synthesis of AuNPs and AgNPs using the aqueous extract of S. pink ruby and study their biological properties. Briefly, we synthesized AuNPs and AgNPs using S. pink ruby aqueous extract (SP-AuNPs and SP-AgNPs, respectively) and characterized them using spectroscopic and electron microscopic techniques. Furthermore, we examined the antimicrobial effects of the synthesized SP-AuNPs and SP-AgNPs against livestock pathogens, their antioxidant activity, and their cell cytotoxicity using various chicken and human cell lines.
## 2.1. UV–Visible (UV–Vis) Spectrophotometry of SP-AuNPs and SP-AgNPs
AuNPs and AgNPs were biofabricated from metal salt precursors HAuCl4 and AgNO3, respectively, using S. pink ruby aqueous extract. NP formation was initially confirmed based on color changes in the reaction solution after mixture, as the color gradually intensified. The presence of SP-mediated AuNPs was apparent as there was an immediate change to pale purple after the addition of plant extract. Similarly, upon the addition of plant extract to silver nitrate solution, the color changed to yellowish-brown within 20 min due to the reduction of silver ions to AgNPs (Figure 1b). Both NP mixtures were subjected to UV–Vis spectrophotometry at specific time intervals (30 min for 24 h). Figure 1c shows the absorption spectra of the synthesized AuNPs and AgNPs, with λmax at 531 and 410 nm, respectively.
Furthermore, metal NP production was optimized, with enhanced yield and reduced production cost, via green synthesis. In the present study, we used three major key factors, namely pH, plant extract ratio, and metal ion concentration, to regulate and optimize NP synthesis. As shown in Figure S1a–f, an increase in pH affected the reduction time and color of the reaction mixture, reflecting changes in the size and shape of NPs. Moreover, precipitation increased at lower pH values in both SP-AuNPs and SP-AgNPs (Figure S1a,d). Furthermore, a higher metal ion concentration in the synthesis medium resulted in a broader UV–Vis absorption peak, slower reduction rate, and gradual appearance of color in the colloidal medium. However, the addition of plant extracts rapidly converted the metal ions (Au3+ and Ag+) into AuNPs and AgNPs. However, a 1:2 ratio of plant extracts to metal ions reduced metal ions more efficiently than a higher or lower ratio. Of note, the UV–*Vis spectra* became sharper, and the optical density increased slightly, indicating that the NPs showed a distinct size and shape. The appearance of a pale pink, brownish color in the nanosolution suggests the reduction of metal ions and formation of NPs and reflects the related surface plasmon resonance of AuNPs and AgNPs in the UV–Vis range. The peaks of SP-AuNPs and SP-AgNPs were within the previously reported wavelength ranges for metal NPs synthesized from with various plant extracts [28,29,30]. Similarly, in a previous study using Clerodendrum inerme (CI) leaf extract, the absorption maximum was detected at 534 nm for Cl-AuNPs and at 412 nm for Cl-AgNPs. In addition, the CI leaf extract exhibited a strong absorption band at 380 nm, suggesting the contributions of polyphenolics and flavonoids to NP synthesis [31]. For optimum synthesis, the core plant extract and water dilution ratio of 1:1 was economic, and the peak for Cl-AuNPs biosynthesized from the CI extract overlapped with that for AuNPs produced via biological approaches. For, AgNP synthesis, plant extract without dilution was optimal. The optimized gold and silver salt concentration was 2–5 mM; at lower or higher salt concentrations than this range, NP yield decreased and peaks broadened; in other words, particle stability decreased, leading to agglomeration [32,33]. pH plays a crucial role in NP synthesis, as the stability of the synthesis medium can change due to alteration of the nature of bioactive compounds present in it. Changes in pH may reduce the capping ability of molecules and prevent the subsequent growth of NPs [34].
## 2.2. FESEM-EDX Characterization of SP-AuNPs and SP-AgNPs
Figure 2a,b show the results of an FESEM-EDX analysis of the size and morphology of SP-AuNPs and SP-AgNPs. SP-AuNPs existed in monodispersed spherical forms, although some irregular particles were observed in the size range of 5–30 nm. Similarly, SP-AgNPs were present in monodispersed spherical forms ranging in size from 10 to 40 nm. These results are consistent with previous reports [35] of spherical NPs synthesized from *Catharanthus roseus* leaf extract ranging in size from 35 to 50 nm.
The energy-dispersive X-ray spectroscopy (EDX) spectra of SP-AuNPs and SP-AgNPs are shown in Figure 2a(x),b(x). Strong elemental signals were detected at 3 and 2.4 keV, corresponding to the presence of gold and silver metals, respectively. Weaker signals corresponding to sodium, carbon, and oxygen were identified, which may be derived from certain biomolecules in aqueous SP extract. Furthermore, we explored the elemental mapping of SP-AuNPs, which consist primarily of gold, carbon, sodium, copper and oxygen (Figure 2a(iv–ix)). A large proportion of gold (~$20\%$) was identified, with strong characteristic spots on the selected area of NPs. Lower proportions of carbon, hydrogen, copper, and oxygen were confirmed at respectively ~$15\%$, ~$12\%$, ~$10\%$, and ~$7\%$ in the samples. The appearance of elements such as carbon, copper, and oxygen may partly be attributed to the use of plant extract and a carbon conductive film for microscopic sample preparation. In SP-AgNPs, silver metal accounted for $70\%$, while carbon ($25\%$), oxygen ($20\%$), and copper ($5\%$) were detected at smaller proportions (Figure 2b(iv–ix)). Nevertheless, the proportion of gold was less than that of silver, which may be due to the yield of metal NPs obtained from the SP extract, and the total contents of carbon, oxygen, and copper varied between the synthesized AuNPs and AgNPs.
Consistent with literature reports [36], we observed that the average size of SP-AuNPs and SP-AgNPs was 5–30 nm and 10–40 nm, respectively. In a previous study, AuNPs obtained through a simple plant-mediated approach were highly monodispersed and spherical, with a size of 4 nm [37]. However, the shape and monodispersity of the NPs depend on the downstream preparation of synthesized NPs and biomolecule interactions with metal ions in the redox reaction [38]. C. roseus extract-mediated AuNPs and AgNPs showed elemental compositions of approximately 41 and 43 wt%, respectively [39]. In addition, EDAX spectra exhibited strong absorption signals for Au (at 1.5 keV) and Ag (at 3 keV); the reported peaks for C. roseus extract-mediated AuNPs and AgNPs are similar to our observations.
## 2.3. Transmission Electron Microscopy (TEM) of SP-AuNPs and SP-AgNPs
As shown in Figure 3a(i–iii), most SP-AuNPs were spherical and only a few were rod-shaped or triangular; the average size was 15–30 nm. Additionally, as shown in Figure 3b(i–iii), SP-AgNPs were spherical, with a uniform size range of 20–30 nm. SAED analysis showed that the synthesized SP-AuNPs and SP-AgNPs presented distinct ring patterns, indicating the presence of crystalline particles in the produced source (Figure 3a(iv),b(iv)). In addition, particle size distribution is presented as a histogram bar chart; SP-AuNPs and SP-AgNPs measured 27–31 nm on average and presented monodispersity, with adequate particle size distribution comparable to that of previously reported plant-derived NPs [40].
In a previous study, Qu et al. [ 41] described the morphology of plant-derived NPs; the NPs were uniformly dispersed in a colloidal solution and spherical, ranging in size from 5 to 9 nm. Meanwhile, in another study [41], Limnophilia rugose-derived metallic NPs (LR-MNPs) were nearly spherical and linked together to form clusters; a thin layer of biomolecules produced on the surface of the synthesized nanomaterials likely promoted cluster formation [42].
## 2.4. AFM Characterization of SP-AuNPs and SP-AgNPs
SP-AuNPs and SP-AgNPs were subjected to AFM analysis, which provides 2D and 3D profiles of NP size, height, and phases. Figure 4a,b present the size and morphology of the synthesized AuNPs and AgNPs. Most of the produced SP-AuNPs and SP-AgNPs were spherical and only a few were of irregular shapes, forming little agglomerations in the samples. The molecular sizes of SP-AuNPs and SP-AgNPs were 15 and 40 nm, respectively. AFM data were highly correlated with TEM findings. Synthesized SS-AuNPs and SS-AgNPs (Stereospermum suaveolens (SS)) measured 12–18 and 20–40 nm, respectively, and showed a large surface area. Our AFM and TEM data were consistent with previous AFM findings [43].
## 2.5. FT-IR Spectroscopic Analysis of SP-AuNPs and SP-AgNPs
Figure 5i illustrates the FT-IR spectra of SP-AuNPs, SP-AgNPs, and SP extract. The FT-IR spectrum of the SP extract revealed a broad peak around 3392 cm−1, representing the stretching vibration of OH (hydroxyl), and a sharp narrow peak at 1650 cm−1, representing the vibration of C=C (carboxylic acids). The peaks at 2938 and 1611 cm−1 corresponded to C–H stretching (amide) and carbonyl derivatives, respectively. Peaks at 1436 and 1239 cm−1 corresponded to moderate stretching of the methyl (CH3) group. Meanwhile, bands at 1184 and 1049 cm−1 were related to the stretching vibration of the C–O group in ester and alcohol molecules, respectively. The absorption peak at 1239 cm−1 corresponded to the carbonyl functional groups (C=O) in the ester linkages of fatty acids and triacylglycerols in the SP extract. The sharp peak at 1049 cm−1 corresponded to the strong stretching vibration of C–O in alcohols, ethers, and esters. In the spectrum of the SP extract, regular peaks corresponding to O–H, N–H, and C–O derived from proteins, phenols, flavonoids, sugars, and tannins were noted, with slight deviations from peaks in spectra of SP-AuNPs and SP-AgNPs. The identified functional groups may be derived from bioactive metabolites, which are responsible for the reduction of metal salts to NPs and stability of the nanomaterial.
The FT-IR spectra of the SP extract and the biosynthesized SP-AuNPs and SP-AgNPs revealed significant similarities in terms of the characteristic peaks and intensities. However, in NP spectra, the peaks of certain organic moieties overlapped, and the intensity was higher than that in the SP extract spectrum. Furthermore, the broad band at 3270 cm−1 represented the stretching vibration of hydroxyl (-OH) groups in phenolic compounds, carboxylic acids, and terpenoids of the plant extract. Furthermore, the high-intensity peak at 1602 cm−1 was attributed to the C=C and C=O stretching vibration of aromatic and amino acid carbonyl groups [37]. The SP extract is rich in phenolic groups, which allows Au3+ and Ag+ to nucleate with Au0 and Ag0, respectively. In addition to phenolics, the SP extract contains an array of metabolites that work together to form nuclei and zerovalent Au and Ag NPs of optimum size and shape [44,45]. In a previous study, spectra of C-AuNPs and C-AgNPs derived from the *Cannabis sativa* extract showed intense bands at 3340, 3269, and 3280 cm−1 corresponding to O–H stretching; at 2899 and 2918 cm−1 corresponding to C–H stretching; at 1421 and 1415 cm−1 corresponding to CH3 and CH2 asymmetric deformation; and at 1019 and 1014 cm−1 corresponding to C–O stretching [33]. The synthesized SP-AuNPs exhibited strong signals corresponding to primary amines and carbonyl stretching in the amide linkages derived from amino acid residues and proteins, which are essential compounds for NP formation and agglomeration [45]. Sedum group succulent plants contain certain major metabolite groups, such as flavonoids, tannins, terpenoids, carbohydrates, and amino acids. In addition, SP extract has been used as an antiseptic, antibacterial, and diuretic in traditional medicine, indicating the functions of this plant in folk medicine [46].
## 2.6. Powder XRD Characterization of SP-AuNPs and SP-AgNPs
The XRD patterns of SP-AuNPs and SP-AgNPs are shown in Figure 5i. Four diffraction peaks at the 2θ values of 38.4°, 44.7°, 64.7°, and 77.3° were assigned to [111], [200], [220], and [311] planes, respectively, of the face-centered cubic (fcc) structure of metallic SP-AuNPs. These findings were compared with the Joint Committee on Powder Diffraction Standards (JCPDS) pattern file (JCPDS No. 04-0784) for the Au sample. Similarly, the peaks at the 2θ values of 38.1°, 44.4°, 64.5°, and 77.6° were assigned to [111], [200], [220], and [311] planes, respectively, of SP-AgNPs. The patterns were compared to the JCPDS file [04-0783] for the Ag sample. The fcc crystalline structure of SP-AgNPs was used to index all patterns. The intensity of [111], [220], and [311] planes, in particular, was relatively high, indicating that the predominant growth of the assembled AuNPs and AgNPs in the sample was low. Furthermore, the presence of unassigned peaks on the XRD graphs of gold and silver samples (33.45 and 56.78) indicates the crystalline growth of bio-organic compounds on the surface of the synthesized metallic NPs.
The fcc structures of the synthesized SP-AuNPs and SP-AgNPs with assigned standard planes were compared with literature reports. For instance, the X-ray diffractograms of AuNPs exhibited four intense peaks at 38.2°, 44.4°, 64.6°, and 77.6°, which were assigned to [111], [200], [220], and [311] planes, respectively. Meanwhile, AgNPs showed peaks at 38.2°, 44.3°, and 77.7°, which were assigned to [111], [200], and [311] planes [47]. These results indicate the fcc structures of the synthesized AuNPs and AgNPs. In a previous study [48], Au NPs showed four peaks at the 2θ values of 38.09°, 44.40°, 65.56°, and 77.60°, which were assigned to [111], [200], [220], and [310] lattice planes, respectively. For AgNPs, the XRD peaks at 38.19°, 44.34°, 64.48°, and 77.49° were assigned to [111], [200], [220], and [311] lattice planes, respectively; these values were identical those reported for standard silver metal.
## 2.7. Antibacterial Activity of SP-AuNPs and SP-AgNPs
The antibacterial activity of the SP extract, SP-AuNPs, and SP-AgNPs was evaluated using the agar disc diffusion method, and the results are shown in Figure 6. The agar disc diffusion assay demonstrated the SP-AuNPs and SP-AgNPs evidently inhibited the growth of the selected livestock pathogenic bacteria, such as *Escherichia coli* (Gram-negative), *Salmonella derby* (Gram-negative), *Salmonella enteritidis* (Gram-negative), *Salmonella typhi* (Gram-negative), *Yersinia enterocolitica* (Gram-negative), *Yersinia pseudotuberculosis* (Gram-negative), and *Clostridium difficile* (Gram-positive). Specifically, the SP-AgNPs showed excellent antibacterial activity against E. coli, S. derby, S. enteritidis, S. typhi, Y. enterocolitica, Y. pseudotuberculosis, and C. difficile. Among these, SP-AgNPs showed the largest antibacterial zone of inhibition (ZOI) against Y. pseudotuberculosis (14 ± 3 mm) and moderate antibacterial activity against other strains, such as S. derby, S. enteritidis, S. typhi, E. coli, and Y. enterocolitica (ZOI > 9 ± 3 mm). Furthermore, SP-AuNPs showed the largest ZOI against Y. pseudotuberculosis (20 ± 3 mm); moderate activity against S. typhi (ZOI = 11 ± 3 mm), followed by S. derby (ZOI = 11 ± 3 mm) and E. coli (ZOI = 11 ± 3 mm); weak antibacterial activity against Y. enterocolitica (ZOI < 9 mm); and no activity against S. enteritidis (Table 1). The bactericidal activity of SP-AuNPs and SP-AgNPs against livestock pathogens was much stronger than that of the SP extract alone, supporting previously published data on the bioactivity of plant extracts and metal NPs [49,50,51]. Furthermore, increasing drug (plant extract) concentration may effectively control resistant pathogenic strains, which did not respond to the concentration of 20 µL. Furthermore, the antibacterial activity of the synthesized NPs was realized through their attachment to the bacterial cell wall, which depends on the surface area, size, and shapes of NPs. Additionally, the available active metabolites on the surface of NPs for interaction with pathogens may be another mechanism for the antibacterial activity of NPs. Of note, the SP-AuNPs and SP-AgNPs were highly effective against the tested Gram-negative and Gram-positive bacteria.
Furthermore, at 20 µL volume (stock concentration = 5 mg·mL−1), AgNPs exhibited potent antibacterial activity against Gram-negative bacteria (S. typhi, S. enteritidis, S. derby, E. coli, Y. enterocolitica, and Y. pseudotuberculosis), with the largest ZOI against Y. pseudotuberculosis (14 mm). Meanwhile, at 20 µL concentration, AuNPs showed moderate antibacterial activity against Gram-positive and Gram-negative pathogenic bacteria (S. derby, E. coli, Y. pseudotuberculosis, and C. difficile). Overall, the antibacterial activity of AuNPs and AgNPs depends on the size, morphology, shape, and concentration of NPs and type of pathogens. As such, smaller NP size and unique shapes are more effective in inhibiting the bacteria because of increased interaction with bacterial cell membrane [52]. In a previous study, *Aloe vera* extract did not show inhibitory activity against bacterial strains, such as E. coli, Pseudomonas aeruginosa, and Staphylococcus aureus. In the present study, the synthesized AgNPs were more effective at higher concentrations (10 µg·mL−1) than at lower concentrations (5 µg·mL−1). The synthesized NPs were showed the maximum ZOIs against *Escherichia coli* (7 mm), followed by *Staphylococcus aureus* (4 mm) and *Pseudomonas aeruginosa* (4 mm). Metal silver NPs act as broad-spectrum antimicrobial agents against various human and animal pathogens. Specifically, Aegle marmelos-synthesized NPs showed antimicrobial activity against clinical pathogenic bacteria, such as E. coli, S. aureus, and P. aeruginosa [53,54]. In addition, plant extract-biosynthesized AgNPs showed moderate activity against S. aureus, while AgNPs alone showed slightly lower antibacterial activity against E. coli and S. typhi [39]. The key mechanism underlying the antibacterial activity of NPs is the electrostatic interactions between the metallic NPs and the negative charges on microbial cell wall, resulting the denaturation of proteins, increase in cell wall permeability, leakage of cell wall components, production of reactive oxygen species, and blockade of transportation channels toward cells death [55,56,57,58]. The minimum inhibitory concentrations (MICs) of SP-AuNPs, SP-AgNPs, and standard antibiotics against livestock pathogenic microorganisms were explored. The MIC of ampicillin ranged between 0.019 and 0.347 µg·mL−1 (Table S1). Y. pseudotuberculosis and C. difficile showed the highest susceptibility to AuNPs at MICs of 0.076 and 0.05 µg·mL−1, respectively. Similarly, the selected bacterial strains showed the highest susceptibility to AgNPs at the MIC of 0.102 µg·mL−1. Meanwhile, S. enteritidis and C. glabrata showed the highest resistance to AuNPs at higher concentration (0.250 µg·mL−1). Additionally, Y. pseudotuberculosis showed the highest susceptibility to AgNPs at the MIC of 0.50 µg·mL−1. The observed MICs for SP-AuNPs and SP-AgNPs were comparable to those for standard antibiotics, such as ampicillin, which is used to treat pathogenic microbial infections [59]. These findings confirm that the synthesized SP-AuNPs and SP-AgNPs possess antimicrobial potency against livestock pathogenic bacterial and fungal strains.
## 2.8. Antifungal Activity of SP-AuNPs and SP-AgNPs
Figure 6 shows the antifungal activity of SP-AuNPs and SP-AgNPs against Candida species. SP-AuNPs (20 µL, stock concentration = 5 mg·mL−1) showed moderate inhibitory activity against Candida albicans (9 ± 3 mm) and *Candida tropicalis* (9 ± 3 mm) but no activity against Candida glabrata. Interestingly, SP-AuNPs inhibited the growth of C. albicans and C. tropicalis. SP-AgNPs showed no effect against the three Candida strains. Moreover, the ZOIs of standard drugs against the three Candida species were larger than those of SP-AuNPs and SP-AgNPs.
Surprisingly, SP-AuNPs showed moderate antifungal activity against C. albicans and C. tropicalis. The larger surface area and smaller size of the synthesized SP-AuNPs may allow them to easily interact with the fungal cell membrane. Meanwhile, SP-AgNPs (20 µL, stock concentration = 5 mg·mL−1) did not show antifungal activity against the tested Candida strains. The fungicidal activities of SP-AuNPs and SP-AgNPs may be attributed to their physical interaction with the pathogen or induction of oxidative damage by NPs to the pathogens. AgNP treatment produced a specific response against C. albicans and *Saccharomyces cerevisiae* [10]. Moreover, Ag2O/AgNPs have been reported to be effective control agents against the growth of the three Candida species tested. In addition, the gold and silver NPs possess a higher affinity to protein, implying that the NPs tend to bind the membrane proteins of pathogenic cells [31]. According to the hard–soft acid–base theory, AuNPs and AgNPs showed a higher affinity to bind with the phosphorus and sulfur moieties of the cell membrane proteins of pathogenic organisms and inhibited cell replication.
## 2.9. Antioxidant Activity of SP-AuNPs and SP-AgNPs
Table 1 shows the DPPH free radical scavenging activity of the SP extract, SP-AuNPs, and SP-AgNPs. The DPPH free radical scavenging activity increased as the concentration of SP-AuNPs and SP-AgNPs increased. The free radical scavenging activity of SP-AuNPs and SP-AgNPs was $41\%$ and $43\%$, respectively, at the stock concentration of 5 mg·mL−1. However, it was much lower than that of the ascorbic acid standard. The weak antioxidant activity of SP-AuNPs and SP-AgNPs, however, is consistent with earlier reports. For instance, *Plantago lanceolata* aqueous extract and AgNPs achieved peak antioxidant activity of $62\%$ and $17\%$ at the concentration of 100 µg·mL−1, respectively, indicating higher potential than the standard ascorbic acid ($69\%$ at 100 µg·mL−1) [60]. Moreover, *Mimosa tenuiflora* plant extract showed $50\%$ activity, similar to other plant extracts. However, the antioxidant activity of standard drugs, such as vitamin C and catechin ($46\%$ and $60\%$, respectively), is higher than that of natural compounds [61].
## 2.10. Cytotoxicity of SP-AuNPs and SP-AgNPs
At concentrations ranging from 10−4 to 10−9 (1 µg·mL−1–10 fg·mL−1), SP-AuNPs and SP-AgNPs exhibited low cytotoxicity against the cell lines used in the present study; however, at higher concentrations (10−1 to 10−3–1 mg·mL−1–10 µg·mL−1), excellent cytotoxicity was noted in both chicken and human cancer cell lines. Cell viability exceeded $85\%$ when the two chicken cell lines and the human cancer cell line were cultured with 10−4 to 10−9 (1 µg·mL−1–10 fg·mL−1) concentrations of SP-AuNPs and SP-AgNPs. However, cell viability dropped below $30\%$ after 24 h of incubation with higher concentrations (10−1 to 10−3–1 mg·mL−1–10 µg·mL−1) of SP-AuNPs and SP-AgNPs (Figure 7). Microscopic image analysis revealed the morphology of chicken and human cancer cells treated with SP-AuNPs and SP-AgNPs after 24 h (Figure S2).
At higher concentrations (10−1 (1 mg·mL−1) to 10−3 (10 µg·mL−1) dilution), both SP-AuNPs and SP-AgNPs did not affect the cell structure but slightly reduced percent cell viability. The maximum concentration of NPs that produced a significant impact on cell viability and morphology was determined (Figure S2). These results are consistent with previous reports. In a previous study, at 100 µg·mL−1, D-AuNPs did not affect cell viability after 48 h of treatment [62]. Meanwhile, AgNPs exhibited weak cytotoxicity in A549 cells at same concentration (100 µg·mL−1) [63]. In the present study, the cytotoxic effects of SP-AuNPs and SP-AgNPs on chicken and human cancer cells were weak. However, SP-AgNPs showed significant cytotoxicity (<$50\%$ cell viability) at the concentrations of 100 and 200 µg·mL−1. In particular, silver NPs showed stronger cytotoxicity against chicken macrophages than gold NPs at the same concentration [5,64]. Furthermore, D-AgNPs (Dendropanax morbifera) significantly inhibited cancer cell growth (>$70\%$) after 48 h of exposure to higher concentrations of 100 µg·mL−1 [65]. The cytotoxicity of plant-derived NPs may involve the synergic effects of plant compounds. However, the cytotoxicity of these NPs may increase with increasing concentration. Another possible factor contributing to the cytotoxicity of these NPs is their tendency to aggregate in protein or aqueous media [66,67]. The aggregates of nanomaterial, which may adhere to the cell membranes and block cell function or even result in apoptotic cell death, may be the source of toxicity of metallic NPs [68].
## 3. Conclusions
Here, we synthesized AuNPs and AgNPs from the aqueous extract of S. pink ruby using green chemistry technology. The synthesized SP-AuNPs and SP-AgNPs ranged in size from 5 to 30 nm and from 10 to 45 nm, with average diameters of 25 and 50 nm, respectively. The metal composition of the NPs was determined based on EDX spectra, which indicated $25\%$ Au and $67\%$ Ag in the sample. In addition, other organic moieties, possibly derived from adherent biomolecules in the plant extract, were detected. FT-IR spectra revealed hydroxyl groups, amines, aldehydes, ketones, flavonols, and other phytochemicals responsible for the rapid reduction of metal ions present in the SP extract. Next, the antibacterial properties of SP-AuNPs and SP-AgNPs were examined; the NPs showed potent inhibitory activity against E. coli, S. typhi, S. derby, and Y. pseudotuberculosis but weak activity against Candida species (ZOI of <9 mm for AuNPs). Nonetheless, SP-AuNPs and SP-AgNPs may inhibit livestock pathogenic Gram-negative bacteria. Furthermore, we tested the cytotoxicity of SP-AuNPs and SP-AgNPs against avian-based cell lines, such as chicken fibroblasts and macrophages. The NPs showed no cytotoxicity at lower concentration, although higher concentrations significantly reduced cell viability in both cell lines. Therefore, the synthesized SP-AuNPs and SP-AgNPs may serve as potent antibacterial agents to control pathogenic infections in poultry and livestock animals. Furthermore, these NPs may serve as novel sources of antimicrobial agents to treat livestock infections, adjuvants for vaccine development, and biomedical applications in the near future.
## 4.1. Chemicals
Gold (III) chloride trihydrate (HAuCl4·3H2O, Cat. No. 520918), silver nitrate (AgNO3, Cat. No. 209139), NaOH, and HCl were purchased from Sigma Aldrich (Republic of Korea). All chemical reagents were of HPLC grade. Cell culture media and supplements were obtained from Thermo Fisher Scientific Korea (Seoul, Republic of Korea). The Center for Industrialization of Agricultural and Livestock Microorganism (CILAM, Jeongeup, Republic of Korea) provided pathogenic bacterial and fungal cultures.
## 4.2. Plant Collection and Preparation of Aqueous Extract
S. pink ruby was obtained from a local market in Republic of Korea and maintained at the Department of Biotechnology, Hoseo University, Republic of Korea. Fresh leaves (50 g, cut into 1–2 cm pieces) were mixed with 150 mL of sterile distilled water. The aqueous plant extract was prepared in an autoclave for 10 min at 110 °C under 39.23 kPa pressure. The extract was passed through a cellulose acetate filter (0.22 µm pore size; Sartorius, Göttingen, Germany) under sterile conditions and stored at 4 °C.
## 4.3. Biosynthesis of AuNPs and AgNPs
AuNPs and AgNPs were biosynthesized following a previously described method [69] with minor modifications. AuNPs were synthesized using 5 mL of aqueous SP extract and 45 mL of HAuCl4 (0.001 M) solution at 30 °C ± 2 without light exposure. The reaction mixture immediately turned pink, indicating the formation of NPs in the synthesis medium. The reaction mixture was then transferred to a hot plate at 37 °C for 30 min to complete the growth of AuNPs in a medium. AgNPs were synthesized from 5 mL of SP aqueous extract and 45 mL of AgNO3 (0.001 M) solution without light exposure. The mixture gradually turned from colorless to yellowish-brown over 15 min, indicating the formation of AgNPs in the colloidal solution. The appropriate reaction procedure was repeated two times. The biosynthesized NPs were then centrifuged at 10,000× g for 15 min, and the pellet was freeze dried overnight. Powdered NP samples were stored at 4 °C for further experiments. The biosynthesis procedure of AuNPs and AgNPs was optimized using various reaction parameters, such as pH (4, 5, 6, 7, and 8), plant extract ratio (1:2, 1:4, 1:6, 1:8, and 1:10), and metal salt concentration (10−2, 10−3, 10−4, 10−5), to achieve the maximum yield and quality. Further, the NP quality was preliminarily checked by assessing the appearance of color in the synthesis medium, and the UV–Vis absorbance spectra of the NP colloidal solution were analyzed.
## 4.4. Characterization of SP-AuNPs and SP-AgNPs
The UV–Vis absorption spectra of SP-AuNPs and SP-AgNPs were obtained using a UV–Vis spectrophotometer (UV 1800, Japan; 200–800 scan range and 1 nm resolution).
The size, structure, and elemental ratio/mapping of the NPs were determined using FESEM, EDX, and elemental mapping using an electron microscope (JEM 2100 F; JEOL, MA, Japan) operated at 200 kV. NP powder samples were sputter coated on a carbon film in a sample holder at 90 °C for 90 s. Then, the structure, morphology, and SAED patterns of the synthesized NPs were studied using a transmission electron microscope (Zeiss TEM Microscope, Carl Zeiss, Oberkochen, Germany). TEM specimens were prepared using carbon-coated copper grids. The prepared aqueous NP samples were deposited drop-wise onto the TEM and sample grids, which were dried at 80 °C for 10 min. Further, the grids were used for particle visualization using a transmission electron microscope. ImageJ was used to calculate the diameter of the NPs and their particle size distribution within the samples [70].
The crystalline nature of the freeze-dried AuNPs and AgNPs was evaluated using an X-ray spectrophotometer (D8 Advance, Bruker Corp., Billerica MA, USA) at room temperature with nickel-filtered Cu-Kα radiation at 1.54 Å operated at 40 kV voltage and 30 mA current. The XRD pattern ranges were recorded at 2θ from 5° to 90° with a scan rate of 1·min−1 and a slit width of 6.0 mm [71].
The functional groups of the synthesized AuNPs and AgNPs were characterized by FT-IR spectroscopy (PerkinElmer Inc., Waltham, MA, USA) using the KBr method in the matrix scan range of 4000–500 cm−1 and at the resolution of 4 cm−1 [72].
## 4.5. Antibacterial Activity of SP-AuNPs and SP-AgNPs
The antibacterial activity of the SP extract, SP-AuNPs, and SP-AgNPs was measured against livestock pathogenic microbes, including *Clostridium difficile* (JCM 1296), *Escherichia coli* (KCTC2617), *Salmonella derby* (NCCP 12238), *Salmonella enteritidis* (NCCP 14546), *Salmonella typhimurium* (NCCP 10438), *Yersinia enterocolitica* (NCCP 11129), and *Yersinia pseudotuberculosis* (NCCP 11125), using the disc diffusion method. For the assay, rich Luria broth agar, Sabouraud dextrose (SD) agar, and brain heart infusion agar plates were prepared. Next, 100 µL of bacterial suspension (~1.2 × 108 CFU·mL−1) was spread on the solid agar plates. SP-AuNP, SP-AgNP, and SP extract samples were prepared and dissolved (20 µL each, stock concentration = 5 mg·mL−1) in a sterile 6 mm blank disc. On each disc, the NP solution was spotted and allowed to dry before the disc was placed on the agar plate and incubated at 37 °C for 24 h. Commercial antibiotics (ampicillin) were used as the positive control (20 µg). After incubation, the diameter of the ZOI around each disc was measured using a measuring scale. All experiments were performed in triplicate [73].
## 4.6. Antifungal Activity of SP-AuNPs and SP-AgNPs
The antifungal activity of the biosynthesized SP-AuNPs and SP-AgNPs as well as of the SP extract was tested against three pathogenic fungal strains: Candida albicans (NCCP 31077), *Candida glabrata* (NCCP 30939), and *Candida tropicalis* (NCCP 30262). The selected fungal pathogens were grown in PDA broth at 28 °C for 24 h using the disc diffusion technique. Fresh fungal cultures (100 µL each, i.e., ~2 × 105 CFU·mL−1) were evenly spread in a sterile PD agar Petri plate. SP-AuNPs, SP-AgNPs, and SP plant extract (20 µL each, stock concentration = 5 mg·mL−1) were separately added to a 6 mm blank disc and incubated for 3 h to allow the complete adsorption of the samples. The discs were then dried and placed aseptically on fungal-inoculated Petri plates. The plates were incubated at 25 °C for 3 days. The antifungal activity of each sample was measured based on the diameter of the ZOI around each disc using a measuring scale.
## 4.7. MIC Study
The lowest concentration of AuNPs/AgNPs that completely inhibited the bacterial/fungal growth in the medium was considered the MIC. A loopful of pathogenic bacterial/fungal cultures was inoculated in individual wells containing MHA medium and treated with different concentrations of NPs (20, 10, 5, 2.5, and 1.25 µL samples; stock concentration = 5 mg·mL−1). The sample-treated wells showing no visual bacterial growth (at the lowest concentration) after 8–12 h of incubation were further spotted onto MHA agar medium and incubated at 37 °C for 24 h.
## 4.8. Antioxidant Activity of SP-AuNPs and SP-AgNPs
The antioxidant activity of SP-AuNPs and SP-AgNPs was analyzed using the 2,2-diphenyl1-picrylhydrazyl (DPPH) free radical scavenging assay, as described in a previous study [74] with minor modifications. Briefly, 10 mL of 0.2 mM DPPH stock solution was prepared in methanol. Then, 100 µL aliquots of aqueous AuNPs/AgNPs (10−1, 10−2, 10−3, 10−4, and 10−5 dilutions, stock concentration = 5 mg·mL−1), plant extract, and standard solution (10–100 µg·mL−1) were mixed with 100 µL of methanolic DPPH solution. DPPH with methanolic solution alone was used as the control. All solutions were incubated in the dark at room temperature for 30 min. The DPPH antioxidant activity of each sample was determined based on optical density (OD) measured at 517 nm. Percent DPPH scavenging activity was calculated using the following equation:DPPH free radical scavenging activity (%) = ([ODcontrol − ODsample])/[(ODcontrol)] × 100 where ODsample is the OD value of AuNPs, AgNPs, and plant extract/standard, and ODcontrol is the OD value of DPPH methanolic solution (control).
## 4.9. Cytotoxicity of SP-AuNPs and SP-AgNPs
The cytotoxicity of SP-AuNPs and SP-AgNPs was determined using the WST-1 cell proliferation and viability assay. Chicken (DF-1 cells: chicken embryonic fibroblast cells, HD11 cells: chicken macrophage cells) and human (HT-29: human colon cancer cells) cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin–streptomycin (Gibco, Gaithersburg, MD, USA). The cells were maintained at 37 °C in a humidified atmosphere with $5\%$ CO2. The cells were seeded in a 96-well plate at the density of 1 × 105 cells·well−1 and incubated for 24 h to reach $90\%$ confluence. After reaching confluence, the cells were treated with different concentrations of SP-AuNPs and SP-AgNPs (10−1, 10−2, 10−3, 10−4, and 10−5 dilutions, stock concentration = 10 mg·mL−1) for 24 h. Then, 10 µL of WST-1 solution was added to each well, and the plates were incubated for an additional hour [75]. Then, the OD of each plate was measured at 490 nm using a microplate reader (Thermo Fisher Scientific Solutions Co., Ltd., Waltham, MA, USA). Percent cell viability was calculated using the following equation:Cell viability (%) = (OD490sample − OD490blank)/(OD490control − OD490blank) × 100
## 4.10. Statistical Analysis
All experiments were performed in triplicate. Data are presented as mean ± standard error (SE). One-way analysis of variance (ANOVA) was used to determine significant differences in means among the samples, followed by Duncan’s multiple comparison test (SPSS 20.0, IBM, Armonk, NY, USA). $p \leq 0.05$ was considered significant; in figures and tables, significance is indicated with subscript lowercase letters (a, b, c, d, and so on).
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